{
  "schemaVersion": 2,
  "notice": "Illustrative mapping from the AI Governance Engineer Body of Knowledge v0.5.0 (not a claim of conformity)",
  "version": "0.5.0",
  "asOf": "2026-09-24",
  "license": "CC BY 4.0",
  "source": "https://aigovernanceengineer.com/resources/crosswalk",
  "citation": {
    "title": "AI Governance Engineering: The Thesis & Body of Knowledge",
    "authors": [
      "Jorge García Aibar"
    ],
    "parentDoi": "https://doi.org/10.5281/zenodo.22956197",
    "conceptDoi": "https://doi.org/10.5281/zenodo.22857084"
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      "name": "Singapore Model AI Governance Framework for Generative AI",
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      "issuer": "IMDA / AI Verify Foundation",
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      "name": "Council of Europe Framework Convention on AI (CETS No. 225)",
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      "issuer": "Council of Europe",
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      "id": "g7-hiroshima-coc",
      "name": "G7 Hiroshima Process International Code of Conduct for Advanced AI Systems",
      "short": "G7 Code",
      "type": "code",
      "issuer": "G7",
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      "name": "GAO AI Accountability Framework (GAO-21-519SP)",
      "short": "GAO AI Accountability",
      "type": "framework",
      "issuer": "U.S. Government Accountability Office",
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      "name": "TC260 AI Safety Governance Framework 3.0",
      "short": "TC260 Framework 3.0",
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      "issuer": "TC260 under CAC guidance (China)",
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      "name": "China Interim Measures for Generative AI Services (2023)",
      "short": "China GenAI Measures",
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      "issuer": "CAC and six other bodies (China)",
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      "id": "cn-deep-synthesis",
      "name": "China Provisions on Deep Synthesis (2023)",
      "short": "China Deep Synthesis",
      "type": "law",
      "issuer": "CAC, MIIT and MPS (China)",
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      "chipPrefix": "DeepSyn"
    },
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      "id": "cn-algo-recommendation",
      "name": "China Provisions on Algorithmic Recommendation (2022)",
      "short": "China Algo. Rec.",
      "type": "law",
      "issuer": "CAC, MIIT, MPS and SAMR (China)",
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      "chipPrefix": "AlgoRec"
    },
    {
      "id": "cn-content-labelling",
      "name": "China Measures for Labelling AI-Generated Synthetic Content (2025)",
      "short": "China AI Labelling",
      "type": "law",
      "issuer": "CAC, MIIT, MPS and NRTA (China)",
      "url": "https://www.cac.gov.cn/2025-03/14/c_1743654684782215.htm",
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      "chipPrefix": "Label"
    },
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      "id": "cn-gbt-45654",
      "name": "GB/T 45654-2025 Basic security requirements for generative AI services",
      "short": "GB/T 45654",
      "type": "standard",
      "issuer": "SAMR / SAC, drafted by TC260 (China)",
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  ],
  "topics": [
    {
      "id": "risk-management",
      "name": "Risk management",
      "summary": "Identifying, analysing and treating AI risks across the lifecycle, and keeping the treatment current as the system and its context change.",
      "layers": [
        1,
        3
      ],
      "read": [
        {
          "label": "13. Where risk management sits",
          "url": "https://aigovernanceengineer.com/bok/risk-management"
        }
      ]
    },
    {
      "id": "governance-accountability",
      "name": "Governance and accountability",
      "summary": "The policies, roles and accountability structures that put a named owner behind every AI decision and control.",
      "layers": [
        1
      ],
      "read": [
        {
          "label": "12. Running the AI governance program",
          "url": "https://aigovernanceengineer.com/bok/governance-program"
        }
      ]
    },
    {
      "id": "impact-assessment",
      "name": "Impact assessment",
      "summary": "Assessing an AI system's impact on fundamental rights, individuals and society before and during deployment.",
      "layers": [
        1,
        2
      ],
      "read": [
        {
          "label": "18. The EU AI Act in one pass: fundamental rights impact assessment",
          "url": "https://aigovernanceengineer.com/bok/eu-ai-act#fundamental-rights-impact-assessment-article-27"
        }
      ]
    },
    {
      "id": "data-governance",
      "name": "Data governance",
      "summary": "Governing the data an AI system trains on and processes: lawful sourcing, quality, lineage and protection of personal and input data.",
      "layers": [
        2,
        3
      ],
      "read": [
        {
          "label": "14. Governing AI development",
          "url": "https://aigovernanceengineer.com/bok/governing-development"
        }
      ]
    },
    {
      "id": "documentation-transparency",
      "name": "Documentation and transparency",
      "summary": "Technical documentation, disclosures and content labelling that make an AI system legible to regulators, deployers and users.",
      "layers": [
        2
      ],
      "read": []
    },
    {
      "id": "inventory-registration",
      "name": "Inventory and registration",
      "summary": "Keeping an inventory of AI systems and agents and, where required, registering or filing them with the authorities.",
      "layers": [
        2
      ],
      "read": [
        {
          "label": "11. AI, defined for governance",
          "url": "https://aigovernanceengineer.com/bok/ai-defined"
        }
      ]
    },
    {
      "id": "logging-traceability",
      "name": "Logging and traceability",
      "summary": "Automatic, tamper-evident logs and records that make an AI system's behaviour reconstructable after the fact.",
      "layers": [
        4
      ],
      "read": []
    },
    {
      "id": "human-oversight",
      "name": "Human oversight",
      "summary": "Human-in-the-loop checkpoints, approval gates and the ability to intervene in or stop an AI system.",
      "layers": [
        4
      ],
      "read": []
    },
    {
      "id": "runtime-guardrails",
      "name": "Runtime guardrails",
      "summary": "Controls that constrain an AI system while it runs: input/output filtering, tool-invocation limits, isolation and memory management.",
      "layers": [
        4
      ],
      "read": []
    },
    {
      "id": "robustness-security-evals",
      "name": "Robustness, security and evaluations",
      "summary": "Testing an AI system for accuracy, robustness, security and adversarial failure, including red-teaming and sandbox validation.",
      "layers": [
        3,
        4
      ],
      "read": []
    },
    {
      "id": "incident-monitoring",
      "name": "Incident response and monitoring",
      "summary": "Post-market monitoring, incident detection and reporting, and the complaint channels that surface real-world failures.",
      "layers": [
        5
      ],
      "read": [
        {
          "label": "17. Incidents, issues and root causes",
          "url": "https://aigovernanceengineer.com/bok/incidents"
        }
      ]
    },
    {
      "id": "supply-chain",
      "name": "Supply chain and third parties",
      "summary": "Allocating responsibility along the AI value chain and managing risks from third-party models, data, tools and technical supporters.",
      "layers": [
        2,
        5
      ],
      "read": [
        {
          "label": "18. The EU AI Act in one pass: who you are in the value chain",
          "url": "https://aigovernanceengineer.com/bok/eu-ai-act#who-you-are-in-the-value-chain"
        }
      ]
    },
    {
      "id": "prohibited-practices",
      "name": "Prohibited practices",
      "summary": "Uses of AI that a jurisdiction bans outright or a framework treats as unacceptable, and the intake controls that keep them out of the portfolio.",
      "layers": [
        1
      ],
      "read": [
        {
          "label": "18. The EU AI Act in one pass: prohibited practices",
          "url": "https://aigovernanceengineer.com/bok/eu-ai-act#prohibited-practices-article-5"
        }
      ]
    },
    {
      "id": "fairness-non-discrimination",
      "name": "Fairness and non-discrimination",
      "summary": "Detecting and correcting bias in data, models and outcomes, and the lawful handling of the sensitive data that bias testing needs.",
      "layers": [
        2,
        3
      ],
      "read": [
        {
          "label": "16. Fairness and explainability for practitioners",
          "url": "https://aigovernanceengineer.com/bok/fairness-and-explainability"
        }
      ]
    },
    {
      "id": "privacy-data-protection",
      "name": "Privacy and data protection",
      "summary": "Lawful basis, minimisation, privacy by design and the privacy attacks specific to models, wherever an AI system touches personal data.",
      "layers": [
        1,
        2,
        4
      ],
      "read": [
        {
          "label": "19. Privacy and data protection law applied to AI",
          "url": "https://aigovernanceengineer.com/bok/privacy-and-ai"
        }
      ]
    },
    {
      "id": "explainability",
      "name": "Explainability and right to explanation",
      "summary": "Explaining a model and an individual output to the people who use it or are affected by it, and the legal rights to an explanation and to contest.",
      "layers": [
        2,
        4
      ],
      "read": [
        {
          "label": "18. The EU AI Act in one pass: explanation and notice to affected people",
          "url": "https://aigovernanceengineer.com/bok/eu-ai-act#explanation-and-notice-to-affected-people"
        }
      ]
    },
    {
      "id": "ai-literacy",
      "name": "AI literacy and competence",
      "summary": "Making sure the people who build, operate, oversee and use an AI system have the knowledge their role needs, with a record that shows it.",
      "layers": [
        1
      ],
      "read": [
        {
          "label": "18. The EU AI Act in one pass: AI literacy and bias-detection data",
          "url": "https://aigovernanceengineer.com/bok/eu-ai-act#ai-literacy-and-bias-detection-data"
        }
      ]
    },
    {
      "id": "conformity-assessment",
      "name": "Conformity assessment and certification",
      "summary": "Demonstrating conformity before market entry, independent audit and certification, and the standards that carry a presumption of conformity.",
      "layers": [
        5
      ],
      "read": [
        {
          "label": "18. The EU AI Act in one pass: conformity assessment, declaration, marking and registration",
          "url": "https://aigovernanceengineer.com/bok/eu-ai-act#conformity-assessment-declaration-marking-and-registration"
        }
      ]
    },
    {
      "id": "gpai-foundation-models",
      "name": "GPAI and foundation models",
      "summary": "Duties that attach to general-purpose and foundation models themselves: documentation for downstream providers, evaluation, and systemic-risk management.",
      "layers": [
        2,
        3
      ],
      "read": [
        {
          "label": "18. The EU AI Act in one pass: general-purpose AI models",
          "url": "https://aigovernanceengineer.com/bok/eu-ai-act#general-purpose-ai-models"
        }
      ]
    },
    {
      "id": "ip-copyright",
      "name": "IP and copyright",
      "summary": "Lawful access to training content, honouring rights reservations, and keeping outputs from reproducing protected works.",
      "layers": [
        2
      ],
      "read": [
        {
          "label": "20. Other law that already applies to AI",
          "url": "https://aigovernanceengineer.com/bok/existing-law"
        }
      ]
    },
    {
      "id": "agent-identity-autonomy",
      "name": "Agent identity and autonomy",
      "summary": "Giving each agent its own identity and scoped permissions, bounding what it may do on its own, and keeping a human able to stop it.",
      "layers": [
        2,
        4
      ],
      "read": [
        {
          "label": "05. Patterns: Agent Identity & Scoped Credentials",
          "url": "https://aigovernanceengineer.com/bok/patterns#pattern-agent-identity--scoped-credentials"
        }
      ]
    },
    {
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      "topic": "runtime-guardrails",
      "framework": "cn-genai-measures",
      "frameworkShort": "China GenAI Measures",
      "column": "cn",
      "reference": "Art. 14",
      "clauseId": "art-14",
      "label": "GenAI Art. 14",
      "title": "Stop unlawful generation",
      "strength": "core",
      "verified": true,
      "note": "Stop generation and transmission of unlawful content; CAC text.",
      "url": "https://www.cac.gov.cn/2023-07/13/c_1690898327029107.htm",
      "obligation": "Interim Measures for Generative AI Services (in force 2023-08-15)",
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    },
    {
      "topic": "runtime-guardrails",
      "framework": "cn-deep-synthesis",
      "frameworkShort": "China Deep Synthesis",
      "column": "cn",
      "reference": "Art. 10",
      "clauseId": "art-10",
      "label": "DeepSyn Art. 10",
      "title": "Input and output review",
      "strength": "core",
      "verified": true,
      "note": "Technical or manual review of user inputs and synthetic outputs; CAC text.",
      "url": "https://www.cac.gov.cn/2022-12/11/c_1672221949354811.htm",
      "obligation": "Provisions on Deep Synthesis (in force 2023-01-10)",
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    {
      "topic": "runtime-guardrails",
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      "frameworkShort": "China Algo. Rec.",
      "column": "cn",
      "reference": "Art. 8",
      "clauseId": "art-8",
      "label": "AlgoRec Art. 8",
      "title": "Periodic algorithm review",
      "strength": "related",
      "verified": true,
      "note": "Regularly review the algorithm mechanisms, models and data; CAC text.",
      "url": "https://www.cac.gov.cn/2022-01/04/c_1642894606364259.htm",
      "obligation": "Provisions on Algorithmic Recommendation (in force 2022-03-01)",
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      "topic": "runtime-guardrails",
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      "frameworkShort": "China Algo. Rec.",
      "column": "cn",
      "reference": "Art. 9",
      "clauseId": "art-9",
      "label": "AlgoRec Art. 9",
      "title": "Feature database for unlawful content",
      "strength": "related",
      "verified": true,
      "note": "Maintain a feature library to identify unlawful and harmful information; CAC text.",
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      "clauseId": "art-15",
      "label": "Art. 15",
      "title": "Accuracy, robustness and cybersecurity",
      "strength": "core",
      "verified": true,
      "note": "See pattern: /bok/patterns#pattern-adversarial-red-team-suite",
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      "note": "Real-world testing plan and controls.",
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      "verified": true,
      "note": "Technical measures across model, algorithm and data; printed p. 73.",
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      "note": "Sandbox validation, red teaming and auditing for agents; printed pp. 124-125.",
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      "column": "cn",
      "reference": "5.3.14",
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      "label": "TC260 5.3.14",
      "title": "Resilience",
      "strength": "related",
      "verified": true,
      "note": "System resilience requirement; printed p. 103.",
      "url": "https://www.cac.gov.cn/rootimages/uploadimg/1791137114683961/1791137114683961.pdf",
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    {
      "topic": "robustness-security-evals",
      "framework": "cn-deep-synthesis",
      "frameworkShort": "China Deep Synthesis",
      "column": "cn",
      "reference": "Art. 15",
      "clauseId": "art-15",
      "label": "DeepSyn Art. 15",
      "title": "Technology management and algorithm verification",
      "strength": "core",
      "verified": true,
      "note": "Regular audit, assessment and verification of the synthesis-algorithm mechanisms; CAC text.",
      "url": "https://www.cac.gov.cn/2022-12/11/c_1672221949354811.htm",
      "obligation": "Provisions on Deep Synthesis (in force 2023-01-10)",
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    {
      "topic": "robustness-security-evals",
      "framework": "cn-deep-synthesis",
      "frameworkShort": "China Deep Synthesis",
      "column": "cn",
      "reference": "Art. 20",
      "clauseId": "art-20",
      "label": "DeepSyn Art. 20",
      "title": "Security assessment of new products",
      "strength": "core",
      "verified": true,
      "note": "Security assessment before launching public-opinion products; CAC text.",
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      "topic": "robustness-security-evals",
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      "frameworkShort": "China GenAI Measures",
      "column": "cn",
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      "verified": true,
      "note": "Pre-deployment security assessment; CAC text.",
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      "label": "GB/T 45654 Security assessment",
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      "verified": false,
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      "verified": true,
      "note": "Continuous post-market monitoring.",
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      "note": "See pattern: /bok/patterns#pattern-incident-pipeline",
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      "note": "Incident communication to interested parties.",
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      "topic": "incident-monitoring",
      "framework": "nist-ai-rmf",
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      "title": "MANAGE 4: Risk treatments, including response and recovery, and communication plans for the identified and measured AI risks are documented and monitored",
      "strength": "core",
      "verified": true,
      "note": "See pattern: /bok/patterns#pattern-continuous-assurance-telemetry",
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      "note": "Real-time monitoring of AI risks in operation; printed p. 102.",
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