{ "schemaVersion": "cybersim-cae-crosswalk-3.0", "authority": { "frameworkVersion": "2026", "verifiedAt": "2026-10-04", "knowledgeUnits": { "url": "https://www.caecommunity.org/_files/ugd/e7fed7_904a106b5897427eb6698e1dd5ecc7d2.pdf", "sha256": "4cd537bcafe33cbeffe031bc41748a9ca6aa5f0887cb9b1ecb433829025969e5" }, "requirements": { "url": "https://www.caecommunity.org/_files/ugd/e7fed7_4b504c88ab464971bb9edd6e195648d6.pdf", "sha256": "2c5cbc57c835f768f1611e371820b30fb7929c18c87fbcd2bb6d291bd7d5d1ff" } }, "generatedFrom": "CAE CyberAI (CAE-CAI) Knowledge Units, Version 2026", "framework": { "version": "2026", "title": "NCAE-C CAE CyberAI Knowledge Units", "sourceUrl": "https://www.caecommunity.org/cop-cyber-ai", "sourceDocument": "CAE CyberAI (CAE-CAI) Knowledge Units, Version 2026", "accessedAt": "2026-10-04", "knowledgeUnits": { "CSF": { "id": "CSF", "title": "Cybersecurity Fundamentals", "outcomes": [ { "id": "CSF.1", "statement": "Define the fundamental principles of cybersecurity." }, { "id": "CSF.2", "statement": "Describe the goals of cybersecurity." }, { "id": "CSF.3", "statement": "Describe the potential objectives, tactics, and motivations of cyber attackers." }, { "id": "CSF.4", "statement": "Describe various types of cyber-attacks." }, { "id": "CSF.5", "statement": "Describe various common vulnerability types and evaluate their severity." }, { "id": "CSF.6", "statement": "Describe different types of malware." }, { "id": "CSF.7", "statement": "Describe common mechanisms used to protect against cyber-attacks." }, { "id": "CSF.8", "statement": "Given a specific scenario, identify potential protection mechanisms and design principles." }, { "id": "CSF.9", "statement": "Describe the components of access control and how they are implemented." }, { "id": "CSF.10", "statement": "Discuss current events in cybersecurity." } ] }, "ISC": { "id": "ISC", "title": "IT Systems Components", "outcomes": [ { "id": "ISC.1", "statement": "Describe the various hardware components of modern computing environments and their individual functions." }, { "id": "ISC.2", "statement": "Describe the types, functions, and characteristics of networks that interconnect computing systems and components." }, { "id": "ISC.3", "statement": "Describe the various IT activities and roles involved in managing computing systems." }, { "id": "ISC.4", "statement": "Understand system and software architectures." }, { "id": "ISC.5", "statement": "Describe how software is managed within an organization." }, { "id": "ISC.6", "statement": "Understand basic security issues in modern computing environments." } ] }, "BSP": { "id": "BSP", "title": "Basic Scripting and Programming", "outcomes": [ { "id": "BSP.1", "statement": "Write simple scripts to automate system administration tasks." }, { "id": "BSP.2", "statement": "Write programs in a high-level language using basic programming constructs and concepts." }, { "id": "BSP.3", "statement": "Implement basic security practices in scripting, including bounds checking and input validation." }, { "id": "BSP.4", "statement": "Demonstrate conceptual understanding and practical use of regular expressions." }, { "id": "BSP.5", "statement": "Demonstrate the use of basic data structures and algorithms." } ] }, "MAF": { "id": "MAF", "title": "Math Fundamentals", "outcomes": [ { "id": "MAF.1", "statement": "Apply basic algebraic operations and solve linear equations, inequalities, and systems of equations." }, { "id": "MAF.2", "statement": "Analyze and graph various types of functions, including linear, quadratic, polynomial, rational, exponential, and logarithmic functions." }, { "id": "MAF.3", "statement": "Perform operations with functions, including composition and finding inverses." }, { "id": "MAF.4", "statement": "Solve quadratic, higher-degree polynomial, exponential, and logarithmic equations." }, { "id": "MAF.5", "statement": "Use algebraic models to represent and solve real-world problems, including those involving sequences and series." }, { "id": "MAF.6", "statement": "Understand and differentiate between descriptive and inferential statistics, including the use of statistical measures and distributions (e.g., solve classification problems)." }, { "id": "MAF.7", "statement": "Organize and summarize data using tables, charts, and graphs, and interpret measures of central tendency and variability." }, { "id": "MAF.8", "statement": "Understand and apply fundamental probability principles." }, { "id": "MAF.9", "statement": "Understand iterative numerical techniques for approximating solutions, including notions of convergence and approximation error." } ] }, "AIG": { "id": "AIG", "title": "AI Governance, Laws, and Ethics", "outcomes": [ { "id": "AIG.1", "statement": "Analyze AI Governance related to the US and International legal and regulatory frameworks, organizational policies, and oversight mechanisms." }, { "id": "AIG.2", "statement": "Describe the societal responsibility of AI developers, operators, and users to act legally and ethically at all times." }, { "id": "AIG.3", "statement": "Describe AI ethical considerations (Fairness(bias)), Transparency, Accountability, and Non-discrimination)." }, { "id": "AIG.4", "statement": "Assess the impact of laws and regulations on AI development and security." } ] }, "AIF": { "id": "AIF", "title": "AI Fundamentals", "outcomes": [ { "id": "AIF.1", "statement": "Describe the principles and concepts of AI, including terminology, components, tools, various AI technologies, and their evolution." }, { "id": "AIF.2", "statement": "Explain classical artificial intelligence algorithms, such as search and optimization algorithms." }, { "id": "AIF.3", "statement": "Explore and assess how AI technologies can transform cybersecurity practices, including enhancing security protocols and predicting security risks (e.g., threats and vulnerabilities)." }, { "id": "AIF.4", "statement": "Evaluate the suitability of an AI tool for a specific task, particularly within the context of cybersecurity." }, { "id": "AIF.5", "statement": "Discuss applications of Artificial Intelligence techniques in cybersecurity and related domains." }, { "id": "AIF.6", "statement": "Describe the ethical considerations and potential risks associated with AI, focusing on bias, privacy, safety, and security throughout the lifecycle." }, { "id": "AIF.7", "statement": "Identify solutions to business problems by utilizing intelligent systems and machine learning." } ] }, "MLF": { "id": "MLF", "title": "Machine Learning Fundamentals", "outcomes": [ { "id": "MLF.1", "statement": "Define machine learning (ML) and discuss frequently used ML methods." }, { "id": "MLF.2", "statement": "Describe various ML algorithms." }, { "id": "MLF.3", "statement": "Compare and contrast various ML methods." }, { "id": "MLF.4", "statement": "Identify the classes of problems for which ML is best suited." }, { "id": "MLF.5", "statement": "Evaluate the performance and effectiveness of different ML methods and tools." }, { "id": "MLF.6", "statement": "Identify various data types and formats used by AI systems." }, { "id": "MLF.7", "statement": "Apply ML methods effectively for cybersecurity applications." }, { "id": "MLF.8", "statement": "Analyze security case studies and threats using ML techniques." } ] }, "COF": { "id": "COF", "title": "Computer Science Fundamentals", "outcomes": [ { "id": "COF.1", "statement": "Design logic circuits that perform basic computational tasks." }, { "id": "COF.2", "statement": "Encode and decode data to and from binary format." }, { "id": "COF.3", "statement": "Define fundamental data structures and their appropriate uses." }, { "id": "COF.4", "statement": "Apply the principles of abstraction and modularity in the design of software." }, { "id": "COF.5", "statement": "Characterize the complexity of algorithms." }, { "id": "COF.6", "statement": "Identify when heuristics are needed for finding practical solutions to algorithmic problems." } ] }, "BNW": { "id": "BNW", "title": "Basic Networking", "outcomes": [ { "id": "BNW.1", "statement": "Describe and explain the fundamental concepts, technologies, components and issues related to communications and data networks." }, { "id": "BNW.2", "statement": "Apply networking concepts to design a basic network architecture given a specific need and set of hosts/clients." }, { "id": "BNW.3", "statement": "Apply and demonstrate knowledge of networking concepts by tracking and identifying the data packets involved in a simple TCP connection (or perform a trace of such a connection)." }, { "id": "BNW.4", "statement": "Apply and demonstrate use of a network monitoring tool to observe the flow of packets (e.g., Wireshark)." }, { "id": "BNW.5", "statement": "Perform and examine results of network mapping (i.e., enumeration and identification of network components) (e.g., Nmap)." }, { "id": "BNW.6", "statement": "Describe common network vulnerabilities." } ] }, "NDF": { "id": "NDF", "title": "Network Defense", "outcomes": [ { "id": "NDF.1", "statement": "Describe and discuss the key concepts in network defense (defense in depth, minimizing exposure, etc.)." }, { "id": "NDF.2", "statement": "Explain and discuss how network defense tools (e.g., firewalls, IDS, etc.) are used to defend against attacks and mitigate vulnerabilities." }, { "id": "NDF.3", "statement": "Analyze and evaluate how security policies are implemented on systems to protect a network." }, { "id": "NDF.4", "statement": "Evaluate how network operational procedures relate to network security." } ] }, "AVM": { "id": "AVM", "title": "Advanced Math for AI", "outcomes": [ { "id": "AVM.1", "statement": "Demonstrate understanding of vectors, matrices, correlation, and regression of data." }, { "id": "AVM.2", "statement": "Demonstrate the application of partial derivatives (e.g., the chain rule) to AI applications." }, { "id": "AVM.3", "statement": "Apply mathematical principles to analyze, develop, and evaluate algorithms and models using statistical techniques." }, { "id": "AVM.4", "statement": "Analyze mathematical problems (e.g., graphs, tree search problems, training neural networks, etc.) and determine appropriate techniques for an applied context." }, { "id": "AVM.5", "statement": "Construct and implement AI algorithms (e.g., linear equations, graphs, tree search problems, etc.) using fundamental mathematical principles." }, { "id": "AVM.6", "statement": "Demonstrate the use of hypothesis testing." } ] }, "MLA": { "id": "MLA", "title": "Machine Learning Algorithms", "outcomes": [ { "id": "MLA.1", "statement": "Compare and contrast various ML algorithms." }, { "id": "MLA.2", "statement": "Identify and implement the appropriate algorithm for specific problems based on the data provided." }, { "id": "MLA.3", "statement": "Determine the optimal learning paradigm(s) for specific use cases." }, { "id": "MLA.4", "statement": "Implement and assess the performance of various ML algorithms as applied to a specific problem." }, { "id": "MLA.5", "statement": "Tune algorithm parameters to improve accuracy and robustness." }, { "id": "MLA.6", "statement": "Implement ensemble algorithms by combining different techniques to solve problems." } ] }, "DPL": { "id": "DPL", "title": "Deep Learning", "outcomes": [ { "id": "DPL.1", "statement": "Implement a multilayer artificial neural network." }, { "id": "DPL.2", "statement": "Model sequential data using recurrent neural networks." }, { "id": "DPL.3", "statement": "Explain the importance and concepts of activation functions, loss functions, and bias in deep neural networks." }, { "id": "DPL.4", "statement": "Implement common techniques for generating synthetic data." }, { "id": "DPL.5", "statement": "Describe the use-case of reinforcement learning for model training." }, { "id": "DPL.6", "statement": "Validate the performance of a trained neural network." }, { "id": "DPL.7", "statement": "Evaluate the explainability of a trained neural network using XAI methods." }, { "id": "DPL.8", "statement": "Identify the best learning paradigm(s) for a specific use case." } ] }, "MES": { "id": "MES", "title": "Model Selection, Evaluation, and Specification", "outcomes": [ { "id": "MES.1", "statement": "Identify the requirements of a task to guide model selection." }, { "id": "MES.2", "statement": "Apply criteria for model selection based on problem requirements." }, { "id": "MES.3", "statement": "Analyze the appropriateness and performance of different AI/ML models for a specific task using various metrics." }, { "id": "MES.4", "statement": "Evaluate the impact of different inputs, prompts, and hyperparameter combinations on model performance and implement techniques to optimize ML models." }, { "id": "MES.5", "statement": "Evaluate the trade-offs between conflicting goals and requirements in the selection and evaluation of a model." }, { "id": "MES.6", "statement": "Evaluate the impact of data quality and bias on model performance." }, { "id": "MES.7", "statement": "Evaluate the ethical concerns in data acquisition, model selection, and model implementation." }, { "id": "MES.8", "statement": "Justify the choice of evaluation methods based on the model." } ] }, "AIL": { "id": "AIL", "title": "Securing the AI Lifecycle", "outcomes": [ { "id": "AIL.1", "statement": "Assess threats and prioritize risks at different stages of the AI lifecycle." }, { "id": "AIL.2", "statement": "Describe the security challenges and vulnerabilities at each stage of the AI lifecycle." }, { "id": "AIL.3", "statement": "Examine and analyze vulnerabilities in connections between AI components." }, { "id": "AIL.4", "statement": "Apply best practices for maintaining the confidentiality, integrity, and availability of AI-enabled systems." }, { "id": "AIL.5", "statement": "Design, implement, and evaluate security measures for protecting AI models." }, { "id": "AIL.6", "statement": "Integrate AI models into systems and implement strategies to mitigate risk from vulnerabilities." } ] }, "AIR": { "id": "AIR", "title": "Risk Management of AI", "outcomes": [ { "id": "AIR.1", "statement": "Evaluate and select appropriate benchmarking frameworks for AI Systems." }, { "id": "AIR.2", "statement": "Produce comprehensive reports with actionable recommendations for risk mitigation." }, { "id": "AIR.3", "statement": "Conduct continuous assessment and improvement of AI systems." }, { "id": "AIR.4", "statement": "Design and develop mitigation strategies for ML and AI threats." }, { "id": "AIR.5", "statement": "Assess the impact of identified vulnerabilities on AI systems." }, { "id": "AIR.6", "statement": "Implement strategies to mitigate identified security vulnerabilities." }, { "id": "AIR.7", "statement": "Map attack surfaces and develop comprehensive threat models." } ] }, "ADL": { "id": "ADL", "title": "Adversarial Learning", "outcomes": [ { "id": "ADL.1", "statement": "Investigate how adversarial examples can be crafted to manipulate machine learning models." }, { "id": "ADL.2", "statement": "Identify and analyze attacker objectives with respect to AI targets." }, { "id": "ADL.3", "statement": "Assess the robustness of AI models against adversarial attacks." }, { "id": "ADL.4", "statement": "Apply defenses to protect AI models from adversarial attacks." }, { "id": "ADL.5", "statement": "Understand ethical and privacy issues with Adversarial Attacks." }, { "id": "ADL.6", "statement": "Model psychological aspects of insider threats using AI." } ] }, "AIS": { "id": "AIS", "title": "AI for Security Assessments", "outcomes": [ { "id": "AIS.1", "statement": "Conduct security evaluations using AI enhancements to standard tools and techniques." }, { "id": "AIS.2", "statement": "Produce comprehensive reports with actionable recommendations." }, { "id": "AIS.3", "statement": "Leverage AI to assess the impact of identified vulnerabilities and design and implement mitigation strategies." }, { "id": "AIS.4", "statement": "Perform threat modeling and develop mitigation strategies." } ] }, "DFA": { "id": "DFA", "title": "Defensive Applications of AI", "outcomes": [ { "id": "DFA.1", "statement": "Implement AI techniques to enhance security posture and automate cybersecurity processes." }, { "id": "DFA.2", "statement": "Develop and apply AI and machine learning models to identify cyber risks (e.g., vulnerabilities and threats) and their mitigation." }, { "id": "DFA.3", "statement": "Understand frameworks like MITRE ATLAS for AI-driven cyber threat intelligence." }, { "id": "DFA.4", "statement": "Understand Explainable AI-driven cybersecurity solutions." } ] }, "OFA": { "id": "OFA", "title": "Offensive Applications of AI", "outcomes": [ { "id": "OFA.1", "statement": "Identify and explain adversarial attacks on machine learning models." }, { "id": "OFA.2", "statement": "Identify opportunities for AI to enable offensive cybersecurity operations." }, { "id": "OFA.3", "statement": "Supplement current offensive Tactics, Techniques, and Procedures (TTPs) with AI." }, { "id": "OFA.4", "statement": "Evaluate the effectiveness of implementing AI for conducting cyber-attacks." }, { "id": "OFA.5", "statement": "Discuss the ethical and legal implications of AI-enabled cyber offensive operations." }, { "id": "OFA.6", "statement": "Correlate offensive AI techniques to existing cybersecurity frameworks." } ] } }, "pathways": { "secure-ai": { "id": "secure-ai", "title": "Security of AI", "shortTitle": "SecureAI", "direction": "Secure AI systems and infrastructure throughout their lifecycle.", "foundationalKuIds": [ "CSF", "ISC", "BSP", "MAF", "AIG", "AIF", "MLF" ], "coreKuIds": [ "COF", "AVM", "AIL", "MLA", "DPL", "ADL" ], "optionalKuIds": [ "MES", "AIR" ], "optionalKuCount": 1 }, "ai-cyber": { "id": "ai-cyber", "title": "AI for Cybersecurity", "shortTitle": "AICyber", "direction": "Apply AI to traditional cybersecurity assessment and defense.", 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evidence record.", "rationale": "Direct status is awarded only after every official outcome passes its deterministic check or published faculty rubric, including the transfer variant." }, { "id": "cae-practicum-mes", "pathwayIds": [ "secure-ai" ], "kuId": "MES", "outcomeIds": [ "MES.1", "MES.2", "MES.3", "MES.4", "MES.5", "MES.6", "MES.7", "MES.8" ], "constructIds": [], "practicumTaskIds": [ "cae-practicum-mes" ], "coverage": "assessed", "performanceRefs": [ "practicum:MES" ], "evidence": "A KU-native Model Selection, Evaluation, and Specification performance task, hidden transfer fixture, and outcome-level evidence record.", "rationale": "Direct status is awarded only after every official outcome passes its deterministic check or published faculty rubric, including the transfer variant." }, { "id": "cae-practicum-adl", "pathwayIds": [ "secure-ai" ], "kuId": "ADL", "outcomeIds": [ "ADL.1", "ADL.2", "ADL.3", "ADL.4", "ADL.5", "ADL.6" ], "constructIds": [], "practicumTaskIds": [ "cae-practicum-adl" ], "coverage": "assessed", "performanceRefs": [ "practicum:ADL" ], "evidence": "A KU-native Adversarial Learning performance task, hidden transfer fixture, and outcome-level evidence record.", "rationale": "Direct status is awarded only after every official outcome passes its deterministic check or published faculty rubric, including the transfer variant." }, { "id": "cae-practicum-air", "pathwayIds": [ "secure-ai" ], "kuId": "AIR", "outcomeIds": [ "AIR.1", "AIR.2", "AIR.3", "AIR.4", "AIR.5", "AIR.6", "AIR.7" ], "constructIds": [], "practicumTaskIds": [ "cae-practicum-air" ], "coverage": "assessed", "performanceRefs": [ "practicum:AIR" ], "evidence": "A KU-native Risk Management of AI performance task, hidden transfer fixture, and outcome-level evidence record.", "rationale": "Direct status is awarded only after every official outcome passes its deterministic check or published faculty rubric, including the transfer variant." }, { "id": "cae-practicum-bnw", "pathwayIds": [ "ai-cyber" ], "kuId": "BNW", "outcomeIds": [ "BNW.1", "BNW.2", "BNW.3", "BNW.4", "BNW.5", "BNW.6" ], "constructIds": [], "practicumTaskIds": [ "cae-practicum-bnw" ], "coverage": "assessed", "performanceRefs": [ "practicum:BNW" ], "evidence": "A KU-native Basic Networking performance task, hidden transfer fixture, and outcome-level evidence record.", "rationale": "Direct status is awarded only after every official outcome passes its deterministic check or published faculty rubric, including the transfer variant." }, { "id": "cae-practicum-ndf", "pathwayIds": [ "ai-cyber" ], "kuId": "NDF", "outcomeIds": [ "NDF.1", "NDF.2", "NDF.3", "NDF.4" ], "constructIds": [], "practicumTaskIds": [ "cae-practicum-ndf" ], "coverage": "assessed", "performanceRefs": [ "practicum:NDF" ], "evidence": "A KU-native Network Defense performance task, hidden transfer fixture, and outcome-level evidence record.", "rationale": "Direct status is awarded only after every official outcome passes its deterministic check or published faculty rubric, including the transfer variant." }, { "id": "cae-practicum-ais", "pathwayIds": [ "ai-cyber" ], "kuId": "AIS", "outcomeIds": [ "AIS.1", "AIS.2", "AIS.3", "AIS.4" ], "constructIds": [], "practicumTaskIds": [ "cae-practicum-ais" ], "coverage": "assessed", "performanceRefs": [ "practicum:AIS" ], "evidence": "A KU-native AI for Security Assessments performance task, hidden transfer fixture, and outcome-level evidence record.", "rationale": "Direct status is awarded only after every official outcome passes its deterministic check or published faculty rubric, including the transfer variant." }, { "id": "cae-practicum-dfa", "pathwayIds": [ "ai-cyber" ], "kuId": "DFA", "outcomeIds": [ "DFA.1", "DFA.2", "DFA.3", "DFA.4" ], "constructIds": [], "practicumTaskIds": [ "cae-practicum-dfa" ], "coverage": "assessed", "performanceRefs": [ "practicum:DFA" ], "evidence": "A KU-native Defensive Applications of AI performance task, hidden transfer fixture, and outcome-level evidence record.", "rationale": "Direct status is awarded only after every official outcome passes its deterministic check or published faculty rubric, including the transfer variant." }, { "id": "cae-practicum-ofa", "pathwayIds": [ "ai-cyber" ], "kuId": "OFA", "outcomeIds": [ "OFA.1", "OFA.2", "OFA.3", "OFA.4", "OFA.5", "OFA.6" ], "constructIds": [], "practicumTaskIds": [ "cae-practicum-ofa" ], "coverage": "assessed", "performanceRefs": [ "practicum:OFA" ], "evidence": "A KU-native Offensive Applications of AI performance task, hidden transfer fixture, and outcome-level evidence record.", "rationale": "Direct status is awarded only after every official outcome passes its deterministic check or published faculty rubric, including the transfer variant." }, { "id": "cae-csf-c1", "pathwayIds": [ "secure-ai", "ai-cyber" ], "kuId": "CSF", "outcomeIds": [ "CSF.3", "CSF.4", "CSF.5", "CSF.8" ], "constructIds": [ "C1" ], "coverage": "assessed", "performanceRefs": [ "foundation:C1" ], "evidence": "Two sealed trust-boundary cases with cited records and declared confidence.", "rationale": "The learner distinguishes attacker-controlled instructions and selects bounded protection." }, { "id": "cae-csf-c3", "pathwayIds": [ "secure-ai", "ai-cyber" ], "kuId": "CSF", "outcomeIds": [ "CSF.7", "CSF.9" ], "constructIds": [ "C3" ], "coverage": "assessed", "performanceRefs": [ "foundation:C3" ], "evidence": "Two least-privilege and release-boundary decisions.", "rationale": "The learner applies access control and release mechanisms to an unfamiliar system." }, { "id": "cae-isc-c3", "pathwayIds": [ "secure-ai", "ai-cyber" ], "kuId": "ISC", "outcomeIds": [ "ISC.4", "ISC.6" ], "constructIds": [ "C3" ], "coverage": "reinforced", "performanceRefs": [ "foundation:C3" ], "evidence": "System-boundary and tool-authority evidence.", "rationale": "CyberSim reinforces architecture and security concepts but does not assess the full IT components KU." }, { "id": "cae-aig-c4", "pathwayIds": [ "secure-ai", "ai-cyber" ], "kuId": "AIG", "outcomeIds": [ "AIG.2", "AIG.3" ], "constructIds": [ "C4" ], "coverage": "reinforced", "performanceRefs": [ "foundation:C4" ], "evidence": "Faculty-reviewed incident rationale and residual-risk statement.", "rationale": "The learner practices accountable human judgment; legal and regulatory analysis requires other coursework." }, { "id": "cae-aif-c5", "pathwayIds": [ "secure-ai", "ai-cyber" ], "kuId": "AIF", "outcomeIds": [ "AIF.3", "AIF.4", "AIF.6" ], "constructIds": [ "C5" ], "coverage": "reinforced", "performanceRefs": [ "c5:full-investigation", "c5:transfer" ], "evidence": "A defensive-agent investigation and transfer case with safety vetoes.", "rationale": "The learner evaluates a bounded AI agent in cybersecurity, but CyberSim does not cover the complete AI fundamentals KU." }, { "id": "cae-mlf-c5", "pathwayIds": [ "secure-ai", "ai-cyber" ], "kuId": "MLF", "outcomeIds": [ "MLF.5", "MLF.8" ], "constructIds": [ "C5" ], "coverage": "introduced", "performanceRefs": [ "c5:full-investigation" ], "evidence": "Agent confidence and false-positive evidence review.", "rationale": "The experience introduces evaluation behavior but does not require implementing or comparing ML methods." }, { "id": "cae-ail-c1", "pathwayIds": [ "secure-ai" ], "kuId": "AIL", "outcomeIds": [ "AIL.1", "AIL.2", "AIL.3" ], "constructIds": [ "C1" ], "coverage": "assessed", "performanceRefs": [ "foundation:C1" ], "evidence": "Trust-boundary and injection-path cases.", "rationale": "The learner locates lifecycle and component-boundary threats from records." }, { "id": "cae-ail-c2", "pathwayIds": [ "secure-ai" ], "kuId": "AIL", "outcomeIds": [ "AIL.3", "AIL.4", "AIL.5" ], "constructIds": [ "C2" ], "coverage": "assessed", "performanceRefs": [ "foundation:C2" ], "evidence": "Retrieval, provenance, validation, and output-integrity cases.", "rationale": "The learner applies and evaluates controls that preserve AI-system integrity." }, { "id": "cae-ail-c3", "pathwayIds": [ "secure-ai" ], "kuId": "AIL", "outcomeIds": [ "AIL.4", "AIL.5", "AIL.6" ], "constructIds": [ "C3" ], "coverage": "assessed", "performanceRefs": [ "foundation:C3" ], "evidence": "Least-authority, approval, and release-control cases.", "rationale": "The learner integrates AI tools while constraining data and action authority." }, { "id": "cae-adl-c1", "pathwayIds": [ "secure-ai" ], "kuId": "ADL", "outcomeIds": [ "ADL.2", "ADL.3", "ADL.4" ], "constructIds": [ "C1" ], "coverage": "assessed", "performanceRefs": [ "foundation:C1" ], "evidence": "Direct, indirect, and multimodal injection-path decisions.", "rationale": "The learner identifies attacker objectives and applies defenses in bounded adversarial cases." }, { "id": "cae-adl-c2", "pathwayIds": [ "secure-ai" ], "kuId": "ADL", "outcomeIds": [ "ADL.3", "ADL.4", "ADL.5" ], "constructIds": [ "C2" ], "coverage": "assessed", "performanceRefs": [ "foundation:C2" ], "evidence": "Poisoned retrieval and unsafe-output cases.", "rationale": "The learner evaluates adversarial evidence and selects integrity defenses." }, { "id": "cae-mes-c2", "pathwayIds": [ "secure-ai" ], "kuId": "MES", "outcomeIds": [ "MES.3", "MES.5", "MES.6", "MES.8" ], "constructIds": [ "C2" ], "coverage": "reinforced", "performanceRefs": [ "foundation:C2" ], "evidence": "Conflicting-source and integrity-control decisions.", "rationale": "CyberSim reinforces model/system evaluation tradeoffs but does not assess model implementation or hyperparameter work." }, { "id": "cae-air-c4", "pathwayIds": [ "secure-ai" ], "kuId": "AIR", "outcomeIds": [ "AIR.2", "AIR.4", "AIR.5", "AIR.6", "AIR.7" ], "constructIds": [ "C4" ], "coverage": "assessed", "performanceRefs": [ "foundation:C4" ], "evidence": "Two incident reconstructions, scoped containment plans, and dual faculty ratings.", "rationale": "The learner maps an attack surface, assesses impact, and defends an actionable mitigation." }, { "id": "cae-ndf-c4", "pathwayIds": [ "ai-cyber" ], "kuId": "NDF", "outcomeIds": [ "NDF.1", "NDF.2", "NDF.3", "NDF.4" ], "constructIds": [ "C4" ], "coverage": "assessed", "performanceRefs": [ "foundation:C4" ], "evidence": "Correlated security records, policy checks, containment, and recovery verification.", "rationale": "The incident performance directly assesses defense-in-depth and operational defense decisions." }, { "id": "cae-ais-c5", "pathwayIds": [ "ai-cyber" ], "kuId": "AIS", "outcomeIds": [ "AIS.1", "AIS.2", "AIS.3", "AIS.4" ], "constructIds": [ "C5" ], "coverage": "assessed", "performanceRefs": [ "c5:full-investigation", "c5:transfer" ], "evidence": "AI-assisted investigation, evidence validation, threat modeling, mitigation, and replayable report.", "rationale": "The learner supervises an AI-enhanced security assessment and produces an actionable evidence chain." }, { "id": "cae-dfa-c5", "pathwayIds": [ "ai-cyber" ], "kuId": "DFA", "outcomeIds": [ "DFA.1", "DFA.3", "DFA.4" ], "constructIds": [ "C5" ], "coverage": "assessed", "performanceRefs": [ "c5:full-investigation", "c5:transfer" ], "evidence": "Bounded defensive-agent use, claim validation, authority control, and false-positive handling.", "rationale": "The learner uses and evaluates defensive AI while retaining human approval and explainability." } ], "labModules": [ { "id": "lab-trust-boundaries", "title": "Lab 1: Trace and contain an AI trust-boundary failure", "suggestedCourseCode": "CYAI 310", "pathwayIds": [ "secure-ai" ], "recommendedWeek": "Weeks 4-5", "durationMinutes": 90, "kuOutcomeRefs": [ "CSF.3", "CSF.8", "AIL.1", "AIL.2", "AIL.3", "ADL.2", "ADL.4" ], "learningOutcomes": [ "Locate an untrusted instruction path across retrieval, model, and tool boundaries.", "Select a control that contains the path without discarding the legitimate task." ], "studentTasks": [ "Inspect two sealed cases from different attack families.", "Cite at least two records per decision and declare confidence.", "Defend the selected boundary and control from the evidence." ], "evidenceArtifacts": [ "Hash-chained case decisions with cited record identifiers.", "Construct-level C1 score and confidence calibration." ], "assessmentIndicators": [ "Correctly locates the failed trust boundary in both cases.", "Chooses a proportionate control and supports it with valid evidence." ] }, { "id": "lab-ai-incident", "title": "Lab 2: Reconstruct and contain an AI security incident", "suggestedCourseCode": "CYAI 420", "pathwayIds": [ "secure-ai", "ai-cyber" ], "recommendedWeek": "Weeks 8-9", "durationMinutes": 120, "kuOutcomeRefs": [ "AIR.2", "AIR.4", "AIR.5", "AIR.6", "AIR.7", "NDF.1", "NDF.3", "NDF.4" ], "learningOutcomes": [ "Correlate conflicting records into a defensible incident timeline.", "Propose scoped containment, recovery verification, and residual-risk communication." ], "studentTasks": [ "Reconstruct two unfamiliar incidents from sealed records.", "Preserve evidence while selecting the smallest effective containment.", "Submit a rationale for independent faculty review." ], "evidenceArtifacts": [ "Replayable C4 submissions and evidence roots.", "Two independent rubric ratings and adjudication outcome." ], "assessmentIndicators": [ "Uses multiple records to identify the failed boundary.", "Contains with least privilege and states residual risk without unsupported attribution." ] }, { "id": "lab-defensive-agent", "title": "Lab 3: Supervise a bounded defensive AI agent", "suggestedCourseCode": "CYAI 430", "pathwayIds": [ "ai-cyber" ], "recommendedWeek": "Weeks 11-12", "durationMinutes": 120, "kuOutcomeRefs": [ "AIF.3", "AIF.4", "AIS.1", "AIS.2", "AIS.3", "AIS.4", "DFA.1", "DFA.3", "DFA.4" ], "learningOutcomes": [ "Validate an agent's claims against cited telemetry and policy.", "Constrain tool authority, approve or reject action, and verify recovery across a transfer case." ], "studentTasks": [ "Compare competing hypotheses and challenge unsupported agent claims.", "Approve, narrow, reject, or request evidence for a proposed action.", "Execute once, verify recovery, and explain residual risk on a different threat family." ], "evidenceArtifacts": [ "Hash-chained C5 investigation and transfer record.", "Safety-veto results, authority decisions, and dual faculty ratings." ], "assessmentIndicators": [ "Rejects fabricated evidence and prohibited scope.", "Transfers evidence-grounded supervision to an unfamiliar threat family." ] } ], "practicumTasks": [ { "id": "cae-practicum-csf", "kuId": "CSF", "pathwayIds": [ "secure-ai", "ai-cyber" ], "title": "Attack-to-control range", "mission": "Investigate a compromised AI support service, classify the attack path, rank vulnerabilities, and deploy proportionate controls.", "workbench": "threat-lab", "estimatedMinutes": 70, "studentActions": [ "Build an attack path from mixed host, identity, and model records.", "Rank three vulnerabilities by impact and exploitability.", "Configure access, integrity, and recovery controls, then rerun the incident." ], "artifactRequirements": [ "Attack-path graph with cited records.", "Before-and-after control test report." ], "outcomeChecks": [ { "outcomeId": "CSF.1", "verification": "faculty-rubric", "evidencePrompt": "Identify confidentiality, integrity, availability, authenticity, and nonrepudiation in an incident record.", "passingRule": "Earn level 3 or higher under rubric cae-2026-csf-01-1.0, with signed primary and transfer artifacts." }, { "outcomeId": "CSF.2", "verification": "faculty-rubric", "evidencePrompt": "Prioritize competing cybersecurity goals for a service with explicit operational constraints.", "passingRule": "Earn level 3 or higher under rubric cae-2026-csf-02-1.0, with signed primary and transfer artifacts." }, { "outcomeId": "CSF.3", "verification": "faculty-rubric", "evidencePrompt": "Infer an attacker's objective, capability, access, and motivation from cited telemetry.", "passingRule": "Earn level 3 or higher under rubric cae-2026-csf-03-1.0, with signed primary and transfer artifacts." }, { "outcomeId": "CSF.4", "verification": "faculty-rubric", "evidencePrompt": "Classify attack techniques from host, identity, network, and application evidence.", "passingRule": "Earn level 3 or higher under rubric cae-2026-csf-04-1.0, with signed primary and transfer artifacts." }, { "outcomeId": "CSF.5", "verification": "faculty-rubric", "evidencePrompt": "Rank vulnerabilities by exploitability, impact, exposure, and affected assets.", "passingRule": "Earn level 3 or higher under rubric cae-2026-csf-05-1.0, with signed primary and transfer artifacts." }, { "outcomeId": "CSF.6", "verification": "faculty-rubric", "evidencePrompt": "Distinguish malware behavior families from process, persistence, and network indicators.", "passingRule": "Earn level 3 or higher under rubric cae-2026-csf-06-1.0, with signed primary and transfer artifacts." }, { "outcomeId": "CSF.7", "verification": "faculty-rubric", "evidencePrompt": "Match preventive, detective, corrective, and recovery mechanisms to observed attack paths.", "passingRule": "Earn level 3 or higher under rubric cae-2026-csf-07-1.0, with signed primary and transfer artifacts." }, { "outcomeId": "CSF.8", "verification": "faculty-rubric", "evidencePrompt": "Design and test layered protection for an unfamiliar system while preserving required service behavior.", "passingRule": "Earn level 3 or higher under rubric cae-2026-csf-08-1.0, with signed primary and transfer artifacts." }, { "outcomeId": "CSF.9", "verification": "faculty-rubric", "evidencePrompt": "Configure identification, authentication, authorization, and audit controls and verify enforcement.", "passingRule": "Earn level 3 or higher under rubric cae-2026-csf-09-1.0, with signed primary and transfer artifacts." }, { "outcomeId": "CSF.10", "verification": "faculty-rubric", "evidencePrompt": "Analyze a dated cybersecurity event from authoritative sources and separate confirmed facts from inference.", "passingRule": "Earn level 3 or higher under rubric cae-2026-csf-10-1.0, with signed primary and transfer artifacts." } ], "transferVariantId": "cae-practicum-csf-transfer", "scoringPolicy": { "minimumOutcomeRate": 1, "minimumFacultyScore": 3, "requiresTransfer": true } }, { "id": "cae-practicum-isc", "kuId": "ISC", "pathwayIds": [ "secure-ai", "ai-cyber" ], "title": "AI system architecture teardown", "mission": "Reconstruct the hardware, network, software, data, and operating roles behind an AI service and locate its security boundaries.", "workbench": "architecture-map", "estimatedMinutes": 75, "studentActions": [ "Place components and trust zones on the architecture canvas.", "Connect service, model, data, and administrative flows.", "Assign operational ownership and identify component-specific failure modes." ], "artifactRequirements": [ "Validated component and data-flow diagram.", "Operations-and-risk register." ], "outcomeChecks": [ { "outcomeId": "ISC.1", "verification": "faculty-rubric", "evidencePrompt": "Inventory processor, memory, storage, accelerator, and peripheral roles from a running-system profile.", "passingRule": "Earn level 3 or higher under rubric cae-2026-isc-01-1.0, with signed primary and transfer artifacts." }, { "outcomeId": "ISC.2", "verification": "faculty-rubric", "evidencePrompt": "Reconstruct LAN, WAN, wireless, cloud, and service connections from topology and flow evidence.", "passingRule": "Earn level 3 or higher under rubric cae-2026-isc-02-1.0, with signed primary and transfer artifacts." }, { "outcomeId": "ISC.3", "verification": "faculty-rubric", "evidencePrompt": "Assign operational ownership for provisioning, monitoring, patching, backup, and incident response.", "passingRule": "Earn level 3 or higher under rubric cae-2026-isc-03-1.0, with signed primary and transfer artifacts." }, { "outcomeId": "ISC.4", "verification": "faculty-rubric", "evidencePrompt": "Construct hardware, operating-system, application, data, and service architecture boundaries.", "passingRule": "Earn level 3 or higher under rubric cae-2026-isc-04-1.0, with signed primary and transfer artifacts." }, { "outcomeId": "ISC.5", "verification": "faculty-rubric", "evidencePrompt": "Evaluate software acquisition, configuration, versioning, patch, and retirement controls.", "passingRule": "Earn level 3 or higher under rubric cae-2026-isc-05-1.0, with signed primary and transfer artifacts." }, { "outcomeId": "ISC.6", "verification": "faculty-rubric", "evidencePrompt": "Locate component-specific security failures and select controls that preserve system operation.", "passingRule": "Earn level 3 or higher under rubric cae-2026-isc-06-1.0, with signed primary and transfer artifacts." } ], "transferVariantId": "cae-practicum-isc-transfer", "scoringPolicy": { "minimumOutcomeRate": 1, "minimumFacultyScore": 3, "requiresTransfer": true } }, { "id": "cae-practicum-bsp", "kuId": "BSP", "pathwayIds": [ "secure-ai", "ai-cyber" ], "title": "Secure response automation", "mission": "Write a constrained response script that parses security records, validates input, groups alerts, and produces bounded remediation actions.", "workbench": "code-lab", "estimatedMinutes": 110, "studentActions": [ "Implement the workflow in CyberSim SafeScript.", "Use regular expressions to extract indicators from noisy records.", "Run normal, malformed, boundary, and adversarial test fixtures." ], "artifactRequirements": [ "Versioned SafeScript source and abstract syntax tree.", "Passing test transcript including malformed-input cases." ], "outcomeChecks": [ { "outcomeId": "BSP.1", "verification": "deterministic", "evidencePrompt": "Write and execute a script that automates a bounded system-administration workflow.", "passingRule": "Pass the signed bsp-01 primary and transfer fixtures under rubric cae-2026-bsp-01-1.0." }, { "outcomeId": "BSP.2", "verification": "deterministic", "evidencePrompt": "Implement variables, branches, loops, functions, collections, and file or console input in an executable program.", "passingRule": "Pass the signed bsp-02 primary and transfer fixtures under rubric cae-2026-bsp-02-1.0." }, { "outcomeId": "BSP.3", "verification": "deterministic", "evidencePrompt": "Reject malformed, oversized, mistyped, and out-of-range inputs through executable checks.", "passingRule": "Pass the signed bsp-03 primary and transfer fixtures under rubric cae-2026-bsp-03-1.0." }, { "outcomeId": "BSP.4", "verification": "deterministic", "evidencePrompt": "Write and test regular expressions against valid, invalid, boundary, and adversarial records.", "passingRule": "Pass the signed bsp-04 primary and transfer fixtures under rubric cae-2026-bsp-04-1.0." }, { "outcomeId": "BSP.5", "verification": "deterministic", "evidencePrompt": "Implement and compare basic collections, search, grouping, sorting, and traversal algorithms.", "passingRule": "Pass the signed bsp-05 primary and transfer fixtures under rubric cae-2026-bsp-05-1.0." } ], "transferVariantId": "cae-practicum-bsp-transfer", "scoringPolicy": { "minimumOutcomeRate": 1, "minimumFacultyScore": null, "requiresTransfer": true } }, { "id": "cae-practicum-maf", "kuId": "MAF", "pathwayIds": [ "secure-ai", "ai-cyber" ], "title": "Risk threshold mathematics", "mission": "Model alert risk with algebra, probability, functions, summary statistics, and an iterative threshold search.", "workbench": "quantitative-lab", "estimatedMinutes": 95, "studentActions": [ "Solve the operating constraints and graph the cost functions.", "Estimate conditional risk and summarize score distributions.", "Search for a threshold, report convergence error, and defend the operating point." ], "artifactRequirements": [ "Reproducible calculation notebook.", "Threshold chart with uncertainty and error analysis." ], "outcomeChecks": [ { "outcomeId": "MAF.1", "verification": "deterministic", "evidencePrompt": "Solve and verify linear equations, inequalities, and systems arising from a cyber risk model.", "passingRule": "Pass the signed maf-01 primary and transfer fixtures under rubric cae-2026-maf-01-1.0." }, { "outcomeId": "MAF.2", "verification": "deterministic", "evidencePrompt": "Graph and compare linear, quadratic, polynomial, rational, exponential, and logarithmic functions.", "passingRule": "Pass the signed maf-02 primary and transfer fixtures under rubric cae-2026-maf-02-1.0." }, { "outcomeId": "MAF.3", "verification": "deterministic", "evidencePrompt": "Compose functions, calculate inverses, and verify domain and range restrictions.", "passingRule": "Pass the signed maf-03 primary and transfer fixtures under rubric cae-2026-maf-03-1.0." }, { "outcomeId": "MAF.4", "verification": "deterministic", "evidencePrompt": "Solve polynomial, exponential, and logarithmic equations and check numerical residuals.", "passingRule": "Pass the signed maf-04 primary and transfer fixtures under rubric cae-2026-maf-04-1.0." }, { "outcomeId": "MAF.5", "verification": "deterministic", "evidencePrompt": "Construct an algebraic model for an applied sequence, series, cost, or capacity problem.", "passingRule": "Pass the signed maf-05 primary and transfer fixtures under rubric cae-2026-maf-05-1.0." }, { "outcomeId": "MAF.6", "verification": "deterministic", "evidencePrompt": "Select descriptive or inferential methods and justify the choice for a supplied dataset.", "passingRule": "Pass the signed maf-06 primary and transfer fixtures under rubric cae-2026-maf-06-1.0." }, { "outcomeId": "MAF.7", "verification": "deterministic", "evidencePrompt": "Build tables and charts and calculate central tendency, spread, and distribution summaries.", "passingRule": "Pass the signed maf-07 primary and transfer fixtures under rubric cae-2026-maf-07-1.0." }, { "outcomeId": "MAF.8", "verification": "deterministic", "evidencePrompt": "Calculate conditional, joint, and complementary probabilities for an uncertain security event.", "passingRule": "Pass the signed maf-08 primary and transfer fixtures under rubric cae-2026-maf-08-1.0." }, { "outcomeId": "MAF.9", "verification": "deterministic", "evidencePrompt": "Run an iterative numerical method and report convergence, stopping conditions, and approximation error.", "passingRule": "Pass the signed maf-09 primary and transfer fixtures under rubric cae-2026-maf-09-1.0." } ], "transferVariantId": "cae-practicum-maf-transfer", "scoringPolicy": { "minimumOutcomeRate": 1, "minimumFacultyScore": null, "requiresTransfer": true } }, { "id": "cae-practicum-aig", "kuId": "AIG", "pathwayIds": [ "secure-ai", "ai-cyber" ], "title": "AI governance review board", "mission": "Conduct a deployment review involving privacy, fairness, security, transparency, and conflicting legal obligations.", "workbench": "governance-hearing", "estimatedMinutes": 90, "studentActions": [ "Map stakeholder duties and applicable governance obligations.", "Audit subgroup impact and challenge unsupported fairness claims.", "Present a release, restrict, or reject decision under questioning." ], "artifactRequirements": [ "Governance decision memo with cited authority.", "Recorded oral defense and faculty rubric." ], "outcomeChecks": [ { "outcomeId": "AIG.1", "verification": "faculty-rubric", "evidencePrompt": "Compare current US and international AI laws, regulations, policies, standards, and oversight for a deployment.", "passingRule": "Earn level 3 or higher under rubric cae-2026-aig-01-1.0, with signed primary and transfer artifacts." }, { "outcomeId": "AIG.2", "verification": "faculty-rubric", "evidencePrompt": "Assign legal and ethical responsibilities to developers, deployers, operators, users, and affected parties.", "passingRule": "Earn level 3 or higher under rubric cae-2026-aig-02-1.0, with signed primary and transfer artifacts." }, { "outcomeId": "AIG.3", "verification": "faculty-rubric", "evidencePrompt": "Audit fairness, transparency, accountability, privacy, and nondiscrimination claims with subgroup evidence.", "passingRule": "Earn level 3 or higher under rubric cae-2026-aig-03-1.0, with signed primary and transfer artifacts." }, { "outcomeId": "AIG.4", "verification": "faculty-rubric", "evidencePrompt": "Determine how cited laws and regulations change the design, security, release, and monitoring plan.", "passingRule": "Earn level 3 or higher under rubric cae-2026-aig-04-1.0, with signed primary and transfer artifacts." } ], "transferVariantId": "cae-practicum-aig-transfer", "scoringPolicy": { "minimumOutcomeRate": 1, "minimumFacultyScore": 3, "requiresTransfer": true } }, { "id": "cae-practicum-aif", "kuId": "AIF", "pathwayIds": [ "secure-ai", "ai-cyber" ], "title": "AI capability and safety bench", "mission": "Select and test AI approaches for a cyber problem while exposing capability limits, optimization behavior, bias, privacy, and safety risks.", "workbench": "model-lab", "estimatedMinutes": 90, "studentActions": [ "Compare search, rules, supervised learning, and generative approaches.", "Run a bounded optimization and inspect failure cases.", "Select a suitable approach and document when not to use AI." ], "artifactRequirements": [ "Approach-comparison matrix with measured results.", "Suitability and risk decision record." ], "outcomeChecks": [ { "outcomeId": "AIF.1", "verification": "faculty-rubric", "evidencePrompt": "Explain an AI system's concepts, components, tools, technologies, and historical development from its architecture.", "passingRule": "Earn level 3 or higher under rubric cae-2026-aif-01-1.0, with signed primary and transfer artifacts." }, { "outcomeId": "AIF.2", "verification": "faculty-rubric", "evidencePrompt": "Execute and compare classical search, optimization, and rule-based algorithms on a bounded problem.", "passingRule": "Earn level 3 or higher under rubric cae-2026-aif-02-1.0, with signed primary and transfer artifacts." }, { "outcomeId": "AIF.3", "verification": "faculty-rubric", "evidencePrompt": "Measure how an AI method changes cyber detection, prioritization, or risk prediction performance.", "passingRule": "Earn level 3 or higher under rubric cae-2026-aif-03-1.0, with signed primary and transfer artifacts." }, { "outcomeId": "AIF.4", "verification": "faculty-rubric", "evidencePrompt": "Select or reject an AI tool for a cyber task using measured suitability, risk, and operating constraints.", "passingRule": "Earn level 3 or higher under rubric cae-2026-aif-04-1.0, with signed primary and transfer artifacts." }, { "outcomeId": "AIF.5", "verification": "faculty-rubric", "evidencePrompt": "Compare AI applications across security and a transfer domain without conflating their evidence needs.", "passingRule": "Earn level 3 or higher under rubric cae-2026-aif-05-1.0, with signed primary and transfer artifacts." }, { "outcomeId": "AIF.6", "verification": "faculty-rubric", "evidencePrompt": "Find bias, privacy, safety, security, and ethical failure modes across an AI lifecycle.", "passingRule": "Earn level 3 or higher under rubric cae-2026-aif-06-1.0, with signed primary and transfer artifacts." }, { "outcomeId": "AIF.7", "verification": "faculty-rubric", "evidencePrompt": "Formulate a business problem and choose an intelligent-system approach with a defensible non-AI baseline.", "passingRule": "Earn level 3 or higher under rubric cae-2026-aif-07-1.0, with signed primary and transfer artifacts." } ], "transferVariantId": "cae-practicum-aif-transfer", "scoringPolicy": { "minimumOutcomeRate": 1, "minimumFacultyScore": 3, "requiresTransfer": true } }, { "id": "cae-practicum-mlf", "kuId": "MLF", "pathwayIds": [ "secure-ai", "ai-cyber" ], "title": "Anomaly model field test", "mission": "Prepare cyber data, train multiple baseline methods, compare their behavior, and use the best justified method on an unfamiliar threat set.", "workbench": "model-lab", "estimatedMinutes": 105, "studentActions": [ "Inspect labels, feature types, missing values, and class imbalance.", "Train and compare three supplied learning methods.", "Interpret errors and transfer the selected method to a shifted dataset." ], "artifactRequirements": [ "Dataset card and model-comparison report.", "Transfer confusion matrix with error analysis." ], "outcomeChecks": [ { "outcomeId": "MLF.1", "verification": "faculty-rubric", "evidencePrompt": "Define machine learning and distinguish supervised, unsupervised, semi-supervised, and reinforcement methods.", "passingRule": "Earn level 3 or higher under rubric cae-2026-mlf-01-1.0, with signed primary and transfer artifacts." }, { "outcomeId": "MLF.2", "verification": "faculty-rubric", "evidencePrompt": "Explain and execute representative classification, regression, clustering, and anomaly-detection algorithms.", "passingRule": "Earn level 3 or higher under rubric cae-2026-mlf-02-1.0, with signed primary and transfer artifacts." }, { "outcomeId": "MLF.3", "verification": "faculty-rubric", "evidencePrompt": "Compare learning methods on common data, metrics, compute cost, and error patterns.", "passingRule": "Earn level 3 or higher under rubric cae-2026-mlf-03-1.0, with signed primary and transfer artifacts." }, { "outcomeId": "MLF.4", "verification": "faculty-rubric", "evidencePrompt": "Determine whether supplied problems are suitable for machine learning and state a credible baseline.", "passingRule": "Earn level 3 or higher under rubric cae-2026-mlf-04-1.0, with signed primary and transfer artifacts." }, { "outcomeId": "MLF.5", "verification": "faculty-rubric", "evidencePrompt": "Evaluate trained methods with held-out data, cross-validation, confusion matrices, and error analysis.", "passingRule": "Earn level 3 or higher under rubric cae-2026-mlf-05-1.0, with signed primary and transfer artifacts." }, { "outcomeId": "MLF.6", "verification": "faculty-rubric", "evidencePrompt": "Inspect and transform tabular, text, categorical, temporal, and binary data formats.", "passingRule": "Earn level 3 or higher under rubric cae-2026-mlf-06-1.0, with signed primary and transfer artifacts." }, { "outcomeId": "MLF.7", "verification": "faculty-rubric", "evidencePrompt": "Train and evaluate a learning method on a cybersecurity dataset.", "passingRule": "Earn level 3 or higher under rubric cae-2026-mlf-07-1.0, with signed primary and transfer artifacts." }, { "outcomeId": "MLF.8", "verification": "faculty-rubric", "evidencePrompt": "Analyze an unfamiliar security case with a trained method and explain failures under distribution shift.", "passingRule": "Earn level 3 or higher under rubric cae-2026-mlf-08-1.0, with signed primary and transfer artifacts." } ], "transferVariantId": "cae-practicum-mlf-transfer", "scoringPolicy": { "minimumOutcomeRate": 1, "minimumFacultyScore": 3, "requiresTransfer": true } }, { "id": "cae-practicum-cof", "kuId": "COF", "pathwayIds": [ "secure-ai" ], "title": "Secure inference rule engine", "mission": "Build a small inference guard from digital logic through data structures, modular code, and complexity analysis.", "workbench": "code-lab", "estimatedMinutes": 110, "studentActions": [ "Assemble and test the Boolean release circuit.", "Encode records and choose data structures for lookup and ordering.", "Implement a modular heuristic and measure its growth on larger inputs." ], "artifactRequirements": [ "Passing circuit and binary-encoding tests.", "Modular SafeScript with measured complexity trace." ], "outcomeChecks": [ { "outcomeId": "COF.1", "verification": "deterministic", "evidencePrompt": "Design and truth-table a logic circuit that performs a stated computational task.", "passingRule": "Pass the signed cof-01 primary and transfer fixtures under rubric cae-2026-cof-01-1.0." }, { "outcomeId": "COF.2", "verification": "deterministic", "evidencePrompt": "Encode and decode signed integers, text, and structured fields in binary representations.", "passingRule": "Pass the signed cof-02 primary and transfer fixtures under rubric cae-2026-cof-02-1.0." }, { "outcomeId": "COF.3", "verification": "deterministic", "evidencePrompt": "Select and operate arrays, lists, stacks, queues, maps, sets, trees, or graphs for stated access patterns.", "passingRule": "Pass the signed cof-03 primary and transfer fixtures under rubric cae-2026-cof-03-1.0." }, { "outcomeId": "COF.4", "verification": "deterministic", "evidencePrompt": "Refactor a program into explicit abstractions and modules and verify interfaces with tests.", "passingRule": "Pass the signed cof-04 primary and transfer fixtures under rubric cae-2026-cof-04-1.0." }, { "outcomeId": "COF.5", "verification": "deterministic", "evidencePrompt": "Measure operation counts and characterize best, average, and worst-case algorithm growth.", "passingRule": "Pass the signed cof-05 primary and transfer fixtures under rubric cae-2026-cof-05-1.0." }, { "outcomeId": "COF.6", "verification": "deterministic", "evidencePrompt": "Identify an intractable search and implement and evaluate a bounded heuristic.", "passingRule": "Pass the signed cof-06 primary and transfer fixtures under rubric cae-2026-cof-06-1.0." } ], "transferVariantId": "cae-practicum-cof-transfer", "scoringPolicy": { "minimumOutcomeRate": 1, "minimumFacultyScore": null, "requiresTransfer": true } }, { "id": "cae-practicum-avm", "kuId": "AVM", "pathwayIds": [ "secure-ai" ], "title": "Robustness mathematics laboratory", "mission": "Use vectors, matrices, derivatives, regression, statistics, and hypothesis testing to evaluate a model under attack.", "workbench": "quantitative-lab", "estimatedMinutes": 120, "studentActions": [ "Transform the feature matrix and calculate similarity and correlation.", "Compute a gradient step and fit a regression baseline.", "Test the robustness claim and select the appropriate mathematical method." ], "artifactRequirements": [ "Reproducible matrix and gradient notebook.", "Statistical robustness report with test decision." ], "outcomeChecks": [ { "outcomeId": "AVM.1", "verification": "deterministic", "evidencePrompt": "Perform vector and matrix operations and calculate correlation and regression on model data.", "passingRule": "Pass the signed avm-01 primary and transfer fixtures under rubric cae-2026-avm-01-1.0." }, { "outcomeId": "AVM.2", "verification": "deterministic", "evidencePrompt": "Calculate partial derivatives and apply the chain rule to a model-loss function.", "passingRule": "Pass the signed avm-02 primary and transfer fixtures under rubric cae-2026-avm-02-1.0." }, { "outcomeId": "AVM.3", "verification": "deterministic", "evidencePrompt": "Develop and evaluate an algorithm using statistical and mathematical principles.", "passingRule": "Pass the signed avm-03 primary and transfer fixtures under rubric cae-2026-avm-03-1.0." }, { "outcomeId": "AVM.4", "verification": "deterministic", "evidencePrompt": "Select appropriate graph, search, optimization, or neural-training mathematics for an applied problem.", "passingRule": "Pass the signed avm-04 primary and transfer fixtures under rubric cae-2026-avm-04-1.0." }, { "outcomeId": "AVM.5", "verification": "deterministic", "evidencePrompt": "Construct and execute an AI algorithm from equations, graphs, or search principles.", "passingRule": "Pass the signed avm-05 primary and transfer fixtures under rubric cae-2026-avm-05-1.0." }, { "outcomeId": "AVM.6", "verification": "deterministic", "evidencePrompt": "Form a hypothesis, select a test, check assumptions, calculate significance, and interpret the decision.", "passingRule": "Pass the signed avm-06 primary and transfer fixtures under rubric cae-2026-avm-06-1.0." } ], "transferVariantId": "cae-practicum-avm-transfer", "scoringPolicy": { "minimumOutcomeRate": 1, "minimumFacultyScore": null, "requiresTransfer": true } }, { "id": "cae-practicum-ail", "kuId": "AIL", "pathwayIds": [ "secure-ai" ], "title": "Secure AI lifecycle control room", "mission": "Threat-model an AI system from data acquisition through deployment and monitoring, then validate controls at every component boundary.", "workbench": "architecture-map", "estimatedMinutes": 105, "studentActions": [ "Map threats and prioritize risks at each lifecycle phase.", "Apply confidentiality, integrity, availability, provenance, and model controls.", "Integrate the model into a service and run abuse, rollback, and recovery checks." ], "artifactRequirements": [ "Lifecycle threat model with ranked controls.", "Integration security test and recovery transcript." ], "outcomeChecks": [ { "outcomeId": "AIL.1", "verification": "faculty-rubric", "evidencePrompt": "Assess and prioritize threats at acquisition, training, deployment, monitoring, and retirement stages.", "passingRule": "Earn level 3 or higher under rubric cae-2026-ail-01-1.0, with signed primary and transfer artifacts." }, { "outcomeId": "AIL.2", "verification": "faculty-rubric", "evidencePrompt": "Explain stage-specific security challenges with cited data, model, infrastructure, and human factors.", "passingRule": "Earn level 3 or higher under rubric cae-2026-ail-02-1.0, with signed primary and transfer artifacts." }, { "outcomeId": "AIL.3", "verification": "faculty-rubric", "evidencePrompt": "Trace vulnerabilities across data, model, API, plugin, identity, and deployment component connections.", "passingRule": "Earn level 3 or higher under rubric cae-2026-ail-03-1.0, with signed primary and transfer artifacts." }, { "outcomeId": "AIL.4", "verification": "faculty-rubric", "evidencePrompt": "Implement and verify confidentiality, integrity, and availability controls in an AI-enabled pipeline.", "passingRule": "Earn level 3 or higher under rubric cae-2026-ail-04-1.0, with signed primary and transfer artifacts." }, { "outcomeId": "AIL.5", "verification": "faculty-rubric", "evidencePrompt": "Design, implement, attack, and evaluate controls that protect model behavior and artifacts.", "passingRule": "Earn level 3 or higher under rubric cae-2026-ail-05-1.0, with signed primary and transfer artifacts." }, { "outcomeId": "AIL.6", "verification": "faculty-rubric", "evidencePrompt": "Integrate a model into a service and verify abuse handling, rollback, recovery, and residual risk.", "passingRule": "Earn level 3 or higher under rubric cae-2026-ail-06-1.0, with signed primary and transfer artifacts." } ], "transferVariantId": "cae-practicum-ail-transfer", "scoringPolicy": { "minimumOutcomeRate": 1, "minimumFacultyScore": 3, "requiresTransfer": true } }, { "id": "cae-practicum-mla", "kuId": "MLA", "pathwayIds": [ "secure-ai" ], "title": "Algorithm implementation shootout", "mission": "Implement, tune, compare, and ensemble learning algorithms for hostile and imbalanced security data.", "workbench": "model-lab", "estimatedMinutes": 135, "studentActions": [ "Implement two algorithms in the constrained model workbench.", "Select paradigms and tune parameters against held-out data.", "Build an ensemble and test accuracy and robustness after distribution shift." ], "artifactRequirements": [ "Versioned algorithm configurations and test outputs.", "Model-selection report with ensemble and robustness results." ], "outcomeChecks": [ { "outcomeId": "MLA.1", "verification": "faculty-rubric", "evidencePrompt": "Compare algorithms on assumptions, complexity, data fit, metrics, and failure modes.", "passingRule": "Earn level 3 or higher under rubric cae-2026-mla-01-1.0, with signed primary and transfer artifacts." }, { "outcomeId": "MLA.2", "verification": "faculty-rubric", "evidencePrompt": "Implement the algorithm best suited to supplied data and verify it against a baseline.", "passingRule": "Earn level 3 or higher under rubric cae-2026-mla-02-1.0, with signed primary and transfer artifacts." }, { "outcomeId": "MLA.3", "verification": "faculty-rubric", "evidencePrompt": "Select supervised, unsupervised, semi-supervised, active, or reinforcement learning for a use case.", "passingRule": "Earn level 3 or higher under rubric cae-2026-mla-03-1.0, with signed primary and transfer artifacts." }, { "outcomeId": "MLA.4", "verification": "faculty-rubric", "evidencePrompt": "Implement and assess multiple algorithms on the same problem with reproducible evaluation.", "passingRule": "Earn level 3 or higher under rubric cae-2026-mla-04-1.0, with signed primary and transfer artifacts." }, { "outcomeId": "MLA.5", "verification": "faculty-rubric", "evidencePrompt": "Tune parameters using held-out validation while measuring accuracy, robustness, and overfitting.", "passingRule": "Earn level 3 or higher under rubric cae-2026-mla-05-1.0, with signed primary and transfer artifacts." }, { "outcomeId": "MLA.6", "verification": "faculty-rubric", "evidencePrompt": "Implement an ensemble and compare it with its constituent models on normal and shifted data.", "passingRule": "Earn level 3 or higher under rubric cae-2026-mla-06-1.0, with signed primary and transfer artifacts." } ], "transferVariantId": "cae-practicum-mla-transfer", "scoringPolicy": { "minimumOutcomeRate": 1, "minimumFacultyScore": 3, "requiresTransfer": true } }, { "id": "cae-practicum-dpl", "kuId": "DPL", "pathwayIds": [ "secure-ai" ], "title": "Deep model construction and validation", "mission": "Construct and validate multilayer and sequence models, then test explanations and synthetic-data choices under security constraints.", "workbench": "model-lab", "estimatedMinutes": 150, "studentActions": [ "Configure a multilayer network and inspect activation, loss, and bias behavior.", "Model a short event sequence and compare it with a non-sequential baseline.", "Generate bounded synthetic data, validate the model, and evaluate an explanation." ], "artifactRequirements": [ "Model graph, training trace, and validation metrics.", "Explanation audit and paradigm-selection memo." ], "outcomeChecks": [ { "outcomeId": "DPL.1", "verification": "faculty-rubric", "evidencePrompt": "Implement and train a multilayer neural network with explicit architecture and reproducible initialization.", "passingRule": "Earn level 3 or higher under rubric cae-2026-dpl-01-1.0, with signed primary and transfer artifacts." }, { "outcomeId": "DPL.2", "verification": "faculty-rubric", "evidencePrompt": "Model sequential event data with a recurrent architecture and compare a non-sequential baseline.", "passingRule": "Earn level 3 or higher under rubric cae-2026-dpl-02-1.0, with signed primary and transfer artifacts." }, { "outcomeId": "DPL.3", "verification": "faculty-rubric", "evidencePrompt": "Explain and experimentally compare activation functions, loss functions, and bias terms.", "passingRule": "Earn level 3 or higher under rubric cae-2026-dpl-03-1.0, with signed primary and transfer artifacts." }, { "outcomeId": "DPL.4", "verification": "faculty-rubric", "evidencePrompt": "Generate synthetic data and measure fidelity, privacy, bias, and downstream utility.", "passingRule": "Earn level 3 or higher under rubric cae-2026-dpl-04-1.0, with signed primary and transfer artifacts." }, { "outcomeId": "DPL.5", "verification": "faculty-rubric", "evidencePrompt": "Design a reinforcement-learning use case and identify state, action, reward, policy, and safety constraints.", "passingRule": "Earn level 3 or higher under rubric cae-2026-dpl-05-1.0, with signed primary and transfer artifacts." }, { "outcomeId": "DPL.6", "verification": "faculty-rubric", "evidencePrompt": "Validate a trained network with held-out data, calibration, robustness, and error analysis.", "passingRule": "Earn level 3 or higher under rubric cae-2026-dpl-06-1.0, with signed primary and transfer artifacts." }, { "outcomeId": "DPL.7", "verification": "faculty-rubric", "evidencePrompt": "Evaluate a trained network with an explainability method and test explanation faithfulness.", "passingRule": "Earn level 3 or higher under rubric cae-2026-dpl-07-1.0, with signed primary and transfer artifacts." }, { "outcomeId": "DPL.8", "verification": "faculty-rubric", "evidencePrompt": "Select a learning paradigm for a use case and defend it against viable alternatives.", "passingRule": "Earn level 3 or higher under rubric cae-2026-dpl-08-1.0, with signed primary and transfer artifacts." } ], "transferVariantId": "cae-practicum-dpl-transfer", "scoringPolicy": { "minimumOutcomeRate": 1, "minimumFacultyScore": 3, "requiresTransfer": true } }, { "id": "cae-practicum-mes", "kuId": "MES", "pathwayIds": [ "secure-ai" ], "title": "Model selection and evaluation board", "mission": "Specify model requirements, compare candidate systems, tune without leakage, and defend a selection across performance, risk, fairness, privacy, latency, cost, and explainability.", "workbench": "model-lab", "estimatedMinutes": 130, "studentActions": [ "Turn stakeholder needs and operating constraints into testable model requirements.", "Run reproducible baseline, calibration, subgroup, robustness, and hyperparameter comparisons.", "Defend the selected model and evaluation methods while documenting tradeoffs and rejected alternatives." ], "artifactRequirements": [ "Versioned model specification and reproducible comparison report.", "Bias, tradeoff, and evaluation-method decision record." ], "outcomeChecks": [ { "outcomeId": "MES.1", "verification": "faculty-rubric", "evidencePrompt": "Translate task objectives, constraints, risks, latency, data, and interpretability needs into model requirements.", "passingRule": "Earn level 3 or higher under rubric cae-2026-mes-01-1.0, with signed primary and transfer artifacts." }, { "outcomeId": "MES.2", "verification": "faculty-rubric", "evidencePrompt": "Select candidate models using explicit problem and operating criteria.", "passingRule": "Earn level 3 or higher under rubric cae-2026-mes-02-1.0, with signed primary and transfer artifacts." }, { "outcomeId": "MES.3", "verification": "faculty-rubric", "evidencePrompt": "Evaluate model appropriateness with task-valid metrics, baselines, calibration, and failure analysis.", "passingRule": "Earn level 3 or higher under rubric cae-2026-mes-03-1.0, with signed primary and transfer artifacts." }, { "outcomeId": "MES.4", "verification": "faculty-rubric", "evidencePrompt": "Optimize inputs, prompts, and hyperparameters using a reproducible search without test-set leakage.", "passingRule": "Earn level 3 or higher under rubric cae-2026-mes-04-1.0, with signed primary and transfer artifacts." }, { "outcomeId": "MES.5", "verification": "faculty-rubric", "evidencePrompt": "Analyze accuracy, robustness, fairness, privacy, latency, cost, and explainability tradeoffs.", "passingRule": "Earn level 3 or higher under rubric cae-2026-mes-05-1.0, with signed primary and transfer artifacts." }, { "outcomeId": "MES.6", "verification": "faculty-rubric", "evidencePrompt": "Measure data quality and subgroup bias effects on model behavior.", "passingRule": "Earn level 3 or higher under rubric cae-2026-mes-06-1.0, with signed primary and transfer artifacts." }, { "outcomeId": "MES.7", "verification": "faculty-rubric", "evidencePrompt": "Evaluate consent, provenance, representation, and downstream ethical concerns in data and model use.", "passingRule": "Earn level 3 or higher under rubric cae-2026-mes-07-1.0, with signed primary and transfer artifacts." }, { "outcomeId": "MES.8", "verification": "faculty-rubric", "evidencePrompt": "Justify evaluation methods and show why rejected metrics would misrepresent performance.", "passingRule": "Earn level 3 or higher under rubric cae-2026-mes-08-1.0, with signed primary and transfer artifacts." } ], "transferVariantId": "cae-practicum-mes-transfer", "scoringPolicy": { "minimumOutcomeRate": 1, "minimumFacultyScore": 3, "requiresTransfer": true } }, { "id": "cae-practicum-adl", "kuId": "ADL", "pathwayIds": [ "secure-ai" ], "title": "Adversarial robustness range", "mission": "Attack and defend an ML pipeline with adversarial examples, poisoning, extraction pressure, and an insider-risk scenario.", "workbench": "threat-lab", "estimatedMinutes": 120, "studentActions": [ "Generate bounded adversarial examples and identify attacker objectives.", "Measure robustness before and after two defenses.", "Analyze privacy, ethics, and insider behavior without profiling protected traits." ], "artifactRequirements": [ "Attack-and-defense experiment record.", "Robustness, privacy, and insider-risk analysis." ], "outcomeChecks": [ { "outcomeId": "ADL.1", "verification": "faculty-rubric", "evidencePrompt": "Craft bounded adversarial examples and measure how perturbations change model decisions.", "passingRule": "Earn level 3 or higher under rubric cae-2026-adl-01-1.0, with signed primary and transfer artifacts." }, { "outcomeId": "ADL.2", "verification": "faculty-rubric", "evidencePrompt": "Identify attacker access, knowledge, capability, objective, and success conditions for an AI target.", "passingRule": "Earn level 3 or higher under rubric cae-2026-adl-02-1.0, with signed primary and transfer artifacts." }, { "outcomeId": "ADL.3", "verification": "faculty-rubric", "evidencePrompt": "Measure model robustness across evasion, poisoning, extraction, inversion, and prompt-injection pressure.", "passingRule": "Earn level 3 or higher under rubric cae-2026-adl-03-1.0, with signed primary and transfer artifacts." }, { "outcomeId": "ADL.4", "verification": "faculty-rubric", "evidencePrompt": "Apply and compare defenses and quantify robustness, utility, and operational tradeoffs.", "passingRule": "Earn level 3 or higher under rubric cae-2026-adl-04-1.0, with signed primary and transfer artifacts." }, { "outcomeId": "ADL.5", "verification": "faculty-rubric", "evidencePrompt": "Analyze privacy, consent, misuse, and disclosure issues in an adversarial experiment.", "passingRule": "Earn level 3 or higher under rubric cae-2026-adl-05-1.0, with signed primary and transfer artifacts." }, { "outcomeId": "ADL.6", "verification": "faculty-rubric", "evidencePrompt": "Model insider-risk behaviors without inferring or profiling protected characteristics.", "passingRule": "Earn level 3 or higher under rubric cae-2026-adl-06-1.0, with signed primary and transfer artifacts." } ], "transferVariantId": "cae-practicum-adl-transfer", "scoringPolicy": { "minimumOutcomeRate": 1, "minimumFacultyScore": 3, "requiresTransfer": true } }, { "id": "cae-practicum-air", "kuId": "AIR", "pathwayIds": [ "secure-ai" ], "title": "AI risk management tabletop", "mission": "Benchmark, threat-model, mitigate, and continuously reassess an AI service after a material control failure.", "workbench": "governance-hearing", "estimatedMinutes": 120, "studentActions": [ "Select a benchmark and build the attack-surface model.", "Assess vulnerability impact and implement proportionate mitigations.", "Produce an actionable report and defend the continuous-improvement plan." ], "artifactRequirements": [ "Risk register, threat model, and mitigation verification.", "Faculty-rated executive report and oral defense." ], "outcomeChecks": [ { "outcomeId": "AIR.1", "verification": "faculty-rubric", "evidencePrompt": "Evaluate and select a benchmarking framework appropriate to an AI system and decision context.", "passingRule": "Earn level 3 or higher under rubric cae-2026-air-01-1.0, with signed primary and transfer artifacts." }, { "outcomeId": "AIR.2", "verification": "faculty-rubric", "evidencePrompt": "Produce an actionable risk report with evidence, owners, priorities, deadlines, and verification criteria.", "passingRule": "Earn level 3 or higher under rubric cae-2026-air-02-1.0, with signed primary and transfer artifacts." }, { "outcomeId": "AIR.3", "verification": "faculty-rubric", "evidencePrompt": "Run a repeated assessment cycle and show how monitoring evidence changes the risk decision.", "passingRule": "Earn level 3 or higher under rubric cae-2026-air-03-1.0, with signed primary and transfer artifacts." }, { "outcomeId": "AIR.4", "verification": "faculty-rubric", "evidencePrompt": "Design mitigations for model, data, infrastructure, supply-chain, and human AI threats.", "passingRule": "Earn level 3 or higher under rubric cae-2026-air-04-1.0, with signed primary and transfer artifacts." }, { "outcomeId": "AIR.5", "verification": "faculty-rubric", "evidencePrompt": "Assess vulnerability impact on model behavior, data, users, operations, and dependent systems.", "passingRule": "Earn level 3 or higher under rubric cae-2026-air-05-1.0, with signed primary and transfer artifacts." }, { "outcomeId": "AIR.6", "verification": "faculty-rubric", "evidencePrompt": "Implement a mitigation and verify risk reduction without unacceptable functional regression.", "passingRule": "Earn level 3 or higher under rubric cae-2026-air-06-1.0, with signed primary and transfer artifacts." }, { "outcomeId": "AIR.7", "verification": "faculty-rubric", "evidencePrompt": "Map an AI attack surface and construct a threat model with assets, actors, paths, controls, and residual risks.", "passingRule": "Earn level 3 or higher under rubric cae-2026-air-07-1.0, with signed primary and transfer artifacts." } ], "transferVariantId": "cae-practicum-air-transfer", "scoringPolicy": { "minimumOutcomeRate": 1, "minimumFacultyScore": 3, "requiresTransfer": true } }, { "id": "cae-practicum-bnw", "kuId": "BNW", "pathwayIds": [ "ai-cyber" ], "title": "Network build and packet investigation", "mission": "Design a segmented network, trace a TCP exchange, inspect packet flow, map hosts, and diagnose vulnerable services.", "workbench": "network-lab", "estimatedMinutes": 120, "studentActions": [ "Build a topology from operational requirements.", "Trace handshake and application packets in the protocol workbench.", "Run bounded monitoring and mapping tools, then explain the exposed services." ], "artifactRequirements": [ "Validated network topology and packet trace.", "Monitoring capture and vulnerability map." ], "outcomeChecks": [ { "outcomeId": "BNW.1", "verification": "deterministic", "evidencePrompt": "Explain addressing, routing, switching, transport, services, components, and operational network constraints.", "passingRule": "Pass the signed bnw-01 primary and transfer fixtures under rubric cae-2026-bnw-01-1.0." }, { "outcomeId": "BNW.2", "verification": "deterministic", "evidencePrompt": "Design and validate a network topology that satisfies availability, segmentation, and service requirements.", "passingRule": "Pass the signed bnw-02 primary and transfer fixtures under rubric cae-2026-bnw-02-1.0." }, { "outcomeId": "BNW.3", "verification": "deterministic", "evidencePrompt": "Trace sequence numbers, flags, acknowledgments, and payload flow through a TCP exchange.", "passingRule": "Pass the signed bnw-03 primary and transfer fixtures under rubric cae-2026-bnw-03-1.0." }, { "outcomeId": "BNW.4", "verification": "deterministic", "evidencePrompt": "Capture and filter packets with a monitoring tool and explain observed protocol behavior.", "passingRule": "Pass the signed bnw-04 primary and transfer fixtures under rubric cae-2026-bnw-04-1.0." }, { "outcomeId": "BNW.5", "verification": "deterministic", "evidencePrompt": "Perform authorized host and service mapping and reconcile results with the topology.", "passingRule": "Pass the signed bnw-05 primary and transfer fixtures under rubric cae-2026-bnw-05-1.0." }, { "outcomeId": "BNW.6", "verification": "deterministic", "evidencePrompt": "Identify network vulnerabilities from protocol, configuration, exposure, and service evidence.", "passingRule": "Pass the signed bnw-06 primary and transfer fixtures under rubric cae-2026-bnw-06-1.0." } ], "transferVariantId": "cae-practicum-bnw-transfer", "scoringPolicy": { "minimumOutcomeRate": 1, "minimumFacultyScore": null, "requiresTransfer": true } }, { "id": "cae-practicum-ndf", "kuId": "NDF", "pathwayIds": [ "ai-cyber" ], "title": "Layered network defense exercise", "mission": "Defend an AI-enabled service with layered controls, policy, monitoring, containment, and recovery under live traffic.", "workbench": "network-lab", "estimatedMinutes": 105, "studentActions": [ "Place preventive, detective, and recovery controls.", "Test policy and tool behavior against normal and hostile flows.", "Change operations, contain the incident, and verify service recovery." ], "artifactRequirements": [ "Defense-in-depth topology and control results.", "Policy/operations decision log with recovery evidence." ], "outcomeChecks": [ { "outcomeId": "NDF.1", "verification": "faculty-rubric", "evidencePrompt": "Design defense in depth that reduces exposure while preserving authorized network behavior.", "passingRule": "Earn level 3 or higher under rubric cae-2026-ndf-01-1.0, with signed primary and transfer artifacts." }, { "outcomeId": "NDF.2", "verification": "faculty-rubric", "evidencePrompt": "Configure and test network-defense tools against normal, malformed, and hostile traffic.", "passingRule": "Earn level 3 or higher under rubric cae-2026-ndf-02-1.0, with signed primary and transfer artifacts." }, { "outcomeId": "NDF.3", "verification": "faculty-rubric", "evidencePrompt": "Evaluate access, segmentation, monitoring, response, and recovery policy enforcement.", "passingRule": "Earn level 3 or higher under rubric cae-2026-ndf-03-1.0, with signed primary and transfer artifacts." }, { "outcomeId": "NDF.4", "verification": "faculty-rubric", "evidencePrompt": "Measure how operational changes affect visibility, attack paths, resilience, and recovery.", "passingRule": "Earn level 3 or higher under rubric cae-2026-ndf-04-1.0, with signed primary and transfer artifacts." } ], "transferVariantId": "cae-practicum-ndf-transfer", "scoringPolicy": { "minimumOutcomeRate": 1, "minimumFacultyScore": 3, "requiresTransfer": true } }, { "id": "cae-practicum-ais", "kuId": "AIS", "pathwayIds": [ "ai-cyber" ], "title": "AI-augmented security assessment", "mission": "Supervise AI-enhanced discovery, validate every claim, assess vulnerability impact, and deliver an actionable security report.", "workbench": "agent-console", "estimatedMinutes": 120, "studentActions": [ "Run a bounded AI-assisted assessment and inspect its evidence.", "Reject unsupported findings and construct a threat model.", "Prioritize mitigations and defend the final report to a reviewer." ], "artifactRequirements": [ "Replayable assessment evidence chain.", "Faculty-rated report, threat model, and recommendations." ], "outcomeChecks": [ { "outcomeId": "AIS.1", "verification": "faculty-rubric", "evidencePrompt": "Conduct an authorized security evaluation using AI-enhanced and conventional tools and compare their evidence.", "passingRule": "Earn level 3 or higher under rubric cae-2026-ais-01-1.0, with signed primary and transfer artifacts." }, { "outcomeId": "AIS.2", "verification": "faculty-rubric", "evidencePrompt": "Produce a reproducible assessment report with prioritized, actionable, evidence-linked recommendations.", "passingRule": "Earn level 3 or higher under rubric cae-2026-ais-02-1.0, with signed primary and transfer artifacts." }, { "outcomeId": "AIS.3", "verification": "faculty-rubric", "evidencePrompt": "Use AI to assess vulnerability impact, reject unsupported claims, and design and verify mitigation.", "passingRule": "Earn level 3 or higher under rubric cae-2026-ais-03-1.0, with signed primary and transfer artifacts." }, { "outcomeId": "AIS.4", "verification": "faculty-rubric", "evidencePrompt": "Build a threat model from observed attack surfaces and validate mitigation against a transfer scenario.", "passingRule": "Earn level 3 or higher under rubric cae-2026-ais-04-1.0, with signed primary and transfer artifacts." } ], "transferVariantId": "cae-practicum-ais-transfer", "scoringPolicy": { "minimumOutcomeRate": 1, "minimumFacultyScore": 3, "requiresTransfer": true } }, { "id": "cae-practicum-dfa", "kuId": "DFA", "pathwayIds": [ "ai-cyber" ], "title": "Defensive AI operator qualification", "mission": "Configure and supervise an explainable defensive agent through detection, response, threat-intelligence mapping, and transfer.", "workbench": "agent-console", "estimatedMinutes": 125, "studentActions": [ "Select a bounded AI workflow and authority envelope.", "Validate detections and map behaviors to MITRE ATLAS.", "Approve or reject action, explain the model output, and transfer to a new incident." ], "artifactRequirements": [ "Agent configuration, claim-validation, and action ledger.", "MITRE ATLAS mapping and explanation audit." ], "outcomeChecks": [ { "outcomeId": "DFA.1", "verification": "faculty-rubric", "evidencePrompt": "Implement a bounded AI workflow that measurably improves detection, triage, or response while retaining human control.", "passingRule": "Earn level 3 or higher under rubric cae-2026-dfa-01-1.0, with signed primary and transfer artifacts." }, { "outcomeId": "DFA.2", "verification": "faculty-rubric", "evidencePrompt": "Develop or apply a model to identify cyber risks and verify mitigation against held-out events.", "passingRule": "Earn level 3 or higher under rubric cae-2026-dfa-02-1.0, with signed primary and transfer artifacts." }, { "outcomeId": "DFA.3", "verification": "faculty-rubric", "evidencePrompt": "Map observed adversary behavior and defensive evidence to MITRE ATLAS techniques.", "passingRule": "Earn level 3 or higher under rubric cae-2026-dfa-03-1.0, with signed primary and transfer artifacts." }, { "outcomeId": "DFA.4", "verification": "faculty-rubric", "evidencePrompt": "Generate and evaluate explanations for defensive model decisions, including misleading failure cases.", "passingRule": "Earn level 3 or higher under rubric cae-2026-dfa-04-1.0, with signed primary and transfer artifacts." } ], "transferVariantId": "cae-practicum-dfa-transfer", "scoringPolicy": { "minimumOutcomeRate": 1, "minimumFacultyScore": 3, "requiresTransfer": true } }, { "id": "cae-practicum-ofa", "kuId": "OFA", "pathwayIds": [ "ai-cyber" ], "title": "Authorized offensive AI range", "mission": "Plan and execute a bounded AI-assisted offensive exercise inside an authorized range, measure effectiveness and detectability, and map behavior to ATT&CK or ATLAS with legal and ethical controls.", "workbench": "agent-console", "estimatedMinutes": 135, "studentActions": [ "Define authorization, scope, prohibited actions, approvals, and stop conditions before execution.", "Use AI to augment a bounded tactic while capturing every prompt, tool call, result, and human decision.", "Measure effectiveness, reliability, cost, transfer limits, and defensive detections, then defend the exercise under review." ], "artifactRequirements": [ "Signed authorization, action ledger, and replayable range transcript.", "Faculty-rated effectiveness, framework mapping, legal, ethical, and defensive analysis." ], "outcomeChecks": [ { "outcomeId": "OFA.1", "verification": "faculty-rubric", "evidencePrompt": "Identify and reproduce bounded adversarial attacks on a model inside an authorized range.", "passingRule": "Earn level 3 or higher under rubric cae-2026-ofa-01-1.0, with signed primary and transfer artifacts." }, { "outcomeId": "OFA.2", "verification": "faculty-rubric", "evidencePrompt": "Identify AI-enabled opportunities in an authorized offensive objective and reject prohibited scope.", "passingRule": "Earn level 3 or higher under rubric cae-2026-ofa-02-1.0, with signed primary and transfer artifacts." }, { "outcomeId": "OFA.3", "verification": "faculty-rubric", "evidencePrompt": "Augment a tactic, technique, or procedure with AI while preserving approval and evidence boundaries.", "passingRule": "Earn level 3 or higher under rubric cae-2026-ofa-03-1.0, with signed primary and transfer artifacts." }, { "outcomeId": "OFA.4", "verification": "faculty-rubric", "evidencePrompt": "Measure AI-enabled attack effectiveness, reliability, detectability, cost, and transfer limitations.", "passingRule": "Earn level 3 or higher under rubric cae-2026-ofa-04-1.0, with signed primary and transfer artifacts." }, { "outcomeId": "OFA.5", "verification": "faculty-rubric", "evidencePrompt": "Apply legal, ethical, authorization, minimization, and disclosure constraints to an offensive exercise.", "passingRule": "Earn level 3 or higher under rubric cae-2026-ofa-05-1.0, with signed primary and transfer artifacts." }, { "outcomeId": "OFA.6", "verification": "faculty-rubric", "evidencePrompt": "Map offensive AI behavior to MITRE ATT&CK or ATLAS and identify corresponding detections and mitigations.", "passingRule": "Earn level 3 or higher under rubric cae-2026-ofa-06-1.0, with signed primary and transfer artifacts." } ], "transferVariantId": "cae-practicum-ofa-transfer", "scoringPolicy": { "minimumOutcomeRate": 1, "minimumFacultyScore": 3, "requiresTransfer": true } } ], "outcomeContracts": { "CSF": [ { "kuId": "CSF", "outcomeId": "CSF.1", "performanceId": "csf-01", "observableAction": "Identify confidentiality, integrity, availability, authenticity, and nonrepudiation in an incident record.", "runtime": "cyber-range", "primaryFixtureId": "csf-01-primary-2026.1", "transferFixtureId": "csf-01-transfer-2026.1", "scoring": { "mode": "hybrid", "rubricVersion": "cae-2026-csf-01-1.0", "criteria": [ "The primary artifact contains observable, reproducible evidence that the learner can identify confidentiality, integrity, availability, authenticity, and nonrepudiation in an incident record.", "The learner repeats the underlying method on the unfamiliar CSF.1 transfer fixture and explains material differences without relying on memorized answers." ], "minimumLevel": 3 } }, { "kuId": "CSF", "outcomeId": "CSF.2", "performanceId": "csf-02", "observableAction": "Prioritize competing cybersecurity goals for a service with explicit operational constraints.", "runtime": "cyber-range", "primaryFixtureId": "csf-02-primary-2026.1", "transferFixtureId": "csf-02-transfer-2026.1", "scoring": { "mode": "hybrid", "rubricVersion": "cae-2026-csf-02-1.0", "criteria": [ "The primary artifact contains observable, reproducible evidence that the learner can prioritize competing cybersecurity goals for a service with explicit operational constraints.", "The learner repeats the underlying method on the unfamiliar CSF.2 transfer fixture and explains material differences without relying on memorized answers." ], "minimumLevel": 3 } }, { "kuId": "CSF", "outcomeId": "CSF.3", "performanceId": "csf-03", "observableAction": "Infer an attacker's objective, capability, access, and motivation from cited telemetry.", "runtime": "cyber-range", "primaryFixtureId": "csf-03-primary-2026.1", "transferFixtureId": "csf-03-transfer-2026.1", "scoring": { "mode": "hybrid", "rubricVersion": "cae-2026-csf-03-1.0", "criteria": [ "The primary artifact contains observable, reproducible evidence that the learner can infer an attacker's objective, capability, access, and motivation from cited telemetry.", "The learner repeats the underlying method on the unfamiliar CSF.3 transfer fixture and explains material differences without relying on memorized answers." ], "minimumLevel": 3 } }, { "kuId": "CSF", "outcomeId": "CSF.4", "performanceId": "csf-04", "observableAction": "Classify attack techniques from host, identity, network, and application evidence.", "runtime": "cyber-range", "primaryFixtureId": "csf-04-primary-2026.1", "transferFixtureId": "csf-04-transfer-2026.1", "scoring": { "mode": "hybrid", "rubricVersion": "cae-2026-csf-04-1.0", "criteria": [ "The primary artifact contains observable, reproducible evidence that the learner can classify attack techniques from host, identity, network, and application evidence.", "The learner repeats the underlying method on the unfamiliar CSF.4 transfer fixture and explains material differences without relying on memorized answers." ], "minimumLevel": 3 } }, { "kuId": "CSF", "outcomeId": "CSF.5", "performanceId": "csf-05", "observableAction": "Rank vulnerabilities by exploitability, impact, exposure, and affected assets.", "runtime": "cyber-range", "primaryFixtureId": "csf-05-primary-2026.1", "transferFixtureId": "csf-05-transfer-2026.1", "scoring": { "mode": "hybrid", "rubricVersion": "cae-2026-csf-05-1.0", "criteria": [ "The primary artifact contains observable, reproducible evidence that the learner can rank vulnerabilities by exploitability, impact, exposure, and affected assets.", "The learner repeats the underlying method on the unfamiliar CSF.5 transfer fixture and explains material differences without relying on memorized answers." ], "minimumLevel": 3 } }, { "kuId": "CSF", "outcomeId": "CSF.6", "performanceId": "csf-06", "observableAction": "Distinguish malware behavior families from process, persistence, and network indicators.", "runtime": "cyber-range", "primaryFixtureId": "csf-06-primary-2026.1", "transferFixtureId": "csf-06-transfer-2026.1", "scoring": { "mode": "hybrid", "rubricVersion": "cae-2026-csf-06-1.0", "criteria": [ "The primary artifact contains observable, reproducible evidence that the learner can distinguish malware behavior families from process, persistence, and network indicators.", "The learner repeats the underlying method on the unfamiliar CSF.6 transfer fixture and explains material differences without relying on memorized answers." ], "minimumLevel": 3 } }, { "kuId": "CSF", "outcomeId": "CSF.7", "performanceId": "csf-07", "observableAction": "Match preventive, detective, corrective, and recovery mechanisms to observed attack paths.", "runtime": "cyber-range", "primaryFixtureId": "csf-07-primary-2026.1", "transferFixtureId": "csf-07-transfer-2026.1", "scoring": { "mode": "hybrid", "rubricVersion": "cae-2026-csf-07-1.0", "criteria": [ "The primary artifact contains observable, reproducible evidence that the learner can match preventive, detective, corrective, and recovery mechanisms to observed attack paths.", "The learner repeats the underlying method on the unfamiliar CSF.7 transfer fixture and explains material differences without relying on memorized answers." ], "minimumLevel": 3 } }, { "kuId": "CSF", "outcomeId": "CSF.8", "performanceId": "csf-08", "observableAction": "Design and test layered protection for an unfamiliar system while preserving required service behavior.", "runtime": "cyber-range", "primaryFixtureId": "csf-08-primary-2026.1", "transferFixtureId": "csf-08-transfer-2026.1", "scoring": { "mode": "hybrid", "rubricVersion": "cae-2026-csf-08-1.0", "criteria": [ "The primary artifact contains observable, reproducible evidence that the learner can design and test layered protection for an unfamiliar system while preserving required service behavior.", "The learner repeats the underlying method on the unfamiliar CSF.8 transfer fixture and explains material differences without relying on memorized answers." ], "minimumLevel": 3 } }, { "kuId": "CSF", "outcomeId": "CSF.9", "performanceId": "csf-09", "observableAction": "Configure identification, authentication, authorization, and audit controls and verify enforcement.", "runtime": "cyber-range", "primaryFixtureId": "csf-09-primary-2026.1", "transferFixtureId": "csf-09-transfer-2026.1", "scoring": { "mode": "hybrid", "rubricVersion": "cae-2026-csf-09-1.0", "criteria": [ "The primary artifact contains observable, reproducible evidence that the learner can configure identification, authentication, authorization, and audit controls and verify enforcement.", "The learner repeats the underlying method on the unfamiliar CSF.9 transfer fixture and explains material differences without relying on memorized answers." ], "minimumLevel": 3 } }, { "kuId": "CSF", "outcomeId": "CSF.10", "performanceId": "csf-10", "observableAction": "Analyze a dated cybersecurity event from authoritative sources and separate confirmed facts from inference.", "runtime": "cyber-range", "primaryFixtureId": "csf-10-primary-2026.1", "transferFixtureId": "csf-10-transfer-2026.1", "scoring": { "mode": "hybrid", "rubricVersion": "cae-2026-csf-10-1.0", "criteria": [ "The primary artifact contains observable, reproducible evidence that the learner can analyze a dated cybersecurity event from authoritative sources and separate confirmed facts from inference.", "The learner repeats the underlying method on the unfamiliar CSF.10 transfer fixture and explains material differences without relying on memorized answers." ], "minimumLevel": 3 } } ], "ISC": [ { "kuId": "ISC", "outcomeId": "ISC.1", "performanceId": "isc-01", "observableAction": "Inventory processor, memory, storage, accelerator, and peripheral roles from a running-system profile.", "runtime": "systems-lab", "primaryFixtureId": "isc-01-primary-2026.1", "transferFixtureId": "isc-01-transfer-2026.1", "scoring": { "mode": "hybrid", "rubricVersion": "cae-2026-isc-01-1.0", "criteria": [ "The primary artifact contains observable, reproducible evidence that the learner can inventory processor, memory, storage, accelerator, and peripheral roles from a running-system profile.", "The learner repeats the underlying method on the unfamiliar ISC.1 transfer fixture and explains material differences without relying on memorized answers." ], "minimumLevel": 3 } }, { "kuId": "ISC", "outcomeId": "ISC.2", "performanceId": "isc-02", "observableAction": "Reconstruct LAN, WAN, wireless, cloud, and service connections from topology and flow evidence.", "runtime": "systems-lab", "primaryFixtureId": "isc-02-primary-2026.1", "transferFixtureId": "isc-02-transfer-2026.1", "scoring": { "mode": "hybrid", "rubricVersion": "cae-2026-isc-02-1.0", "criteria": [ "The primary artifact contains observable, reproducible evidence that the learner can reconstruct LAN, WAN, wireless, cloud, and service connections from topology and flow evidence.", "The learner repeats the underlying method on the unfamiliar ISC.2 transfer fixture and explains material differences without relying on memorized answers." ], "minimumLevel": 3 } }, { "kuId": "ISC", "outcomeId": "ISC.3", "performanceId": "isc-03", "observableAction": "Assign operational ownership for provisioning, monitoring, patching, backup, and incident response.", "runtime": "systems-lab", "primaryFixtureId": "isc-03-primary-2026.1", "transferFixtureId": "isc-03-transfer-2026.1", "scoring": { "mode": "hybrid", "rubricVersion": "cae-2026-isc-03-1.0", "criteria": [ "The primary artifact contains observable, reproducible evidence that the learner can assign operational ownership for provisioning, monitoring, patching, backup, and incident response.", "The learner repeats the underlying method on the unfamiliar ISC.3 transfer fixture and explains material differences without relying on memorized answers." ], "minimumLevel": 3 } }, { "kuId": "ISC", "outcomeId": "ISC.4", "performanceId": "isc-04", "observableAction": "Construct hardware, operating-system, application, data, and service architecture boundaries.", "runtime": "systems-lab", "primaryFixtureId": "isc-04-primary-2026.1", "transferFixtureId": "isc-04-transfer-2026.1", "scoring": { "mode": "hybrid", "rubricVersion": "cae-2026-isc-04-1.0", "criteria": [ "The primary artifact contains observable, reproducible evidence that the learner can construct hardware, operating-system, application, data, and service architecture boundaries.", "The learner repeats the underlying method on the unfamiliar ISC.4 transfer fixture and explains material differences without relying on memorized answers." ], "minimumLevel": 3 } }, { "kuId": "ISC", "outcomeId": "ISC.5", "performanceId": "isc-05", "observableAction": "Evaluate software acquisition, configuration, versioning, patch, and retirement controls.", "runtime": "systems-lab", "primaryFixtureId": "isc-05-primary-2026.1", "transferFixtureId": "isc-05-transfer-2026.1", "scoring": { "mode": "hybrid", "rubricVersion": "cae-2026-isc-05-1.0", "criteria": [ "The primary artifact contains observable, reproducible evidence that the learner can evaluate software acquisition, configuration, versioning, patch, and retirement controls.", "The learner repeats the underlying method on the unfamiliar ISC.5 transfer fixture and explains material differences without relying on memorized answers." ], "minimumLevel": 3 } }, { "kuId": "ISC", "outcomeId": "ISC.6", "performanceId": "isc-06", "observableAction": "Locate component-specific security failures and select controls that preserve system operation.", "runtime": "systems-lab", "primaryFixtureId": "isc-06-primary-2026.1", "transferFixtureId": "isc-06-transfer-2026.1", "scoring": { "mode": "hybrid", "rubricVersion": "cae-2026-isc-06-1.0", "criteria": [ "The primary artifact contains observable, reproducible evidence that the learner can locate component-specific security failures and select controls that preserve system operation.", "The learner repeats the underlying method on the unfamiliar ISC.6 transfer fixture and explains material differences without relying on memorized answers." ], "minimumLevel": 3 } } ], "BSP": [ { "kuId": "BSP", "outcomeId": "BSP.1", "performanceId": "bsp-01", "observableAction": "Write and execute a script that automates a bounded system-administration workflow.", "runtime": "code-runner", "primaryFixtureId": "bsp-01-primary-2026.1", "transferFixtureId": "bsp-01-transfer-2026.1", "scoring": { "mode": "deterministic", "rubricVersion": "cae-2026-bsp-01-1.0", "criteria": [ "The primary artifact contains observable, reproducible evidence that the learner can write and execute a script that automates a bounded system-administration workflow.", "The learner repeats the underlying method on the unfamiliar BSP.1 transfer fixture and explains material differences without relying on memorized answers." ], "minimumLevel": 3 } }, { "kuId": "BSP", "outcomeId": "BSP.2", "performanceId": "bsp-02", "observableAction": "Implement variables, branches, loops, functions, collections, and file or console input in an executable program.", "runtime": "code-runner", "primaryFixtureId": "bsp-02-primary-2026.1", "transferFixtureId": "bsp-02-transfer-2026.1", "scoring": { "mode": "deterministic", "rubricVersion": "cae-2026-bsp-02-1.0", "criteria": [ "The primary artifact contains observable, reproducible evidence that the learner can implement variables, branches, loops, functions, collections, and file or console input in an executable program.", "The learner repeats the underlying method on the unfamiliar BSP.2 transfer fixture and explains material differences without relying on memorized answers." ], "minimumLevel": 3 } }, { "kuId": "BSP", "outcomeId": "BSP.3", "performanceId": "bsp-03", "observableAction": "Reject malformed, oversized, mistyped, and out-of-range inputs through executable checks.", "runtime": "code-runner", "primaryFixtureId": "bsp-03-primary-2026.1", "transferFixtureId": "bsp-03-transfer-2026.1", "scoring": { "mode": "deterministic", "rubricVersion": "cae-2026-bsp-03-1.0", "criteria": [ "The primary artifact contains observable, reproducible evidence that the learner can reject malformed, oversized, mistyped, and out-of-range inputs through executable checks.", "The learner repeats the underlying method on the unfamiliar BSP.3 transfer fixture and explains material differences without relying on memorized answers." ], "minimumLevel": 3 } }, { "kuId": "BSP", "outcomeId": "BSP.4", "performanceId": "bsp-04", "observableAction": "Write and test regular expressions against valid, invalid, boundary, and adversarial records.", "runtime": "code-runner", "primaryFixtureId": "bsp-04-primary-2026.1", "transferFixtureId": "bsp-04-transfer-2026.1", "scoring": { "mode": "deterministic", "rubricVersion": "cae-2026-bsp-04-1.0", "criteria": [ "The primary artifact contains observable, reproducible evidence that the learner can write and test regular expressions against valid, invalid, boundary, and adversarial records.", "The learner repeats the underlying method on the unfamiliar BSP.4 transfer fixture and explains material differences without relying on memorized answers." ], "minimumLevel": 3 } }, { "kuId": "BSP", "outcomeId": "BSP.5", "performanceId": "bsp-05", "observableAction": "Implement and compare basic collections, search, grouping, sorting, and traversal algorithms.", "runtime": "code-runner", "primaryFixtureId": "bsp-05-primary-2026.1", "transferFixtureId": "bsp-05-transfer-2026.1", "scoring": { "mode": "deterministic", "rubricVersion": "cae-2026-bsp-05-1.0", "criteria": [ "The primary artifact contains observable, reproducible evidence that the learner can implement and compare basic collections, search, grouping, sorting, and traversal algorithms.", "The learner repeats the underlying method on the unfamiliar BSP.5 transfer fixture and explains material differences without relying on memorized answers." ], "minimumLevel": 3 } } ], "MAF": [ { "kuId": "MAF", "outcomeId": "MAF.1", "performanceId": "maf-01", "observableAction": "Solve and verify linear equations, inequalities, and systems arising from a cyber risk model.", "runtime": "math-notebook", "primaryFixtureId": "maf-01-primary-2026.1", "transferFixtureId": "maf-01-transfer-2026.1", "scoring": { "mode": "deterministic", "rubricVersion": "cae-2026-maf-01-1.0", "criteria": [ "The primary artifact contains observable, reproducible evidence that the learner can solve and verify linear equations, inequalities, and systems arising from a cyber risk model.", "The learner repeats the underlying method on the unfamiliar MAF.1 transfer fixture and explains material differences without relying on memorized answers." ], "minimumLevel": 3 } }, { "kuId": "MAF", "outcomeId": "MAF.2", "performanceId": "maf-02", "observableAction": "Graph and compare linear, quadratic, polynomial, rational, exponential, and logarithmic functions.", "runtime": "math-notebook", "primaryFixtureId": "maf-02-primary-2026.1", "transferFixtureId": "maf-02-transfer-2026.1", "scoring": { "mode": "deterministic", "rubricVersion": "cae-2026-maf-02-1.0", "criteria": [ "The primary artifact contains observable, reproducible evidence that the learner can graph and compare linear, quadratic, polynomial, rational, exponential, and logarithmic functions.", "The learner repeats the underlying method on the unfamiliar MAF.2 transfer fixture and explains material differences without relying on memorized answers." ], "minimumLevel": 3 } }, { "kuId": "MAF", "outcomeId": "MAF.3", "performanceId": "maf-03", "observableAction": "Compose functions, calculate inverses, and verify domain and range restrictions.", "runtime": "math-notebook", "primaryFixtureId": "maf-03-primary-2026.1", "transferFixtureId": "maf-03-transfer-2026.1", "scoring": { "mode": "deterministic", "rubricVersion": "cae-2026-maf-03-1.0", "criteria": [ "The primary artifact contains observable, reproducible evidence that the learner can compose functions, calculate inverses, and verify domain and range restrictions.", "The learner repeats the underlying method on the unfamiliar MAF.3 transfer fixture and explains material differences without relying on memorized answers." ], "minimumLevel": 3 } }, { "kuId": "MAF", "outcomeId": "MAF.4", "performanceId": "maf-04", "observableAction": "Solve polynomial, exponential, and logarithmic equations and check numerical residuals.", "runtime": "math-notebook", "primaryFixtureId": "maf-04-primary-2026.1", "transferFixtureId": "maf-04-transfer-2026.1", "scoring": { "mode": "deterministic", "rubricVersion": "cae-2026-maf-04-1.0", "criteria": [ "The primary artifact contains observable, reproducible evidence that the learner can solve polynomial, exponential, and logarithmic equations and check numerical residuals.", "The learner repeats the underlying method on the unfamiliar MAF.4 transfer fixture and explains material differences without relying on memorized answers." ], "minimumLevel": 3 } }, { "kuId": "MAF", "outcomeId": "MAF.5", "performanceId": "maf-05", "observableAction": "Construct an algebraic model for an applied sequence, series, cost, or capacity problem.", "runtime": "math-notebook", "primaryFixtureId": "maf-05-primary-2026.1", "transferFixtureId": "maf-05-transfer-2026.1", "scoring": { "mode": "deterministic", "rubricVersion": "cae-2026-maf-05-1.0", "criteria": [ "The primary artifact contains observable, reproducible evidence that the learner can construct an algebraic model for an applied sequence, series, cost, or capacity problem.", "The learner repeats the underlying method on the unfamiliar MAF.5 transfer fixture and explains material differences without relying on memorized answers." ], "minimumLevel": 3 } }, { "kuId": "MAF", "outcomeId": "MAF.6", "performanceId": "maf-06", "observableAction": "Select descriptive or inferential methods and justify the choice for a supplied dataset.", "runtime": "math-notebook", "primaryFixtureId": "maf-06-primary-2026.1", "transferFixtureId": "maf-06-transfer-2026.1", "scoring": { "mode": "deterministic", "rubricVersion": "cae-2026-maf-06-1.0", "criteria": [ "The primary artifact contains observable, reproducible evidence that the learner can select descriptive or inferential methods and justify the choice for a supplied dataset.", "The learner repeats the underlying method on the unfamiliar MAF.6 transfer fixture and explains material differences without relying on memorized answers." ], "minimumLevel": 3 } }, { "kuId": "MAF", "outcomeId": "MAF.7", "performanceId": "maf-07", "observableAction": "Build tables and charts and calculate central tendency, spread, and distribution summaries.", "runtime": "math-notebook", "primaryFixtureId": "maf-07-primary-2026.1", "transferFixtureId": "maf-07-transfer-2026.1", "scoring": { "mode": "deterministic", "rubricVersion": "cae-2026-maf-07-1.0", "criteria": [ "The primary artifact contains observable, reproducible evidence that the learner can build tables and charts and calculate central tendency, spread, and distribution summaries.", "The learner repeats the underlying method on the unfamiliar MAF.7 transfer fixture and explains material differences without relying on memorized answers." ], "minimumLevel": 3 } }, { "kuId": "MAF", "outcomeId": "MAF.8", "performanceId": "maf-08", "observableAction": "Calculate conditional, joint, and complementary probabilities for an uncertain security event.", "runtime": "math-notebook", "primaryFixtureId": "maf-08-primary-2026.1", "transferFixtureId": "maf-08-transfer-2026.1", "scoring": { "mode": "deterministic", "rubricVersion": "cae-2026-maf-08-1.0", "criteria": [ "The primary artifact contains observable, reproducible evidence that the learner can calculate conditional, joint, and complementary probabilities for an uncertain security event.", "The learner repeats the underlying method on the unfamiliar MAF.8 transfer fixture and explains material differences without relying on memorized answers." ], "minimumLevel": 3 } }, { "kuId": "MAF", "outcomeId": "MAF.9", "performanceId": "maf-09", "observableAction": "Run an iterative numerical method and report convergence, stopping conditions, and approximation error.", "runtime": "math-notebook", "primaryFixtureId": "maf-09-primary-2026.1", "transferFixtureId": "maf-09-transfer-2026.1", "scoring": { "mode": "deterministic", "rubricVersion": "cae-2026-maf-09-1.0", "criteria": [ "The primary artifact contains observable, reproducible evidence that the learner can run an iterative numerical method and report convergence, stopping conditions, and approximation error.", "The learner repeats the underlying method on the unfamiliar MAF.9 transfer fixture and explains material differences without relying on memorized answers." ], "minimumLevel": 3 } } ], "AIG": [ { "kuId": "AIG", "outcomeId": "AIG.1", "performanceId": "aig-01", "observableAction": "Compare current US and international AI laws, regulations, policies, standards, and oversight for a deployment.", "runtime": "source-hearing", "primaryFixtureId": "aig-01-primary-2026.1", "transferFixtureId": "aig-01-transfer-2026.1", "scoring": { "mode": "dual-faculty", "rubricVersion": "cae-2026-aig-01-1.0", "criteria": [ "The primary artifact contains observable, reproducible evidence that the learner can compare current US and international AI laws, regulations, policies, standards, and oversight for a deployment.", "The learner repeats the underlying method on the unfamiliar AIG.1 transfer fixture and explains material differences without relying on memorized answers." ], "minimumLevel": 3 } }, { "kuId": "AIG", "outcomeId": "AIG.2", "performanceId": "aig-02", "observableAction": "Assign legal and ethical responsibilities to developers, deployers, operators, users, and affected parties.", "runtime": "source-hearing", "primaryFixtureId": "aig-02-primary-2026.1", "transferFixtureId": "aig-02-transfer-2026.1", "scoring": { "mode": "dual-faculty", "rubricVersion": "cae-2026-aig-02-1.0", "criteria": [ "The primary artifact contains observable, reproducible evidence that the learner can assign legal and ethical responsibilities to developers, deployers, operators, users, and affected parties.", "The learner repeats the underlying method on the unfamiliar AIG.2 transfer fixture and explains material differences without relying on memorized answers." ], "minimumLevel": 3 } }, { "kuId": "AIG", "outcomeId": "AIG.3", "performanceId": "aig-03", "observableAction": "Audit fairness, transparency, accountability, privacy, and nondiscrimination claims with subgroup evidence.", "runtime": "source-hearing", "primaryFixtureId": "aig-03-primary-2026.1", "transferFixtureId": "aig-03-transfer-2026.1", "scoring": { "mode": "dual-faculty", "rubricVersion": "cae-2026-aig-03-1.0", "criteria": [ "The primary artifact contains observable, reproducible evidence that the learner can audit fairness, transparency, accountability, privacy, and nondiscrimination claims with subgroup evidence.", "The learner repeats the underlying method on the unfamiliar AIG.3 transfer fixture and explains material differences without relying on memorized answers." ], "minimumLevel": 3 } }, { "kuId": "AIG", "outcomeId": "AIG.4", "performanceId": "aig-04", "observableAction": "Determine how cited laws and regulations change the design, security, release, and monitoring plan.", "runtime": "source-hearing", "primaryFixtureId": "aig-04-primary-2026.1", "transferFixtureId": "aig-04-transfer-2026.1", "scoring": { "mode": "dual-faculty", "rubricVersion": "cae-2026-aig-04-1.0", "criteria": [ "The primary artifact contains observable, reproducible evidence that the learner can determine how cited laws and regulations change the design, security, release, and monitoring plan.", "The learner repeats the underlying method on the unfamiliar AIG.4 transfer fixture and explains material differences without relying on memorized answers." ], "minimumLevel": 3 } } ], "AIF": [ { "kuId": "AIF", "outcomeId": "AIF.1", "performanceId": "aif-01", "observableAction": "Explain an AI system's concepts, components, tools, technologies, and historical development from its architecture.", "runtime": "ai-search-lab", "primaryFixtureId": "aif-01-primary-2026.1", "transferFixtureId": "aif-01-transfer-2026.1", "scoring": { "mode": "hybrid", "rubricVersion": "cae-2026-aif-01-1.0", "criteria": [ "The primary artifact contains observable, reproducible evidence that the learner can explain an AI system's concepts, components, tools, technologies, and historical development from its architecture.", "The learner repeats the underlying method on the unfamiliar AIF.1 transfer fixture and explains material differences without relying on memorized answers." ], "minimumLevel": 3 } }, { "kuId": "AIF", "outcomeId": "AIF.2", "performanceId": "aif-02", "observableAction": "Execute and compare classical search, optimization, and rule-based algorithms on a bounded problem.", "runtime": "ai-search-lab", "primaryFixtureId": "aif-02-primary-2026.1", "transferFixtureId": "aif-02-transfer-2026.1", "scoring": { "mode": "hybrid", "rubricVersion": "cae-2026-aif-02-1.0", "criteria": [ "The primary artifact contains observable, reproducible evidence that the learner can execute and compare classical search, optimization, and rule-based algorithms on a bounded problem.", "The learner repeats the underlying method on the unfamiliar AIF.2 transfer fixture and explains material differences without relying on memorized answers." ], "minimumLevel": 3 } }, { "kuId": "AIF", "outcomeId": "AIF.3", "performanceId": "aif-03", "observableAction": "Measure how an AI method changes cyber detection, prioritization, or risk prediction performance.", "runtime": "ai-search-lab", "primaryFixtureId": "aif-03-primary-2026.1", "transferFixtureId": "aif-03-transfer-2026.1", "scoring": { "mode": "hybrid", "rubricVersion": "cae-2026-aif-03-1.0", "criteria": [ "The primary artifact contains observable, reproducible evidence that the learner can measure how an AI method changes cyber detection, prioritization, or risk prediction performance.", "The learner repeats the underlying method on the unfamiliar AIF.3 transfer fixture and explains material differences without relying on memorized answers." ], "minimumLevel": 3 } }, { "kuId": "AIF", "outcomeId": "AIF.4", "performanceId": "aif-04", "observableAction": "Select or reject an AI tool for a cyber task using measured suitability, risk, and operating constraints.", "runtime": "ai-search-lab", "primaryFixtureId": "aif-04-primary-2026.1", "transferFixtureId": "aif-04-transfer-2026.1", "scoring": { "mode": "hybrid", "rubricVersion": "cae-2026-aif-04-1.0", "criteria": [ "The primary artifact contains observable, reproducible evidence that the learner can select or reject an AI tool for a cyber task using measured suitability, risk, and operating constraints.", "The learner repeats the underlying method on the unfamiliar AIF.4 transfer fixture and explains material differences without relying on memorized answers." ], "minimumLevel": 3 } }, { "kuId": "AIF", "outcomeId": "AIF.5", "performanceId": "aif-05", "observableAction": "Compare AI applications across security and a transfer domain without conflating their evidence needs.", "runtime": "ai-search-lab", "primaryFixtureId": "aif-05-primary-2026.1", "transferFixtureId": "aif-05-transfer-2026.1", "scoring": { "mode": "hybrid", "rubricVersion": "cae-2026-aif-05-1.0", "criteria": [ "The primary artifact contains observable, reproducible evidence that the learner can compare AI applications across security and a transfer domain without conflating their evidence needs.", "The learner repeats the underlying method on the unfamiliar AIF.5 transfer fixture and explains material differences without relying on memorized answers." ], "minimumLevel": 3 } }, { "kuId": "AIF", "outcomeId": "AIF.6", "performanceId": "aif-06", "observableAction": "Find bias, privacy, safety, security, and ethical failure modes across an AI lifecycle.", "runtime": "ai-search-lab", "primaryFixtureId": "aif-06-primary-2026.1", "transferFixtureId": "aif-06-transfer-2026.1", "scoring": { "mode": "hybrid", "rubricVersion": "cae-2026-aif-06-1.0", "criteria": [ "The primary artifact contains observable, reproducible evidence that the learner can find bias, privacy, safety, security, and ethical failure modes across an AI lifecycle.", "The learner repeats the underlying method on the unfamiliar AIF.6 transfer fixture and explains material differences without relying on memorized answers." ], "minimumLevel": 3 } }, { "kuId": "AIF", "outcomeId": "AIF.7", "performanceId": "aif-07", "observableAction": "Formulate a business problem and choose an intelligent-system approach with a defensible non-AI baseline.", "runtime": "ai-search-lab", "primaryFixtureId": "aif-07-primary-2026.1", "transferFixtureId": "aif-07-transfer-2026.1", "scoring": { "mode": "hybrid", "rubricVersion": "cae-2026-aif-07-1.0", "criteria": [ "The primary artifact contains observable, reproducible evidence that the learner can formulate a business problem and choose an intelligent-system approach with a defensible non-AI baseline.", "The learner repeats the underlying method on the unfamiliar AIF.7 transfer fixture and explains material differences without relying on memorized answers." ], "minimumLevel": 3 } } ], "MLF": [ { "kuId": "MLF", "outcomeId": "MLF.1", "performanceId": "mlf-01", "observableAction": "Define machine learning and distinguish supervised, unsupervised, semi-supervised, and reinforcement methods.", "runtime": "ml-workbench", "primaryFixtureId": "mlf-01-primary-2026.1", "transferFixtureId": "mlf-01-transfer-2026.1", "scoring": { "mode": "hybrid", "rubricVersion": "cae-2026-mlf-01-1.0", "criteria": [ "The primary artifact contains observable, reproducible evidence that the learner can define machine learning and distinguish supervised, unsupervised, semi-supervised, and reinforcement methods.", "The learner repeats the underlying method on the unfamiliar MLF.1 transfer fixture and explains material differences without relying on memorized answers." ], "minimumLevel": 3 } }, { "kuId": "MLF", "outcomeId": "MLF.2", "performanceId": "mlf-02", "observableAction": "Explain and execute representative classification, regression, clustering, and anomaly-detection algorithms.", "runtime": "ml-workbench", "primaryFixtureId": "mlf-02-primary-2026.1", "transferFixtureId": "mlf-02-transfer-2026.1", "scoring": { "mode": "hybrid", "rubricVersion": "cae-2026-mlf-02-1.0", "criteria": [ "The primary artifact contains observable, reproducible evidence that the learner can explain and execute representative classification, regression, clustering, and anomaly-detection algorithms.", "The learner repeats the underlying method on the unfamiliar MLF.2 transfer fixture and explains material differences without relying on memorized answers." ], "minimumLevel": 3 } }, { "kuId": "MLF", "outcomeId": "MLF.3", "performanceId": "mlf-03", "observableAction": "Compare learning methods on common data, metrics, compute cost, and error patterns.", "runtime": "ml-workbench", "primaryFixtureId": "mlf-03-primary-2026.1", "transferFixtureId": "mlf-03-transfer-2026.1", "scoring": { "mode": "hybrid", "rubricVersion": "cae-2026-mlf-03-1.0", "criteria": [ "The primary artifact contains observable, reproducible evidence that the learner can compare learning methods on common data, metrics, compute cost, and error patterns.", "The learner repeats the underlying method on the unfamiliar MLF.3 transfer fixture and explains material differences without relying on memorized answers." ], "minimumLevel": 3 } }, { "kuId": "MLF", "outcomeId": "MLF.4", "performanceId": "mlf-04", "observableAction": "Determine whether supplied problems are suitable for machine learning and state a credible baseline.", "runtime": "ml-workbench", "primaryFixtureId": "mlf-04-primary-2026.1", "transferFixtureId": "mlf-04-transfer-2026.1", "scoring": { "mode": "hybrid", "rubricVersion": "cae-2026-mlf-04-1.0", "criteria": [ "The primary artifact contains observable, reproducible evidence that the learner can determine whether supplied problems are suitable for machine learning and state a credible baseline.", "The learner repeats the underlying method on the unfamiliar MLF.4 transfer fixture and explains material differences without relying on memorized answers." ], "minimumLevel": 3 } }, { "kuId": "MLF", "outcomeId": "MLF.5", "performanceId": "mlf-05", "observableAction": "Evaluate trained methods with held-out data, cross-validation, confusion matrices, and error analysis.", "runtime": "ml-workbench", "primaryFixtureId": "mlf-05-primary-2026.1", "transferFixtureId": "mlf-05-transfer-2026.1", "scoring": { "mode": "hybrid", "rubricVersion": "cae-2026-mlf-05-1.0", "criteria": [ "The primary artifact contains observable, reproducible evidence that the learner can evaluate trained methods with held-out data, cross-validation, confusion matrices, and error analysis.", "The learner repeats the underlying method on the unfamiliar MLF.5 transfer fixture and explains material differences without relying on memorized answers." ], "minimumLevel": 3 } }, { "kuId": "MLF", "outcomeId": "MLF.6", "performanceId": "mlf-06", "observableAction": "Inspect and transform tabular, text, categorical, temporal, and binary data formats.", "runtime": "ml-workbench", "primaryFixtureId": "mlf-06-primary-2026.1", "transferFixtureId": "mlf-06-transfer-2026.1", "scoring": { "mode": "hybrid", "rubricVersion": "cae-2026-mlf-06-1.0", "criteria": [ "The primary artifact contains observable, reproducible evidence that the learner can inspect and transform tabular, text, categorical, temporal, and binary data formats.", "The learner repeats the underlying method on the unfamiliar MLF.6 transfer fixture and explains material differences without relying on memorized answers." ], "minimumLevel": 3 } }, { "kuId": "MLF", "outcomeId": "MLF.7", "performanceId": "mlf-07", "observableAction": "Train and evaluate a learning method on a cybersecurity dataset.", "runtime": "ml-workbench", "primaryFixtureId": "mlf-07-primary-2026.1", "transferFixtureId": "mlf-07-transfer-2026.1", "scoring": { "mode": "hybrid", "rubricVersion": "cae-2026-mlf-07-1.0", "criteria": [ "The primary artifact contains observable, reproducible evidence that the learner can train and evaluate a learning method on a cybersecurity dataset.", "The learner repeats the underlying method on the unfamiliar MLF.7 transfer fixture and explains material differences without relying on memorized answers." ], "minimumLevel": 3 } }, { "kuId": "MLF", "outcomeId": "MLF.8", "performanceId": "mlf-08", "observableAction": "Analyze an unfamiliar security case with a trained method and explain failures under distribution shift.", "runtime": "ml-workbench", "primaryFixtureId": "mlf-08-primary-2026.1", "transferFixtureId": "mlf-08-transfer-2026.1", "scoring": { "mode": "hybrid", "rubricVersion": "cae-2026-mlf-08-1.0", "criteria": [ "The primary artifact contains observable, reproducible evidence that the learner can analyze an unfamiliar security case with a trained method and explain failures under distribution shift.", "The learner repeats the underlying method on the unfamiliar MLF.8 transfer fixture and explains material differences without relying on memorized answers." ], "minimumLevel": 3 } } ], "COF": [ { "kuId": "COF", "outcomeId": "COF.1", "performanceId": "cof-01", "observableAction": "Design and truth-table a logic circuit that performs a stated computational task.", "runtime": "logic-workbench", "primaryFixtureId": "cof-01-primary-2026.1", "transferFixtureId": "cof-01-transfer-2026.1", "scoring": { "mode": "deterministic", "rubricVersion": "cae-2026-cof-01-1.0", "criteria": [ "The primary artifact contains observable, reproducible evidence that the learner can design and truth-table a logic circuit that performs a stated computational task.", "The learner repeats the underlying method on the unfamiliar COF.1 transfer fixture and explains material differences without relying on memorized answers." ], "minimumLevel": 3 } }, { "kuId": "COF", "outcomeId": "COF.2", "performanceId": "cof-02", "observableAction": "Encode and decode signed integers, text, and structured fields in binary representations.", "runtime": "logic-workbench", "primaryFixtureId": "cof-02-primary-2026.1", "transferFixtureId": "cof-02-transfer-2026.1", "scoring": { "mode": "deterministic", "rubricVersion": "cae-2026-cof-02-1.0", "criteria": [ "The primary artifact contains observable, reproducible evidence that the learner can encode and decode signed integers, text, and structured fields in binary representations.", "The learner repeats the underlying method on the unfamiliar COF.2 transfer fixture and explains material differences without relying on memorized answers." ], "minimumLevel": 3 } }, { "kuId": "COF", "outcomeId": "COF.3", "performanceId": "cof-03", "observableAction": "Select and operate arrays, lists, stacks, queues, maps, sets, trees, or graphs for stated access patterns.", "runtime": "logic-workbench", "primaryFixtureId": "cof-03-primary-2026.1", "transferFixtureId": "cof-03-transfer-2026.1", "scoring": { "mode": "deterministic", "rubricVersion": "cae-2026-cof-03-1.0", "criteria": [ "The primary artifact contains observable, reproducible evidence that the learner can select and operate arrays, lists, stacks, queues, maps, sets, trees, or graphs for stated access patterns.", "The learner repeats the underlying method on the unfamiliar COF.3 transfer fixture and explains material differences without relying on memorized answers." ], "minimumLevel": 3 } }, { "kuId": "COF", "outcomeId": "COF.4", "performanceId": "cof-04", "observableAction": "Refactor a program into explicit abstractions and modules and verify interfaces with tests.", "runtime": "logic-workbench", "primaryFixtureId": "cof-04-primary-2026.1", "transferFixtureId": "cof-04-transfer-2026.1", "scoring": { "mode": "deterministic", "rubricVersion": "cae-2026-cof-04-1.0", "criteria": [ "The primary artifact contains observable, reproducible evidence that the learner can refactor a program into explicit abstractions and modules and verify interfaces with tests.", "The learner repeats the underlying method on the unfamiliar COF.4 transfer fixture and explains material differences without relying on memorized answers." ], "minimumLevel": 3 } }, { "kuId": "COF", "outcomeId": "COF.5", "performanceId": "cof-05", "observableAction": "Measure operation counts and characterize best, average, and worst-case algorithm growth.", "runtime": "logic-workbench", "primaryFixtureId": "cof-05-primary-2026.1", "transferFixtureId": "cof-05-transfer-2026.1", "scoring": { "mode": "deterministic", "rubricVersion": "cae-2026-cof-05-1.0", "criteria": [ "The primary artifact contains observable, reproducible evidence that the learner can measure operation counts and characterize best, average, and worst-case algorithm growth.", "The learner repeats the underlying method on the unfamiliar COF.5 transfer fixture and explains material differences without relying on memorized answers." ], "minimumLevel": 3 } }, { "kuId": "COF", "outcomeId": "COF.6", "performanceId": "cof-06", "observableAction": "Identify an intractable search and implement and evaluate a bounded heuristic.", "runtime": "logic-workbench", "primaryFixtureId": "cof-06-primary-2026.1", "transferFixtureId": "cof-06-transfer-2026.1", "scoring": { "mode": "deterministic", "rubricVersion": "cae-2026-cof-06-1.0", "criteria": [ "The primary artifact contains observable, reproducible evidence that the learner can identify an intractable search and implement and evaluate a bounded heuristic.", "The learner repeats the underlying method on the unfamiliar COF.6 transfer fixture and explains material differences without relying on memorized answers." ], "minimumLevel": 3 } } ], "BNW": [ { "kuId": "BNW", "outcomeId": "BNW.1", "performanceId": "bnw-01", "observableAction": "Explain addressing, routing, switching, transport, services, components, and operational network constraints.", "runtime": "network-range", "primaryFixtureId": "bnw-01-primary-2026.1", "transferFixtureId": "bnw-01-transfer-2026.1", "scoring": { "mode": "deterministic", "rubricVersion": "cae-2026-bnw-01-1.0", "criteria": [ "The primary artifact contains observable, reproducible evidence that the learner can explain addressing, routing, switching, transport, services, components, and operational network constraints.", "The learner repeats the underlying method on the unfamiliar BNW.1 transfer fixture and explains material differences without relying on memorized answers." ], "minimumLevel": 3 } }, { "kuId": "BNW", "outcomeId": "BNW.2", "performanceId": "bnw-02", "observableAction": "Design and validate a network topology that satisfies availability, segmentation, and service requirements.", "runtime": "network-range", "primaryFixtureId": "bnw-02-primary-2026.1", "transferFixtureId": "bnw-02-transfer-2026.1", "scoring": { "mode": "deterministic", "rubricVersion": "cae-2026-bnw-02-1.0", "criteria": [ "The primary artifact contains observable, reproducible evidence that the learner can design and validate a network topology that satisfies availability, segmentation, and service requirements.", "The learner repeats the underlying method on the unfamiliar BNW.2 transfer fixture and explains material differences without relying on memorized answers." ], "minimumLevel": 3 } }, { "kuId": "BNW", "outcomeId": "BNW.3", "performanceId": "bnw-03", "observableAction": "Trace sequence numbers, flags, acknowledgments, and payload flow through a TCP exchange.", "runtime": "network-range", "primaryFixtureId": "bnw-03-primary-2026.1", "transferFixtureId": "bnw-03-transfer-2026.1", "scoring": { "mode": "deterministic", "rubricVersion": "cae-2026-bnw-03-1.0", "criteria": [ "The primary artifact contains observable, reproducible evidence that the learner can trace sequence numbers, flags, acknowledgments, and payload flow through a TCP exchange.", "The learner repeats the underlying method on the unfamiliar BNW.3 transfer fixture and explains material differences without relying on memorized answers." ], "minimumLevel": 3 } }, { "kuId": "BNW", "outcomeId": "BNW.4", "performanceId": "bnw-04", "observableAction": "Capture and filter packets with a monitoring tool and explain observed protocol behavior.", "runtime": "network-range", "primaryFixtureId": "bnw-04-primary-2026.1", "transferFixtureId": "bnw-04-transfer-2026.1", "scoring": { "mode": "deterministic", "rubricVersion": "cae-2026-bnw-04-1.0", "criteria": [ "The primary artifact contains observable, reproducible evidence that the learner can capture and filter packets with a monitoring tool and explain observed protocol behavior.", "The learner repeats the underlying method on the unfamiliar BNW.4 transfer fixture and explains material differences without relying on memorized answers." ], "minimumLevel": 3 } }, { "kuId": "BNW", "outcomeId": "BNW.5", "performanceId": "bnw-05", "observableAction": "Perform authorized host and service mapping and reconcile results with the topology.", "runtime": "network-range", "primaryFixtureId": "bnw-05-primary-2026.1", "transferFixtureId": "bnw-05-transfer-2026.1", "scoring": { "mode": "deterministic", "rubricVersion": "cae-2026-bnw-05-1.0", "criteria": [ "The primary artifact contains observable, reproducible evidence that the learner can perform authorized host and service mapping and reconcile results with the topology.", "The learner repeats the underlying method on the unfamiliar BNW.5 transfer fixture and explains material differences without relying on memorized answers." ], "minimumLevel": 3 } }, { "kuId": "BNW", "outcomeId": "BNW.6", "performanceId": "bnw-06", "observableAction": "Identify network vulnerabilities from protocol, configuration, exposure, and service evidence.", "runtime": "network-range", "primaryFixtureId": "bnw-06-primary-2026.1", "transferFixtureId": "bnw-06-transfer-2026.1", "scoring": { "mode": "deterministic", "rubricVersion": "cae-2026-bnw-06-1.0", "criteria": [ "The primary artifact contains observable, reproducible evidence that the learner can identify network vulnerabilities from protocol, configuration, exposure, and service evidence.", "The learner repeats the underlying method on the unfamiliar BNW.6 transfer fixture and explains material differences without relying on memorized answers." ], "minimumLevel": 3 } } ], "NDF": [ { "kuId": "NDF", "outcomeId": "NDF.1", "performanceId": "ndf-01", "observableAction": "Design defense in depth that reduces exposure while preserving authorized network behavior.", "runtime": "network-range", "primaryFixtureId": "ndf-01-primary-2026.1", "transferFixtureId": "ndf-01-transfer-2026.1", "scoring": { "mode": "hybrid", "rubricVersion": "cae-2026-ndf-01-1.0", "criteria": [ "The primary artifact contains observable, reproducible evidence that the learner can design defense in depth that reduces exposure while preserving authorized network behavior.", "The learner repeats the underlying method on the unfamiliar NDF.1 transfer fixture and explains material differences without relying on memorized answers." ], "minimumLevel": 3 } }, { "kuId": "NDF", "outcomeId": "NDF.2", "performanceId": "ndf-02", "observableAction": "Configure and test network-defense tools against normal, malformed, and hostile traffic.", "runtime": "network-range", "primaryFixtureId": "ndf-02-primary-2026.1", "transferFixtureId": "ndf-02-transfer-2026.1", "scoring": { "mode": "hybrid", "rubricVersion": "cae-2026-ndf-02-1.0", "criteria": [ "The primary artifact contains observable, reproducible evidence that the learner can configure and test network-defense tools against normal, malformed, and hostile traffic.", "The learner repeats the underlying method on the unfamiliar NDF.2 transfer fixture and explains material differences without relying on memorized answers." ], "minimumLevel": 3 } }, { "kuId": "NDF", "outcomeId": "NDF.3", "performanceId": "ndf-03", "observableAction": "Evaluate access, segmentation, monitoring, response, and recovery policy enforcement.", "runtime": "network-range", "primaryFixtureId": "ndf-03-primary-2026.1", "transferFixtureId": "ndf-03-transfer-2026.1", "scoring": { "mode": "hybrid", "rubricVersion": "cae-2026-ndf-03-1.0", "criteria": [ "The primary artifact contains observable, reproducible evidence that the learner can evaluate access, segmentation, monitoring, response, and recovery policy enforcement.", "The learner repeats the underlying method on the unfamiliar NDF.3 transfer fixture and explains material differences without relying on memorized answers." ], "minimumLevel": 3 } }, { "kuId": "NDF", "outcomeId": "NDF.4", "performanceId": "ndf-04", "observableAction": "Measure how operational changes affect visibility, attack paths, resilience, and recovery.", "runtime": "network-range", "primaryFixtureId": "ndf-04-primary-2026.1", "transferFixtureId": "ndf-04-transfer-2026.1", "scoring": { "mode": "hybrid", "rubricVersion": "cae-2026-ndf-04-1.0", "criteria": [ "The primary artifact contains observable, reproducible evidence that the learner can measure how operational changes affect visibility, attack paths, resilience, and recovery.", "The learner repeats the underlying method on the unfamiliar NDF.4 transfer fixture and explains material differences without relying on memorized answers." ], "minimumLevel": 3 } } ], "AVM": [ { "kuId": "AVM", "outcomeId": "AVM.1", "performanceId": "avm-01", "observableAction": "Perform vector and matrix operations and calculate correlation and regression on model data.", "runtime": "math-notebook", "primaryFixtureId": "avm-01-primary-2026.1", "transferFixtureId": "avm-01-transfer-2026.1", "scoring": { "mode": "deterministic", "rubricVersion": "cae-2026-avm-01-1.0", "criteria": [ "The primary artifact contains observable, reproducible evidence that the learner can perform vector and matrix operations and calculate correlation and regression on model data.", "The learner repeats the underlying method on the unfamiliar AVM.1 transfer fixture and explains material differences without relying on memorized answers." ], "minimumLevel": 3 } }, { "kuId": "AVM", "outcomeId": "AVM.2", "performanceId": "avm-02", "observableAction": "Calculate partial derivatives and apply the chain rule to a model-loss function.", "runtime": "math-notebook", "primaryFixtureId": "avm-02-primary-2026.1", "transferFixtureId": "avm-02-transfer-2026.1", "scoring": { "mode": "deterministic", "rubricVersion": "cae-2026-avm-02-1.0", "criteria": [ "The primary artifact contains observable, reproducible evidence that the learner can calculate partial derivatives and apply the chain rule to a model-loss function.", "The learner repeats the underlying method on the unfamiliar AVM.2 transfer fixture and explains material differences without relying on memorized answers." ], "minimumLevel": 3 } }, { "kuId": "AVM", "outcomeId": "AVM.3", "performanceId": "avm-03", "observableAction": "Develop and evaluate an algorithm using statistical and mathematical principles.", "runtime": "math-notebook", "primaryFixtureId": "avm-03-primary-2026.1", "transferFixtureId": "avm-03-transfer-2026.1", "scoring": { "mode": "deterministic", "rubricVersion": "cae-2026-avm-03-1.0", "criteria": [ "The primary artifact contains observable, reproducible evidence that the learner can develop and evaluate an algorithm using statistical and mathematical principles.", "The learner repeats the underlying method on the unfamiliar AVM.3 transfer fixture and explains material differences without relying on memorized answers." ], "minimumLevel": 3 } }, { "kuId": "AVM", "outcomeId": "AVM.4", "performanceId": "avm-04", "observableAction": "Select appropriate graph, search, optimization, or neural-training mathematics for an applied problem.", "runtime": "math-notebook", "primaryFixtureId": "avm-04-primary-2026.1", "transferFixtureId": "avm-04-transfer-2026.1", "scoring": { "mode": "deterministic", "rubricVersion": "cae-2026-avm-04-1.0", "criteria": [ "The primary artifact contains observable, reproducible evidence that the learner can select appropriate graph, search, optimization, or neural-training mathematics for an applied problem.", "The learner repeats the underlying method on the unfamiliar AVM.4 transfer fixture and explains material differences without relying on memorized answers." ], "minimumLevel": 3 } }, { "kuId": "AVM", "outcomeId": "AVM.5", "performanceId": "avm-05", "observableAction": "Construct and execute an AI algorithm from equations, graphs, or search principles.", "runtime": "math-notebook", "primaryFixtureId": "avm-05-primary-2026.1", "transferFixtureId": "avm-05-transfer-2026.1", "scoring": { "mode": "deterministic", "rubricVersion": "cae-2026-avm-05-1.0", "criteria": [ "The primary artifact contains observable, reproducible evidence that the learner can construct and execute an AI algorithm from equations, graphs, or search principles.", "The learner repeats the underlying method on the unfamiliar AVM.5 transfer fixture and explains material differences without relying on memorized answers." ], "minimumLevel": 3 } }, { "kuId": "AVM", "outcomeId": "AVM.6", "performanceId": "avm-06", "observableAction": "Form a hypothesis, select a test, check assumptions, calculate significance, and interpret the decision.", "runtime": "math-notebook", "primaryFixtureId": "avm-06-primary-2026.1", "transferFixtureId": "avm-06-transfer-2026.1", "scoring": { "mode": "deterministic", "rubricVersion": "cae-2026-avm-06-1.0", "criteria": [ "The primary artifact contains observable, reproducible evidence that the learner can form a hypothesis, select a test, check assumptions, calculate significance, and interpret the decision.", "The learner repeats the underlying method on the unfamiliar AVM.6 transfer fixture and explains material differences without relying on memorized answers." ], "minimumLevel": 3 } } ], "MLA": [ { "kuId": "MLA", "outcomeId": "MLA.1", "performanceId": "mla-01", "observableAction": "Compare algorithms on assumptions, complexity, data fit, metrics, and failure modes.", "runtime": "ml-workbench", "primaryFixtureId": "mla-01-primary-2026.1", "transferFixtureId": "mla-01-transfer-2026.1", "scoring": { "mode": "hybrid", "rubricVersion": "cae-2026-mla-01-1.0", "criteria": [ "The primary artifact contains observable, reproducible evidence that the learner can compare algorithms on assumptions, complexity, data fit, metrics, and failure modes.", "The learner repeats the underlying method on the unfamiliar MLA.1 transfer fixture and explains material differences without relying on memorized answers." ], "minimumLevel": 3 } }, { "kuId": "MLA", "outcomeId": "MLA.2", "performanceId": "mla-02", "observableAction": "Implement the algorithm best suited to supplied data and verify it against a baseline.", "runtime": "ml-workbench", "primaryFixtureId": "mla-02-primary-2026.1", "transferFixtureId": "mla-02-transfer-2026.1", "scoring": { "mode": "hybrid", "rubricVersion": "cae-2026-mla-02-1.0", "criteria": [ "The primary artifact contains observable, reproducible evidence that the learner can implement the algorithm best suited to supplied data and verify it against a baseline.", "The learner repeats the underlying method on the unfamiliar MLA.2 transfer fixture and explains material differences without relying on memorized answers." ], "minimumLevel": 3 } }, { "kuId": "MLA", "outcomeId": "MLA.3", "performanceId": "mla-03", "observableAction": "Select supervised, unsupervised, semi-supervised, active, or reinforcement learning for a use case.", "runtime": "ml-workbench", "primaryFixtureId": "mla-03-primary-2026.1", "transferFixtureId": "mla-03-transfer-2026.1", "scoring": { "mode": "hybrid", "rubricVersion": "cae-2026-mla-03-1.0", "criteria": [ "The primary artifact contains observable, reproducible evidence that the learner can select supervised, unsupervised, semi-supervised, active, or reinforcement learning for a use case.", "The learner repeats the underlying method on the unfamiliar MLA.3 transfer fixture and explains material differences without relying on memorized answers." ], "minimumLevel": 3 } }, { "kuId": "MLA", "outcomeId": "MLA.4", "performanceId": "mla-04", "observableAction": "Implement and assess multiple algorithms on the same problem with reproducible evaluation.", "runtime": "ml-workbench", "primaryFixtureId": "mla-04-primary-2026.1", "transferFixtureId": "mla-04-transfer-2026.1", "scoring": { "mode": "hybrid", "rubricVersion": "cae-2026-mla-04-1.0", "criteria": [ "The primary artifact contains observable, reproducible evidence that the learner can implement and assess multiple algorithms on the same problem with reproducible evaluation.", "The learner repeats the underlying method on the unfamiliar MLA.4 transfer fixture and explains material differences without relying on memorized answers." ], "minimumLevel": 3 } }, { "kuId": "MLA", "outcomeId": "MLA.5", "performanceId": "mla-05", "observableAction": "Tune parameters using held-out validation while measuring accuracy, robustness, and overfitting.", "runtime": "ml-workbench", "primaryFixtureId": "mla-05-primary-2026.1", "transferFixtureId": "mla-05-transfer-2026.1", "scoring": { "mode": "hybrid", "rubricVersion": "cae-2026-mla-05-1.0", "criteria": [ "The primary artifact contains observable, reproducible evidence that the learner can tune parameters using held-out validation while measuring accuracy, robustness, and overfitting.", "The learner repeats the underlying method on the unfamiliar MLA.5 transfer fixture and explains material differences without relying on memorized answers." ], "minimumLevel": 3 } }, { "kuId": "MLA", "outcomeId": "MLA.6", "performanceId": "mla-06", "observableAction": "Implement an ensemble and compare it with its constituent models on normal and shifted data.", "runtime": "ml-workbench", "primaryFixtureId": "mla-06-primary-2026.1", "transferFixtureId": "mla-06-transfer-2026.1", "scoring": { "mode": "hybrid", "rubricVersion": "cae-2026-mla-06-1.0", "criteria": [ "The primary artifact contains observable, reproducible evidence that the learner can implement an ensemble and compare it with its constituent models on normal and shifted data.", "The learner repeats the underlying method on the unfamiliar MLA.6 transfer fixture and explains material differences without relying on memorized answers." ], "minimumLevel": 3 } } ], "DPL": [ { "kuId": "DPL", "outcomeId": "DPL.1", "performanceId": "dpl-01", "observableAction": "Implement and train a multilayer neural network with explicit architecture and reproducible initialization.", "runtime": "ml-workbench", "primaryFixtureId": "dpl-01-primary-2026.1", "transferFixtureId": "dpl-01-transfer-2026.1", "scoring": { "mode": "hybrid", "rubricVersion": "cae-2026-dpl-01-1.0", "criteria": [ "The primary artifact contains observable, reproducible evidence that the learner can implement and train a multilayer neural network with explicit architecture and reproducible initialization.", "The learner repeats the underlying method on the unfamiliar DPL.1 transfer fixture and explains material differences without relying on memorized answers." ], "minimumLevel": 3 } }, { "kuId": "DPL", "outcomeId": "DPL.2", "performanceId": "dpl-02", "observableAction": "Model sequential event data with a recurrent architecture and compare a non-sequential baseline.", "runtime": "ml-workbench", "primaryFixtureId": "dpl-02-primary-2026.1", "transferFixtureId": "dpl-02-transfer-2026.1", "scoring": { "mode": "hybrid", "rubricVersion": "cae-2026-dpl-02-1.0", "criteria": [ "The primary artifact contains observable, reproducible evidence that the learner can model sequential event data with a recurrent architecture and compare a non-sequential baseline.", "The learner repeats the underlying method on the unfamiliar DPL.2 transfer fixture and explains material differences without relying on memorized answers." ], "minimumLevel": 3 } }, { "kuId": "DPL", "outcomeId": "DPL.3", "performanceId": "dpl-03", "observableAction": "Explain and experimentally compare activation functions, loss functions, and bias terms.", "runtime": "ml-workbench", "primaryFixtureId": "dpl-03-primary-2026.1", "transferFixtureId": "dpl-03-transfer-2026.1", "scoring": { "mode": "hybrid", "rubricVersion": "cae-2026-dpl-03-1.0", "criteria": [ "The primary artifact contains observable, reproducible evidence that the learner can explain and experimentally compare activation functions, loss functions, and bias terms.", "The learner repeats the underlying method on the unfamiliar DPL.3 transfer fixture and explains material differences without relying on memorized answers." ], "minimumLevel": 3 } }, { "kuId": "DPL", "outcomeId": "DPL.4", "performanceId": "dpl-04", "observableAction": "Generate synthetic data and measure fidelity, privacy, bias, and downstream utility.", "runtime": "ml-workbench", "primaryFixtureId": "dpl-04-primary-2026.1", "transferFixtureId": "dpl-04-transfer-2026.1", "scoring": { "mode": "hybrid", "rubricVersion": "cae-2026-dpl-04-1.0", "criteria": [ "The primary artifact contains observable, reproducible evidence that the learner can generate synthetic data and measure fidelity, privacy, bias, and downstream utility.", "The learner repeats the underlying method on the unfamiliar DPL.4 transfer fixture and explains material differences without relying on memorized answers." ], "minimumLevel": 3 } }, { "kuId": "DPL", "outcomeId": "DPL.5", "performanceId": "dpl-05", "observableAction": "Design a reinforcement-learning use case and identify state, action, reward, policy, and safety constraints.", "runtime": "ml-workbench", "primaryFixtureId": "dpl-05-primary-2026.1", "transferFixtureId": "dpl-05-transfer-2026.1", "scoring": { "mode": "hybrid", "rubricVersion": "cae-2026-dpl-05-1.0", "criteria": [ "The primary artifact contains observable, reproducible evidence that the learner can design a reinforcement-learning use case and identify state, action, reward, policy, and safety constraints.", "The learner repeats the underlying method on the unfamiliar DPL.5 transfer fixture and explains material differences without relying on memorized answers." ], "minimumLevel": 3 } }, { "kuId": "DPL", "outcomeId": "DPL.6", "performanceId": "dpl-06", "observableAction": "Validate a trained network with held-out data, calibration, robustness, and error analysis.", "runtime": "ml-workbench", "primaryFixtureId": "dpl-06-primary-2026.1", "transferFixtureId": "dpl-06-transfer-2026.1", "scoring": { "mode": "hybrid", "rubricVersion": "cae-2026-dpl-06-1.0", "criteria": [ "The primary artifact contains observable, reproducible evidence that the learner can validate a trained network with held-out data, calibration, robustness, and error analysis.", "The learner repeats the underlying method on the unfamiliar DPL.6 transfer fixture and explains material differences without relying on memorized answers." ], "minimumLevel": 3 } }, { "kuId": "DPL", "outcomeId": "DPL.7", "performanceId": "dpl-07", "observableAction": "Evaluate a trained network with an explainability method and test explanation faithfulness.", "runtime": "ml-workbench", "primaryFixtureId": "dpl-07-primary-2026.1", "transferFixtureId": "dpl-07-transfer-2026.1", "scoring": { "mode": "hybrid", "rubricVersion": "cae-2026-dpl-07-1.0", "criteria": [ "The primary artifact contains observable, reproducible evidence that the learner can evaluate a trained network with an explainability method and test explanation faithfulness.", "The learner repeats the underlying method on the unfamiliar DPL.7 transfer fixture and explains material differences without relying on memorized answers." ], "minimumLevel": 3 } }, { "kuId": "DPL", "outcomeId": "DPL.8", "performanceId": "dpl-08", "observableAction": "Select a learning paradigm for a use case and defend it against viable alternatives.", "runtime": "ml-workbench", "primaryFixtureId": "dpl-08-primary-2026.1", "transferFixtureId": "dpl-08-transfer-2026.1", "scoring": { "mode": "hybrid", "rubricVersion": "cae-2026-dpl-08-1.0", "criteria": [ "The primary artifact contains observable, reproducible evidence that the learner can select a learning paradigm for a use case and defend it against viable alternatives.", "The learner repeats the underlying method on the unfamiliar DPL.8 transfer fixture and explains material differences without relying on memorized answers." ], "minimumLevel": 3 } } ], "MES": [ { "kuId": "MES", "outcomeId": "MES.1", "performanceId": "mes-01", "observableAction": "Translate task objectives, constraints, risks, latency, data, and interpretability needs into model requirements.", "runtime": "ml-workbench", "primaryFixtureId": "mes-01-primary-2026.1", "transferFixtureId": "mes-01-transfer-2026.1", "scoring": { "mode": "hybrid", "rubricVersion": "cae-2026-mes-01-1.0", "criteria": [ "The primary artifact contains observable, reproducible evidence that the learner can translate task objectives, constraints, risks, latency, data, and interpretability needs into model requirements.", "The learner repeats the underlying method on the unfamiliar MES.1 transfer fixture and explains material differences without relying on memorized answers." ], "minimumLevel": 3 } }, { "kuId": "MES", "outcomeId": "MES.2", "performanceId": "mes-02", "observableAction": "Select candidate models using explicit problem and operating criteria.", "runtime": "ml-workbench", "primaryFixtureId": "mes-02-primary-2026.1", "transferFixtureId": "mes-02-transfer-2026.1", "scoring": { "mode": "hybrid", "rubricVersion": "cae-2026-mes-02-1.0", "criteria": [ "The primary artifact contains observable, reproducible evidence that the learner can select candidate models using explicit problem and operating criteria.", "The learner repeats the underlying method on the unfamiliar MES.2 transfer fixture and explains material differences without relying on memorized answers." ], "minimumLevel": 3 } }, { "kuId": "MES", "outcomeId": "MES.3", "performanceId": "mes-03", "observableAction": "Evaluate model appropriateness with task-valid metrics, baselines, calibration, and failure analysis.", "runtime": "ml-workbench", "primaryFixtureId": "mes-03-primary-2026.1", "transferFixtureId": "mes-03-transfer-2026.1", "scoring": { "mode": "hybrid", "rubricVersion": "cae-2026-mes-03-1.0", "criteria": [ "The primary artifact contains observable, reproducible evidence that the learner can evaluate model appropriateness with task-valid metrics, baselines, calibration, and failure analysis.", "The learner repeats the underlying method on the unfamiliar MES.3 transfer fixture and explains material differences without relying on memorized answers." ], "minimumLevel": 3 } }, { "kuId": "MES", "outcomeId": "MES.4", "performanceId": "mes-04", "observableAction": "Optimize inputs, prompts, and hyperparameters using a reproducible search without test-set leakage.", "runtime": "ml-workbench", "primaryFixtureId": "mes-04-primary-2026.1", "transferFixtureId": "mes-04-transfer-2026.1", "scoring": { "mode": "hybrid", "rubricVersion": "cae-2026-mes-04-1.0", "criteria": [ "The primary artifact contains observable, reproducible evidence that the learner can optimize inputs, prompts, and hyperparameters using a reproducible search without test-set leakage.", "The learner repeats the underlying method on the unfamiliar MES.4 transfer fixture and explains material differences without relying on memorized answers." ], "minimumLevel": 3 } }, { "kuId": "MES", "outcomeId": "MES.5", "performanceId": "mes-05", "observableAction": "Analyze accuracy, robustness, fairness, privacy, latency, cost, and explainability tradeoffs.", "runtime": "ml-workbench", "primaryFixtureId": "mes-05-primary-2026.1", "transferFixtureId": "mes-05-transfer-2026.1", "scoring": { "mode": "hybrid", "rubricVersion": "cae-2026-mes-05-1.0", "criteria": [ "The primary artifact contains observable, reproducible evidence that the learner can analyze accuracy, robustness, fairness, privacy, latency, cost, and explainability tradeoffs.", "The learner repeats the underlying method on the unfamiliar MES.5 transfer fixture and explains material differences without relying on memorized answers." ], "minimumLevel": 3 } }, { "kuId": "MES", "outcomeId": "MES.6", "performanceId": "mes-06", "observableAction": "Measure data quality and subgroup bias effects on model behavior.", "runtime": "ml-workbench", "primaryFixtureId": "mes-06-primary-2026.1", "transferFixtureId": "mes-06-transfer-2026.1", "scoring": { "mode": "hybrid", "rubricVersion": "cae-2026-mes-06-1.0", "criteria": [ "The primary artifact contains observable, reproducible evidence that the learner can measure data quality and subgroup bias effects on model behavior.", "The learner repeats the underlying method on the unfamiliar MES.6 transfer fixture and explains material differences without relying on memorized answers." ], "minimumLevel": 3 } }, { "kuId": "MES", "outcomeId": "MES.7", "performanceId": "mes-07", "observableAction": "Evaluate consent, provenance, representation, and downstream ethical concerns in data and model use.", "runtime": "ml-workbench", "primaryFixtureId": "mes-07-primary-2026.1", "transferFixtureId": "mes-07-transfer-2026.1", "scoring": { "mode": "hybrid", "rubricVersion": "cae-2026-mes-07-1.0", "criteria": [ "The primary artifact contains observable, reproducible evidence that the learner can evaluate consent, provenance, representation, and downstream ethical concerns in data and model use.", "The learner repeats the underlying method on the unfamiliar MES.7 transfer fixture and explains material differences without relying on memorized answers." ], "minimumLevel": 3 } }, { "kuId": "MES", "outcomeId": "MES.8", "performanceId": "mes-08", "observableAction": "Justify evaluation methods and show why rejected metrics would misrepresent performance.", "runtime": "ml-workbench", "primaryFixtureId": "mes-08-primary-2026.1", "transferFixtureId": "mes-08-transfer-2026.1", "scoring": { "mode": "hybrid", "rubricVersion": "cae-2026-mes-08-1.0", "criteria": [ "The primary artifact contains observable, reproducible evidence that the learner can justify evaluation methods and show why rejected metrics would misrepresent performance.", "The learner repeats the underlying method on the unfamiliar MES.8 transfer fixture and explains material differences without relying on memorized answers." ], "minimumLevel": 3 } } ], "AIL": [ { "kuId": "AIL", "outcomeId": "AIL.1", "performanceId": "ail-01", "observableAction": "Assess and prioritize threats at acquisition, training, deployment, monitoring, and retirement stages.", "runtime": "lifecycle-range", "primaryFixtureId": "ail-01-primary-2026.1", "transferFixtureId": "ail-01-transfer-2026.1", "scoring": { "mode": "hybrid", "rubricVersion": "cae-2026-ail-01-1.0", "criteria": [ "The primary artifact contains observable, reproducible evidence that the learner can assess and prioritize threats at acquisition, training, deployment, monitoring, and retirement stages.", "The learner repeats the underlying method on the unfamiliar AIL.1 transfer fixture and explains material differences without relying on memorized answers." ], "minimumLevel": 3 } }, { "kuId": "AIL", "outcomeId": "AIL.2", "performanceId": "ail-02", "observableAction": "Explain stage-specific security challenges with cited data, model, infrastructure, and human factors.", "runtime": "lifecycle-range", "primaryFixtureId": "ail-02-primary-2026.1", "transferFixtureId": "ail-02-transfer-2026.1", "scoring": { "mode": "hybrid", "rubricVersion": "cae-2026-ail-02-1.0", "criteria": [ "The primary artifact contains observable, reproducible evidence that the learner can explain stage-specific security challenges with cited data, model, infrastructure, and human factors.", "The learner repeats the underlying method on the unfamiliar AIL.2 transfer fixture and explains material differences without relying on memorized answers." ], "minimumLevel": 3 } }, { "kuId": "AIL", "outcomeId": "AIL.3", "performanceId": "ail-03", "observableAction": "Trace vulnerabilities across data, model, API, plugin, identity, and deployment component connections.", "runtime": "lifecycle-range", "primaryFixtureId": "ail-03-primary-2026.1", "transferFixtureId": "ail-03-transfer-2026.1", "scoring": { "mode": "hybrid", "rubricVersion": "cae-2026-ail-03-1.0", "criteria": [ "The primary artifact contains observable, reproducible evidence that the learner can trace vulnerabilities across data, model, API, plugin, identity, and deployment component connections.", "The learner repeats the underlying method on the unfamiliar AIL.3 transfer fixture and explains material differences without relying on memorized answers." ], "minimumLevel": 3 } }, { "kuId": "AIL", "outcomeId": "AIL.4", "performanceId": "ail-04", "observableAction": "Implement and verify confidentiality, integrity, and availability controls in an AI-enabled pipeline.", "runtime": "lifecycle-range", "primaryFixtureId": "ail-04-primary-2026.1", "transferFixtureId": "ail-04-transfer-2026.1", "scoring": { "mode": "hybrid", "rubricVersion": "cae-2026-ail-04-1.0", "criteria": [ "The primary artifact contains observable, reproducible evidence that the learner can implement and verify confidentiality, integrity, and availability controls in an AI-enabled pipeline.", "The learner repeats the underlying method on the unfamiliar AIL.4 transfer fixture and explains material differences without relying on memorized answers." ], "minimumLevel": 3 } }, { "kuId": "AIL", "outcomeId": "AIL.5", "performanceId": "ail-05", "observableAction": "Design, implement, attack, and evaluate controls that protect model behavior and artifacts.", "runtime": "lifecycle-range", "primaryFixtureId": "ail-05-primary-2026.1", "transferFixtureId": "ail-05-transfer-2026.1", "scoring": { "mode": "hybrid", "rubricVersion": "cae-2026-ail-05-1.0", "criteria": [ "The primary artifact contains observable, reproducible evidence that the learner can design, implement, attack, and evaluate controls that protect model behavior and artifacts.", "The learner repeats the underlying method on the unfamiliar AIL.5 transfer fixture and explains material differences without relying on memorized answers." ], "minimumLevel": 3 } }, { "kuId": "AIL", "outcomeId": "AIL.6", "performanceId": "ail-06", "observableAction": "Integrate a model into a service and verify abuse handling, rollback, recovery, and residual risk.", "runtime": "lifecycle-range", "primaryFixtureId": "ail-06-primary-2026.1", "transferFixtureId": "ail-06-transfer-2026.1", "scoring": { "mode": "hybrid", "rubricVersion": "cae-2026-ail-06-1.0", "criteria": [ "The primary artifact contains observable, reproducible evidence that the learner can integrate a model into a service and verify abuse handling, rollback, recovery, and residual risk.", "The learner repeats the underlying method on the unfamiliar AIL.6 transfer fixture and explains material differences without relying on memorized answers." ], "minimumLevel": 3 } } ], "AIR": [ { "kuId": "AIR", "outcomeId": "AIR.1", "performanceId": "air-01", "observableAction": "Evaluate and select a benchmarking framework appropriate to an AI system and decision context.", "runtime": "source-hearing", "primaryFixtureId": "air-01-primary-2026.1", "transferFixtureId": "air-01-transfer-2026.1", "scoring": { "mode": "dual-faculty", "rubricVersion": "cae-2026-air-01-1.0", "criteria": [ "The primary artifact contains observable, reproducible evidence that the learner can evaluate and select a benchmarking framework appropriate to an AI system and decision context.", "The learner repeats the underlying method on the unfamiliar AIR.1 transfer fixture and explains material differences without relying on memorized answers." ], "minimumLevel": 3 } }, { "kuId": "AIR", "outcomeId": "AIR.2", "performanceId": "air-02", "observableAction": "Produce an actionable risk report with evidence, owners, priorities, deadlines, and verification criteria.", "runtime": "source-hearing", "primaryFixtureId": "air-02-primary-2026.1", "transferFixtureId": "air-02-transfer-2026.1", "scoring": { "mode": "dual-faculty", "rubricVersion": "cae-2026-air-02-1.0", "criteria": [ "The primary artifact contains observable, reproducible evidence that the learner can produce an actionable risk report with evidence, owners, priorities, deadlines, and verification criteria.", "The learner repeats the underlying method on the unfamiliar AIR.2 transfer fixture and explains material differences without relying on memorized answers." ], "minimumLevel": 3 } }, { "kuId": "AIR", "outcomeId": "AIR.3", "performanceId": "air-03", "observableAction": "Run a repeated assessment cycle and show how monitoring evidence changes the risk decision.", "runtime": "source-hearing", "primaryFixtureId": "air-03-primary-2026.1", "transferFixtureId": "air-03-transfer-2026.1", "scoring": { "mode": "dual-faculty", "rubricVersion": "cae-2026-air-03-1.0", "criteria": [ "The primary artifact contains observable, reproducible evidence that the learner can run a repeated assessment cycle and show how monitoring evidence changes the risk decision.", "The learner repeats the underlying method on the unfamiliar AIR.3 transfer fixture and explains material differences without relying on memorized answers." ], "minimumLevel": 3 } }, { "kuId": "AIR", "outcomeId": "AIR.4", "performanceId": "air-04", "observableAction": "Design mitigations for model, data, infrastructure, supply-chain, and human AI threats.", "runtime": "source-hearing", "primaryFixtureId": "air-04-primary-2026.1", "transferFixtureId": "air-04-transfer-2026.1", "scoring": { "mode": "dual-faculty", "rubricVersion": "cae-2026-air-04-1.0", "criteria": [ "The primary artifact contains observable, reproducible evidence that the learner can design mitigations for model, data, infrastructure, supply-chain, and human AI threats.", "The learner repeats the underlying method on the unfamiliar AIR.4 transfer fixture and explains material differences without relying on memorized answers." ], "minimumLevel": 3 } }, { "kuId": "AIR", "outcomeId": "AIR.5", "performanceId": "air-05", "observableAction": "Assess vulnerability impact on model behavior, data, users, operations, and dependent systems.", "runtime": "source-hearing", "primaryFixtureId": "air-05-primary-2026.1", "transferFixtureId": "air-05-transfer-2026.1", "scoring": { "mode": "dual-faculty", "rubricVersion": "cae-2026-air-05-1.0", "criteria": [ "The primary artifact contains observable, reproducible evidence that the learner can assess vulnerability impact on model behavior, data, users, operations, and dependent systems.", "The learner repeats the underlying method on the unfamiliar AIR.5 transfer fixture and explains material differences without relying on memorized answers." ], "minimumLevel": 3 } }, { "kuId": "AIR", "outcomeId": "AIR.6", "performanceId": "air-06", "observableAction": "Implement a mitigation and verify risk reduction without unacceptable functional regression.", "runtime": "source-hearing", "primaryFixtureId": "air-06-primary-2026.1", "transferFixtureId": "air-06-transfer-2026.1", "scoring": { "mode": "dual-faculty", "rubricVersion": "cae-2026-air-06-1.0", "criteria": [ "The primary artifact contains observable, reproducible evidence that the learner can implement a mitigation and verify risk reduction without unacceptable functional regression.", "The learner repeats the underlying method on the unfamiliar AIR.6 transfer fixture and explains material differences without relying on memorized answers." ], "minimumLevel": 3 } }, { "kuId": "AIR", "outcomeId": "AIR.7", "performanceId": "air-07", "observableAction": "Map an AI attack surface and construct a threat model with assets, actors, paths, controls, and residual risks.", "runtime": "source-hearing", "primaryFixtureId": "air-07-primary-2026.1", "transferFixtureId": "air-07-transfer-2026.1", "scoring": { "mode": "dual-faculty", "rubricVersion": "cae-2026-air-07-1.0", "criteria": [ "The primary artifact contains observable, reproducible evidence that the learner can map an AI attack surface and construct a threat model with assets, actors, paths, controls, and residual risks.", "The learner repeats the underlying method on the unfamiliar AIR.7 transfer fixture and explains material differences without relying on memorized answers." ], "minimumLevel": 3 } } ], "ADL": [ { "kuId": "ADL", "outcomeId": "ADL.1", "performanceId": "adl-01", "observableAction": "Craft bounded adversarial examples and measure how perturbations change model decisions.", "runtime": "adversarial-ml-range", "primaryFixtureId": "adl-01-primary-2026.1", "transferFixtureId": "adl-01-transfer-2026.1", "scoring": { "mode": "hybrid", "rubricVersion": "cae-2026-adl-01-1.0", "criteria": [ "The primary artifact contains observable, reproducible evidence that the learner can craft bounded adversarial examples and measure how perturbations change model decisions.", "The learner repeats the underlying method on the unfamiliar ADL.1 transfer fixture and explains material differences without relying on memorized answers." ], "minimumLevel": 3 } }, { "kuId": "ADL", "outcomeId": "ADL.2", "performanceId": "adl-02", "observableAction": "Identify attacker access, knowledge, capability, objective, and success conditions for an AI target.", "runtime": "adversarial-ml-range", "primaryFixtureId": "adl-02-primary-2026.1", "transferFixtureId": "adl-02-transfer-2026.1", "scoring": { "mode": "hybrid", "rubricVersion": "cae-2026-adl-02-1.0", "criteria": [ "The primary artifact contains observable, reproducible evidence that the learner can identify attacker access, knowledge, capability, objective, and success conditions for an AI target.", "The learner repeats the underlying method on the unfamiliar ADL.2 transfer fixture and explains material differences without relying on memorized answers." ], "minimumLevel": 3 } }, { "kuId": "ADL", "outcomeId": "ADL.3", "performanceId": "adl-03", "observableAction": "Measure model robustness across evasion, poisoning, extraction, inversion, and prompt-injection pressure.", "runtime": "adversarial-ml-range", "primaryFixtureId": "adl-03-primary-2026.1", "transferFixtureId": "adl-03-transfer-2026.1", "scoring": { "mode": "hybrid", "rubricVersion": "cae-2026-adl-03-1.0", "criteria": [ "The primary artifact contains observable, reproducible evidence that the learner can measure model robustness across evasion, poisoning, extraction, inversion, and prompt-injection pressure.", "The learner repeats the underlying method on the unfamiliar ADL.3 transfer fixture and explains material differences without relying on memorized answers." ], "minimumLevel": 3 } }, { "kuId": "ADL", "outcomeId": "ADL.4", "performanceId": "adl-04", "observableAction": "Apply and compare defenses and quantify robustness, utility, and operational tradeoffs.", "runtime": "adversarial-ml-range", "primaryFixtureId": "adl-04-primary-2026.1", "transferFixtureId": "adl-04-transfer-2026.1", "scoring": { "mode": "hybrid", "rubricVersion": "cae-2026-adl-04-1.0", "criteria": [ "The primary artifact contains observable, reproducible evidence that the learner can apply and compare defenses and quantify robustness, utility, and operational tradeoffs.", "The learner repeats the underlying method on the unfamiliar ADL.4 transfer fixture and explains material differences without relying on memorized answers." ], "minimumLevel": 3 } }, { "kuId": "ADL", "outcomeId": "ADL.5", "performanceId": "adl-05", "observableAction": "Analyze privacy, consent, misuse, and disclosure issues in an adversarial experiment.", "runtime": "adversarial-ml-range", "primaryFixtureId": "adl-05-primary-2026.1", "transferFixtureId": "adl-05-transfer-2026.1", "scoring": { "mode": "hybrid", "rubricVersion": "cae-2026-adl-05-1.0", "criteria": [ "The primary artifact contains observable, reproducible evidence that the learner can analyze privacy, consent, misuse, and disclosure issues in an adversarial experiment.", "The learner repeats the underlying method on the unfamiliar ADL.5 transfer fixture and explains material differences without relying on memorized answers." ], "minimumLevel": 3 } }, { "kuId": "ADL", "outcomeId": "ADL.6", "performanceId": "adl-06", "observableAction": "Model insider-risk behaviors without inferring or profiling protected characteristics.", "runtime": "adversarial-ml-range", "primaryFixtureId": "adl-06-primary-2026.1", "transferFixtureId": "adl-06-transfer-2026.1", "scoring": { "mode": "hybrid", "rubricVersion": "cae-2026-adl-06-1.0", "criteria": [ "The primary artifact contains observable, reproducible evidence that the learner can model insider-risk behaviors without inferring or profiling protected characteristics.", "The learner repeats the underlying method on the unfamiliar ADL.6 transfer fixture and explains material differences without relying on memorized answers." ], "minimumLevel": 3 } } ], "AIS": [ { "kuId": "AIS", "outcomeId": "AIS.1", "performanceId": "ais-01", "observableAction": "Conduct an authorized security evaluation using AI-enhanced and conventional tools and compare their evidence.", "runtime": "agent-range", "primaryFixtureId": "ais-01-primary-2026.1", "transferFixtureId": "ais-01-transfer-2026.1", "scoring": { "mode": "dual-faculty", "rubricVersion": "cae-2026-ais-01-1.0", "criteria": [ "The primary artifact contains observable, reproducible evidence that the learner can conduct an authorized security evaluation using AI-enhanced and conventional tools and compare their evidence.", "The learner repeats the underlying method on the unfamiliar AIS.1 transfer fixture and explains material differences without relying on memorized answers." ], "minimumLevel": 3 } }, { "kuId": "AIS", "outcomeId": "AIS.2", "performanceId": "ais-02", "observableAction": "Produce a reproducible assessment report with prioritized, actionable, evidence-linked recommendations.", "runtime": "agent-range", "primaryFixtureId": "ais-02-primary-2026.1", "transferFixtureId": "ais-02-transfer-2026.1", "scoring": { "mode": "dual-faculty", "rubricVersion": "cae-2026-ais-02-1.0", "criteria": [ "The primary artifact contains observable, reproducible evidence that the learner can produce a reproducible assessment report with prioritized, actionable, evidence-linked recommendations.", "The learner repeats the underlying method on the unfamiliar AIS.2 transfer fixture and explains material differences without relying on memorized answers." ], "minimumLevel": 3 } }, { "kuId": "AIS", "outcomeId": "AIS.3", "performanceId": "ais-03", "observableAction": "Use AI to assess vulnerability impact, reject unsupported claims, and design and verify mitigation.", "runtime": "agent-range", "primaryFixtureId": "ais-03-primary-2026.1", "transferFixtureId": "ais-03-transfer-2026.1", "scoring": { "mode": "dual-faculty", "rubricVersion": "cae-2026-ais-03-1.0", "criteria": [ "The primary artifact contains observable, reproducible evidence that the learner can use AI to assess vulnerability impact, reject unsupported claims, and design and verify mitigation.", "The learner repeats the underlying method on the unfamiliar AIS.3 transfer fixture and explains material differences without relying on memorized answers." ], "minimumLevel": 3 } }, { "kuId": "AIS", "outcomeId": "AIS.4", "performanceId": "ais-04", "observableAction": "Build a threat model from observed attack surfaces and validate mitigation against a transfer scenario.", "runtime": "agent-range", "primaryFixtureId": "ais-04-primary-2026.1", "transferFixtureId": "ais-04-transfer-2026.1", "scoring": { "mode": "dual-faculty", "rubricVersion": "cae-2026-ais-04-1.0", "criteria": [ "The primary artifact contains observable, reproducible evidence that the learner can build a threat model from observed attack surfaces and validate mitigation against a transfer scenario.", "The learner repeats the underlying method on the unfamiliar AIS.4 transfer fixture and explains material differences without relying on memorized answers." ], "minimumLevel": 3 } } ], "DFA": [ { "kuId": "DFA", "outcomeId": "DFA.1", "performanceId": "dfa-01", "observableAction": "Implement a bounded AI workflow that measurably improves detection, triage, or response while retaining human control.", "runtime": "agent-range", "primaryFixtureId": "dfa-01-primary-2026.1", "transferFixtureId": "dfa-01-transfer-2026.1", "scoring": { "mode": "hybrid", "rubricVersion": "cae-2026-dfa-01-1.0", "criteria": [ "The primary artifact contains observable, reproducible evidence that the learner can implement a bounded AI workflow that measurably improves detection, triage, or response while retaining human control.", "The learner repeats the underlying method on the unfamiliar DFA.1 transfer fixture and explains material differences without relying on memorized answers." ], "minimumLevel": 3 } }, { "kuId": "DFA", "outcomeId": "DFA.2", "performanceId": "dfa-02", "observableAction": "Develop or apply a model to identify cyber risks and verify mitigation against held-out events.", "runtime": "agent-range", "primaryFixtureId": "dfa-02-primary-2026.1", "transferFixtureId": "dfa-02-transfer-2026.1", "scoring": { "mode": "hybrid", "rubricVersion": "cae-2026-dfa-02-1.0", "criteria": [ "The primary artifact contains observable, reproducible evidence that the learner can develop or apply a model to identify cyber risks and verify mitigation against held-out events.", "The learner repeats the underlying method on the unfamiliar DFA.2 transfer fixture and explains material differences without relying on memorized answers." ], "minimumLevel": 3 } }, { "kuId": "DFA", "outcomeId": "DFA.3", "performanceId": "dfa-03", "observableAction": "Map observed adversary behavior and defensive evidence to MITRE ATLAS techniques.", "runtime": "agent-range", "primaryFixtureId": "dfa-03-primary-2026.1", "transferFixtureId": "dfa-03-transfer-2026.1", "scoring": { "mode": "hybrid", "rubricVersion": "cae-2026-dfa-03-1.0", "criteria": [ "The primary artifact contains observable, reproducible evidence that the learner can map observed adversary behavior and defensive evidence to MITRE ATLAS techniques.", "The learner repeats the underlying method on the unfamiliar DFA.3 transfer fixture and explains material differences without relying on memorized answers." ], "minimumLevel": 3 } }, { "kuId": "DFA", "outcomeId": "DFA.4", "performanceId": "dfa-04", "observableAction": "Generate and evaluate explanations for defensive model decisions, including misleading failure cases.", "runtime": "agent-range", "primaryFixtureId": "dfa-04-primary-2026.1", "transferFixtureId": "dfa-04-transfer-2026.1", "scoring": { "mode": "hybrid", "rubricVersion": "cae-2026-dfa-04-1.0", "criteria": [ "The primary artifact contains observable, reproducible evidence that the learner can generate and evaluate explanations for defensive model decisions, including misleading failure cases.", "The learner repeats the underlying method on the unfamiliar DFA.4 transfer fixture and explains material differences without relying on memorized answers." ], "minimumLevel": 3 } } ], "OFA": [ { "kuId": "OFA", "outcomeId": "OFA.1", "performanceId": "ofa-01", "observableAction": "Identify and reproduce bounded adversarial attacks on a model inside an authorized range.", "runtime": "agent-range", "primaryFixtureId": "ofa-01-primary-2026.1", "transferFixtureId": "ofa-01-transfer-2026.1", "scoring": { "mode": "dual-faculty", "rubricVersion": "cae-2026-ofa-01-1.0", "criteria": [ "The primary artifact contains observable, reproducible evidence that the learner can identify and reproduce bounded adversarial attacks on a model inside an authorized range.", "The learner repeats the underlying method on the unfamiliar OFA.1 transfer fixture and explains material differences without relying on memorized answers." ], "minimumLevel": 3 } }, { "kuId": "OFA", "outcomeId": "OFA.2", "performanceId": "ofa-02", "observableAction": "Identify AI-enabled opportunities in an authorized offensive objective and reject prohibited scope.", "runtime": "agent-range", "primaryFixtureId": "ofa-02-primary-2026.1", "transferFixtureId": "ofa-02-transfer-2026.1", "scoring": { "mode": "dual-faculty", "rubricVersion": "cae-2026-ofa-02-1.0", "criteria": [ "The primary artifact contains observable, reproducible evidence that the learner can identify AI-enabled opportunities in an authorized offensive objective and reject prohibited scope.", "The learner repeats the underlying method on the unfamiliar OFA.2 transfer fixture and explains material differences without relying on memorized answers." ], "minimumLevel": 3 } }, { "kuId": "OFA", "outcomeId": "OFA.3", "performanceId": "ofa-03", "observableAction": "Augment a tactic, technique, or procedure with AI while preserving approval and evidence boundaries.", "runtime": "agent-range", "primaryFixtureId": "ofa-03-primary-2026.1", "transferFixtureId": "ofa-03-transfer-2026.1", "scoring": { "mode": "dual-faculty", "rubricVersion": "cae-2026-ofa-03-1.0", "criteria": [ "The primary artifact contains observable, reproducible evidence that the learner can augment a tactic, technique, or procedure with AI while preserving approval and evidence boundaries.", "The learner repeats the underlying method on the unfamiliar OFA.3 transfer fixture and explains material differences without relying on memorized answers." ], "minimumLevel": 3 } }, { "kuId": "OFA", "outcomeId": "OFA.4", "performanceId": "ofa-04", "observableAction": "Measure AI-enabled attack effectiveness, reliability, detectability, cost, and transfer limitations.", "runtime": "agent-range", "primaryFixtureId": "ofa-04-primary-2026.1", "transferFixtureId": "ofa-04-transfer-2026.1", "scoring": { "mode": "dual-faculty", "rubricVersion": "cae-2026-ofa-04-1.0", "criteria": [ "The primary artifact contains observable, reproducible evidence that the learner can measure AI-enabled attack effectiveness, reliability, detectability, cost, and transfer limitations.", "The learner repeats the underlying method on the unfamiliar OFA.4 transfer fixture and explains material differences without relying on memorized answers." ], "minimumLevel": 3 } }, { "kuId": "OFA", "outcomeId": "OFA.5", "performanceId": "ofa-05", "observableAction": "Apply legal, ethical, authorization, minimization, and disclosure constraints to an offensive exercise.", "runtime": "agent-range", "primaryFixtureId": "ofa-05-primary-2026.1", "transferFixtureId": "ofa-05-transfer-2026.1", "scoring": { "mode": "dual-faculty", "rubricVersion": "cae-2026-ofa-05-1.0", "criteria": [ "The primary artifact contains observable, reproducible evidence that the learner can apply legal, ethical, authorization, minimization, and disclosure constraints to an offensive exercise.", "The learner repeats the underlying method on the unfamiliar OFA.5 transfer fixture and explains material differences without relying on memorized answers." ], "minimumLevel": 3 } }, { "kuId": "OFA", "outcomeId": "OFA.6", "performanceId": "ofa-06", "observableAction": "Map offensive AI behavior to MITRE ATT&CK or ATLAS and identify corresponding detections and mitigations.", "runtime": "agent-range", "primaryFixtureId": "ofa-06-primary-2026.1", "transferFixtureId": "ofa-06-transfer-2026.1", "scoring": { "mode": "dual-faculty", "rubricVersion": "cae-2026-ofa-06-1.0", "criteria": [ "The primary artifact contains observable, reproducible evidence that the learner can map offensive AI behavior to MITRE ATT&CK or ATLAS and identify corresponding detections and mitigations.", "The learner repeats the underlying method on the unfamiliar OFA.6 transfer fixture 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