CSA AICM v1.1: 247 control objectives across 18 domains
247 control objectives across 18 domains, spanning governance, data, model and runtime
AIGE-OBL-CSA-AICM. Drawn from chapter 08.
Text alternative
- Clause: CSA AICM, AICM v1.1.
- Duty holder: Not stated.
- Applies from: No date, Voluntary.
- Artefact: Control catalogue mapped….
- Layers: Layer 01, Layer 03, Layer 05.
- Evidence record: Evidence record v1.
- Record schema: Evidence record.
- The same topic in 29 other frameworks; the crosswalk section below links each clause.
- Id
AIGE-OBL-CSA-AICM- Instrument
- CSA AI Controls Matrix (AICM) v1.1 controls
- Clause
- AICM v1.1
- Applies from
- No date Voluntary · v1.1 published 2026-06-22
The artefact that evidences it
Control catalogue mapped to policy-as-code and evals; crosswalk to ISO 42001 / NIST AI RMF.
Data table: every relation
| Family | Item | Relation |
|---|---|---|
| Patterns | AIBOM | Direct |
| Patterns | Agent Identity & Scoped Credentials | Direct |
| Patterns | Agent Registry | Direct |
| Patterns | Continuous Assurance Telemetry | Direct |
| Patterns | Framework Crosswalk | Direct |
| Patterns | Kill Switch / Circuit Breaker | Direct |
| Patterns | Policy Card | Direct |
| Patterns | Shadow-AI Discovery | Direct |
| Open controls | AIGE-CTL-ASSURE-012 AI Bill of Materials per Build | Direct |
| Open controls | AIGE-CTL-ASSURE-005 Live Control Status from the Assurance Store | Direct |
| Same crosswalk topic | AI Verify 4.3.1, 4.7.1, 4.2.1, 9.1.1, 9.5.2, 11.2.1, 8.1.1, 8.2.1, 1.1.6, 1.7.2, 1.1.1, 9.12.1, 5.5.1, 3.4.1, 9.8.1, 5.10.1, 10.3.1, 10.3.2, 9.4.1, 4.8.1, 4.1.1, 6.1.1, 5.2.1, 5.9.1, 9.5.4, 1.5.1, 1.6.1, 5.11.1, 9.7.1, 5.3.1, 4.5.2, 9.3.1, 7.1.1, 7.2.1, 7.8.1, 8.3.1, 8.3.2, 1.4.1, 2.2.1, 1.1.4, 9.5.3, 5.1.1, 8.4.1, 8.5.1, 4.9.1, 4.9.2, 1.1.3, 5.14.1, 6.5.1, 4.6.1 | Core clause |
| Same crosswalk topic | China AI Labelling Label Art. 4, Label Art. 5 | Core clause |
| Same crosswalk topic | China Algo. Rec. AlgoRec Art. 27, AlgoRec Art. 7, AlgoRec Art. 16, AlgoRec Art. 24, AlgoRec Art. 17, AlgoRec Art. 8, AlgoRec Art. 9, AlgoRec Art. 21 | Core clause |
| Same crosswalk topic | China Deep Synthesis DeepSyn Art. 7, DeepSyn Art. 14, DeepSyn Art. 16, DeepSyn Art. 17, DeepSyn Art. 19, DeepSyn Art. 10, DeepSyn Art. 15, DeepSyn Art. 20 | Core clause |
| Same crosswalk topic | China GenAI Measures GenAI Art. 17, GenAI Art. 9, GenAI Art. 7, GenAI Art. 8, GenAI Art. 11, GenAI Art. 12, GenAI Art. 19, GenAI Art. 10, GenAI Art. 14, GenAI Art. 15, GenAI Art. 4, GenAI Art. 4(2), GenAI Art. 7(3), GenAI Art. 7(2), GenAI Art. 4(3) | Core clause |
| Same crosswalk topic | CoE Convention CoE Art. 16, CoE Art. 9, CoE Art. 14(2), CoE Art. 15(2), CoE Art. 8, CoE Art. 16(2)(g), CoE Art. 16(4), CoE Art. 10, CoE Art. 11, CoE Art. 13 | Core clause |
| Same crosswalk topic | EU AI Act Art. 9, Art. 3, Art. 17, Art. 4, Art. 87, Art. 27, Art. 10, Art. 10(2)(f)–(g), Art. 4a, Art. 53, Art. 53(1)(c), Art. 5(1)(e), Art. 11, Art. 13, Art. 50, Art. 86, Art. 18, Art. 43, Art. 53(1)(d), Art. 50(2), 50(4), Art. 49, Art. 71, Art. 6, Art. 3(1), Art. 52, Art. 12, Art. 26(6), Art. 26, Art. 19, Art. 14, Art. 14(4)(b), Art. 5, Art. 15, Art. 5(1)(a)–(b), Art. 55, Art. 60, Art. 15(3), Art. 42(3), Art. 72, Art. 73, Art. 26(5), Art. 3(49), Art. 20, Art. 25, Art. 25(4), Art. 22, Art. 23, Art. 24, Art. 54, Art. 59, Art. 13(3)(b)(iv)–(v), Art. 26(2), Art. 95(2)(c), Art. 47, Art. 48, Art. 40, Art. 51, Art. 56, Art. 50(2), Art. 50(4), Art. 3(60), Art. 57, Art. 58, Art. 61, Art. 43(4), Art. 79 | Core clause |
| Same crosswalk topic | G7 Code G7 Action 5, G7 Action 3, G7 Action 1, G7 Action 2, G7 Action 4, G7 Action 11, G7 Action 7 | Core clause |
| Same crosswalk topic | GAO AI Accountability 1.6, 1.2, 1.1, 1.3, 1.5, 2.1, 2.2, 2.4, 2.5, 1.9, 3.5, 1.7, 3.1, 4.3, 3.9, 3.7, 3.2, 4.1, 4.2, 2.6, 2.7, 3.8, 2.8, 1.4, 1.8, 4.4, 4.5 | Core clause |
| Same crosswalk topic | GB/T 45654 GB/T 45654 Corpus security, GB/T 45654 Content labelling, GB/T 45654 Security assessment | Core clause |
| Same crosswalk topic | GDPR Art. 5(2), Art. 35, Art. 36, Art. 5(1)(c), Art. 25, Art. 9, Arts. 13–14, Art. 30, Art. 22, Art. 32, Arts. 33–34, Art. 28, Arts. 44–46, Art. 5(1)(a), Art. 5, Art. 6, Art. 15(1)(h), Art. 13(2)(f), Art. 22(3), Art. 39(1)(b), Art. 42 | Core clause |
| Same crosswalk topic | GPAI Code Safety C1, Safety C3, Safety C8, Copyright 1.1–1.5, Transparency 1.1, Transparency 1.2, Safety C5, Safety 3.2, Safety C6, Safety C9, Safety 3.5, Safety C7, Copyright 1.1, Copyright 1.2, Copyright 1.3, Copyright 1.4, Copyright 1.5 | Core clause |
| Same crosswalk topic | ISO 23894 23894 6.4, 23894 6.5, 23894 6.6 | Core clause |
| Same crosswalk topic | ISO 42001 6.1.2, 6.1.3, 8.2, 8.3, A.6, 6.1.4, 5.1, 5.2, 5.3, A.2, A.3, 9.3, 7.2, 9.2, 10.1, 8.4, A.5, A.7, A.7.3, A.4, 7.5, A.8, A.6.2.8, A.9, 9.1, 10.2, A.10, A.9.4, A.5.4, A.8.2, 7.3, A.6.2.4, A.6.2.5, A.6.2.6 | Core clause |
| Same crosswalk topic | ISO 42005 42005 5.8, 42005 6.8, 42005 5.12 | Core clause |
| Same crosswalk topic | ISO 42006 ISO/IEC 42006 | Core clause |
| Same crosswalk topic | Korea AI Act Art. 34(1)(1), Art. 36, Art. 35, Art. 31, Art. 34(1)(2), Art. 33, Art. 34(1)(5), Art. 34(1)(4), Art. 32(1), Art. 32 | Core clause |
| Same crosswalk topic | NIST AI RMF MAP 1, MAP 5, MANAGE 1, GOVERN 1.3, MAP 1.5, MANAGE 1.3, MANAGE 1.4, MEASURE 3, MEASURE 2, GOVERN 1, GOVERN 2, GOVERN 4, GOVERN 5, MAP 3, MAP 2, MEASURE 2.10, MEASURE 2.11, MEASURE 2.8, MAP 1.6, MEASURE 2.9, GOVERN 1.6, GOVERN 1.7, MANAGE 4, MANAGE 2.4, MAP 3.5, GOVERN 3.2, MANAGE 2, MEASURE 2.7, MEASURE 2.1, MEASURE 1, MANAGE 4.3, GOVERN 4.3, GOVERN 6, MAP 4, MANAGE 3, MANAGE 3.1, GOVERN 6.2, GOVERN 1.1, GOVERN 3.1, GOVERN 2.2, MAP 3.4, MEASURE 1.3, GOVERN 6.1, MAP 4.1, MEASURE 2.3, MANAGE 4.1 | Core clause |
| Same crosswalk topic | OECD AI Principles OECD 1.5(c), OECD 1.5, OECD 1.3, OECD 1.5(b), OECD 1.2(b), OECD 1.4, OECD 1.2 | Core clause |
| Same crosswalk topic | OWASP Agentic ASI09, ASI05, ASI04, ASI03, ASI02, ASI07, ASI10 | Core clause |
| Same crosswalk topic | OWASP LLM LLM05:2026, LLM01:2026, LLM10:2026, LLM06:2026, LLM04:2026, LLM02:2026, LLM03:2026, LLM07:2026 | Core clause |
| Same crosswalk topic | Singapore Agentic Agentic 2.1, Agentic 2.2.1, Agentic 2.3.3, Agentic 2.2.2, Agentic 2.3.1, Agentic 2.3.2, Agentic 2.1.1, Agentic 2.4, Agentic 2.1.2 | Core clause |
| Same crosswalk topic | Singapore GenAI GenAI 1, GenAI 2, GenAI 3, GenAI 5, GenAI 6, GenAI 4, GenAI 9, GenAI 8, GenAI 7 | Core clause |
| Same crosswalk topic | TC260 Framework 3.0 TC260 2, TC260 Summary table, TC260 5.3.19, TC260 4, TC260 5.3.12, TC260 Appendix 1, TC260 2.2, TC260 2.1.3, TC260 5.1, TC260 App. 2 II.2, TC260 4.4.1, TC260 App. 2 II.6, TC260 5.3.6, TC260 App. 2 II.3, TC260 App. 2 II.5, TC260 3.2.1, TC260 3, TC260 5.3.14, TC260 5.3.7, TC260 5.3.18, TC260 App. 2 II.4, TC260 4.4.4, TC260 5.3 | Core clause |
| Same crosswalk topic | UK ATRS ATRS 2.5.2, ATRS 2.1, ATRS 2.5.1, ATRS 2.4.3, ATRS Tier 1, ATRS 2.2, ATRS 2.3.2, ATRS 2.1.4, ATRS 2.4.2, ATRS 2.3.5, ATRS 2.3.4 | Core clause |
| Same crosswalk topic | UK DUAA UK GDPR Art. 22C, UK GDPR Art. 22B | Core clause |
| Same crosswalk topic | prEN 18228 prEN 18228 | Core clause |
| Same crosswalk topic | prEN 18229-1 prEN 18229-1 | Core clause |
| Same crosswalk topic | EN 18286 EN 18286 | Related clause |
Patterns that build it
- Policy Card (layer 1)
- Agent Registry (layer 2)
- AIBOM (layer 2)
- Continuous Assurance Telemetry (layer 5)
- Kill Switch / Circuit Breaker (layer 4)
- Framework Crosswalk (layer 1 and 5)
- Agent Identity & Scoped Credentials (layer 4)
- Shadow-AI Discovery (layer 2)
The same topic in other frameworks
From the topic crosswalk: the clauses filed under the same topics as this one. Mappings are illustrative, not a claim of conformity.
Risk management
- EU AI Act Art. 9 Risk management system (core)
- ISO 42001 6.1.2 AI risk assessment (core)
- ISO 42001 6.1.3 AI risk treatment (core)
- ISO 42001 8.2 AI risk assessment (operation) (core)
- ISO 42001 8.3 AI risk treatment (operation) (core)
- NIST AI RMF MAP 1 MAP 1: Context is established and understood (core)
- NIST AI RMF MAP 5 MAP 5: Impacts to individuals, groups, communities, organizations, and society are characterized (core)
- NIST AI RMF MANAGE 1 MANAGE 1: AI risks based on assessments and other analytical output are prioritized, responded to, and managed (core)
- TC260 Framework 3.0 TC260 2 Classification of AI safety risks (core)
- TC260 Framework 3.0 TC260 Summary table Risks × technological × governance measures (core)
- ISO 23894 23894 6.4 Risk assessment (core) (clause not verified)
- ISO 23894 23894 6.5 Risk treatment (core) (clause not verified)
- NIST AI RMF GOVERN 1.3 GOVERN 1.3: Processes, procedures, and practices are in place to determine the needed level of risk management activities based on the organization's risk tolerance (core)
- NIST AI RMF MAP 1.5 MAP 1.5: Organizational risk tolerances are determined and documented (core)
- NIST AI RMF MANAGE 1.3 MANAGE 1.3: Responses to the AI risks deemed high priority, as identified by the MAP function, are developed, planned, and documented (core)
- NIST AI RMF MANAGE 1.4 MANAGE 1.4: Negative residual risks to both downstream acquirers of AI systems and end users are documented (core)
- NIST AI RMF MEASURE 3 MEASURE 3: Mechanisms for tracking identified AI risks over time are in place (core)
- Korea AI Act Art. 34(1)(1) Risk management plan for high-impact AI (core)
- UK ATRS ATRS 2.5.2 Risks and mitigations (core)
- Singapore Agentic Agentic 2.1 Assess and bound the risks upfront (core)
- CoE Convention CoE Art. 16 Risk and impact management framework (core)
- prEN 18228 prEN 18228 AI risk management (draft; supports Art. 9) (core) (clause not verified)
- GAO AI Accountability 1.6 Risk management: implement an AI-specific risk management plan to systematically identify, analyze, and mitigate risks (core)
- AI Verify 4.3.1 Safety: assess risks, risk metrics and risk levels of the AI system in each specific use case (core)
- AI Verify 4.7.1 Safety: identify and track residual risk that cannot be measured or mitigated, and assess the organisation's tolerance for it (core)
- ISO 42001 A.6 AI system life cycle
- NIST AI RMF MEASURE 2 MEASURE 2: AI systems are evaluated for trustworthy characteristics
- TC260 Framework 3.0 TC260 5.3.19 Re-assessment on material change
- China GenAI Measures GenAI Art. 17 Security assessment and algorithm filing
- China Algo. Rec. AlgoRec Art. 27 Security assessment
- EU AI Act Art. 3 Definitions
- ISO 42001 6.1.4 AI system impact assessment
- ISO 23894 23894 6.6 Monitoring and review (clause not verified)
- GPAI Code Safety C1 Commitment 1: Safety and Security Framework
- GPAI Code Safety C3 Commitment 3: Systemic risk analysis
- OECD AI Principles OECD 1.5(c) Systematic risk management at each lifecycle phase
- AI Verify 4.2.1 Safety: carry out an assessment of materiality on key stakeholders, highlighting the risks of the proposed AI solution
Governance and accountability
- EU AI Act Art. 17 Quality management system (core)
- ISO 42001 5.1 Leadership and commitment (core)
- ISO 42001 5.2 AI policy (core)
- ISO 42001 5.3 Roles, responsibilities and authorities (core)
- ISO 42001 A.2 Policies related to AI (core)
- ISO 42001 A.3 Internal organization (core)
- NIST AI RMF GOVERN 1 GOVERN 1: Policies, processes, procedures, and practices across the organization related to the mapping, measuring, and managing of AI risks are in place, transparent, and implemented effectively (core)
- NIST AI RMF GOVERN 2 GOVERN 2: Accountability structures are in place so that the appropriate teams and individuals are empowered, responsible, and trained (core)
- TC260 Framework 3.0 TC260 4 Comprehensive governance measures (core)
- TC260 Framework 3.0 TC260 5.3.12 Traceable chain of responsibility (core)
- China GenAI Measures GenAI Art. 9 Provider responsibility as content producer (core)
- China Algo. Rec. AlgoRec Art. 7 Algorithm-security responsibility system (core)
- China Deep Synthesis DeepSyn Art. 7 Information-security responsibility system (core)
- GDPR Art. 5(2) Accountability (core)
- ISO 42001 9.3 Management review (core) (clause not verified)
- UK ATRS ATRS 2.1 Owner and responsibility (core)
- Singapore GenAI GenAI 1 Accountability (core)
- Singapore Agentic Agentic 2.2.1 Clear allocation of responsibilities within and outside the organisation (core)
- OECD AI Principles OECD 1.5 Accountability (core)
- GAO AI Accountability 1.2 Roles and responsibilities: define clear roles, responsibilities, and delegation of authority for the AI system (core)
- AI Verify 9.1.1 Accountability: establish policies on the development and use of AI aligned with other organisational policies (core)
- AI Verify 9.5.2 Accountability: clear internal governance mechanisms and roles, including a cross-departmental AI governance committee (core)
- EU AI Act Art. 4 AI literacy
- EU AI Act Art. 87 Reporting of infringements and protection of reporting persons
- ISO 42001 7.2 Competence (clause not verified)
- ISO 42001 9.2 Internal audit (clause not verified)
- ISO 42001 10.1 Continual improvement (clause not verified)
- NIST AI RMF GOVERN 4 GOVERN 4: Organizational teams are committed to a culture that considers and communicates AI risk
- NIST AI RMF GOVERN 5 GOVERN 5: Processes are in place for robust engagement with relevant AI actors
- GPAI Code Safety C8 Commitment 8: Systemic risk responsibility allocation
- Korea AI Act Art. 36 Domestic representative
- CoE Convention CoE Art. 9 Accountability and responsibility
- G7 Code G7 Action 5 Develop, implement and disclose AI governance and risk-management policies
- EN 18286 EN 18286 Quality management system for EU AI Act regulatory purposes
- GAO AI Accountability 1.1 Clear goals: define clear goals and objectives for the AI system
- GAO AI Accountability 1.3 Values: demonstrate a commitment to values and principles established by the entity
Impact assessment
- EU AI Act Art. 27 Fundamental rights impact assessment for high-risk AI systems (core)
- ISO 42001 6.1.4 AI system impact assessment (core)
- ISO 42001 8.4 AI system impact assessment (operation) (core)
- ISO 42001 A.5 Assessing impacts of AI systems (core)
- NIST AI RMF MAP 3 MAP 3: AI capabilities, targeted usage, goals, and expected benefits and costs are understood (core)
- NIST AI RMF MAP 5 MAP 5: Impacts to individuals, groups, communities, organizations, and society are characterized (core)
- TC260 Framework 3.0 TC260 Appendix 1 Grading principles (core)
- China GenAI Measures GenAI Art. 17 Security assessment (core)
- GDPR Art. 35 Data protection impact assessment (core)
- ISO 42005 42005 5.8 Performing the AI system impact assessment (core) (clause not verified)
- ISO 42005 42005 6.8 Actual and reasonably foreseeable impacts (core) (clause not verified)
- Korea AI Act Art. 35 Impact assessment (best-effort duty) (core)
- UK ATRS ATRS 2.5.1 Impact assessments (core)
- AI Verify 11.2.1 Inclusive growth, societal and environmental well-being: assess the impact of the AI system on individuals and society (core)
- EU AI Act Art. 9 Risk management system
- TC260 Framework 3.0 TC260 2.2 Safety risks in the application of AI
- GDPR Art. 36 Prior consultation
- ISO 42005 42005 5.12 Monitoring and review (clause not verified)
- CoE Convention CoE Art. 16 Risk and impact management framework
- GAO AI Accountability 1.5 Stakeholder involvement: include diverse perspectives from a community of stakeholders throughout the AI life cycle
- AI Verify 4.2.1 Safety: carry out an assessment of materiality on key stakeholders
Data governance
- EU AI Act Art. 10 Data and data governance (core)
- ISO 42001 A.7 Data for AI systems (core)
- TC260 Framework 3.0 TC260 2.1.3 Data safety risks (core)
- China GenAI Measures GenAI Art. 7 Training-data lawful sourcing (core)
- China GenAI Measures GenAI Art. 8 Data-annotation standards (core)
- China GenAI Measures GenAI Art. 11 Protection of user input and records (core)
- China Deep Synthesis DeepSyn Art. 14 Training-data management (core)
- GB/T 45654 GB/T 45654 Corpus security Training-corpus (data) security requirements (core) (clause not verified)
- EU AI Act Art. 10(2)(f)–(g) Examination for possible biases; measures to detect, prevent and mitigate them (core)
- GDPR Art. 5(1)(c) Data minimisation (core)
- GDPR Art. 25 Data protection by design and by default (core)
- ISO 42001 A.7.3 Acquisition of data (core) (clause not verified)
- UK ATRS ATRS 2.4.3 Development data specification (core)
- Singapore GenAI GenAI 2 Data (core)
- GAO AI Accountability 2.1 Sources: document sources and origins of data used to develop the models (core)
- GAO AI Accountability 2.2 Reliability: assess reliability of data used to develop the models (core)
- AI Verify 8.1.1 Data Governance: put in place measures to ensure data quality over time (core)
- AI Verify 8.2.1 Data Governance: understand the lineage of data (origin, collection, curation, movement) through a data provenance record (core)
- EU AI Act Art. 4a Special-category data for bias detection
- ISO 42001 A.4 Resources for AI systems
- NIST AI RMF MAP 2 MAP 2: Categorization of the AI system is performed
- NIST AI RMF MEASURE 2.10 MEASURE 2.10: Privacy risk of the AI system is examined and documented
- NIST AI RMF MEASURE 2.11 MEASURE 2.11: Fairness and bias are evaluated and results are documented
- TC260 Framework 3.0 TC260 5.1 Model R&D safety guidelines
- EU AI Act Art. 53 Obligations for providers of general-purpose AI models
- EU AI Act Art. 53(1)(c) Copyright policy, including rights reservations
- EU AI Act Art. 5(1)(e) Prohibited: untargeted scraping of facial images
- GPAI Code Copyright 1.1–1.5 Commitment 1: Copyright policy (Measures 1.1 to 1.5)
- GDPR Art. 9 Processing of special categories of personal data
- OWASP LLM LLM05:2026 Data and Model Poisoning
- GAO AI Accountability 2.4 Variable selection: assess data variables used in the AI component models
- GAO AI Accountability 2.5 Enhancement: assess the use of synthetic, imputed, and/or augmented data
Documentation and transparency
- EU AI Act Art. 11 Technical documentation (core)
- EU AI Act Art. 13 Transparency and provision of information to deployers (core)
- EU AI Act Art. 53 Obligations for providers of general-purpose AI models (core)
- ISO 42001 7.5 Documented information (core)
- ISO 42001 A.6 AI system life cycle (core)
- ISO 42001 A.8 Information for interested parties (core)
- China AI Labelling Label Art. 4 Explicit labels for generated content (core)
- China AI Labelling Label Art. 5 Implicit (metadata) labels (core)
- China GenAI Measures GenAI Art. 12 Labelling of generated content (core)
- China Deep Synthesis DeepSyn Art. 16 Implicit technical labels (core)
- China Deep Synthesis DeepSyn Art. 17 Conspicuous labels for confusable content (core)
- GPAI Code Transparency 1.1 Drawing up and keeping up-to-date model documentation (core)
- GDPR Arts. 13–14 Information to be provided to the data subject (core)
- Korea AI Act Art. 31 Transparency: prior notice, output labelling, realistic synthetic content (core)
- UK ATRS ATRS Tier 1 Summary information (core)
- Singapore GenAI GenAI 3 Trusted Development and Deployment (core)
- CoE Convention CoE Art. 14(2) Documentation sufficient to contest decisions; complaint to authorities (core)
- OECD AI Principles OECD 1.3 Transparency and explainability (core)
- G7 Code G7 Action 3 Publicly report capabilities, limitations and domains of use (core)
- GAO AI Accountability 1.9 Transparency: enable external stakeholders to access information on the design, operation, and limitations of the AI system (core)
- GAO AI Accountability 3.5 Documentation: document the methods for assessment, performance metrics, and outcomes of the AI system (core)
- AI Verify 1.1.6 Transparency: publicly report accuracy, intended use cases, limitations, risk assessment and mitigations to stakeholders (core)
- AI Verify 1.7.2 Transparency: make model or system cards publicly available where warranted by public interest (core)
- EU AI Act Art. 50 Transparency obligations for providers and deployers of certain AI systems
- NIST AI RMF MAP 1 MAP 1: Context is established and understood
- NIST AI RMF MEASURE 2.8 MEASURE 2.8: Risks associated with transparency and accountability are examined and documented
- China GenAI Measures GenAI Art. 19 Disclosure to regulators
- China Algo. Rec. AlgoRec Art. 16 Notice that recommendation is used
- GB/T 45654 GB/T 45654 Content labelling Generated-content labelling requirements (clause not verified)
- EU AI Act Art. 86 Right to explanation of individual decision-making
- EU AI Act Art. 18 Documentation keeping
- EU AI Act Art. 43 Conformity assessment
- EU AI Act Art. 53(1)(d) Public summary of the content used for training
- EU AI Act Art. 50(2), 50(4) Machine-readable marking of synthetic content; disclosure of deep fakes
- GPAI Code Transparency 1.2 Providing relevant information
- GDPR Art. 30 Records of processing activities
- NIST AI RMF MAP 1.6 MAP 1.6: System requirements are elicited from and understood by relevant AI actors. Design decisions take socio-technical implications into account to address AI risks
- NIST AI RMF MEASURE 2.9 MEASURE 2.9: The AI model is explained, validated, and documented, and AI system output is interpreted within its context as identified in the MAP function to inform responsible use and governance
- Korea AI Act Art. 34(1)(2) Explanation plan: result, main criteria, training-data overview
- UK ATRS ATRS 2.2 Description and rationale
- CoE Convention CoE Art. 15(2) Notification of interaction with an AI system
- GAO AI Accountability 1.7 Specifications: establish and document technical specifications
- AI Verify 1.1.1 Transparency: design an in-house policy on communication to consumers that articulates the principles for transparency
Inventory and registration
- EU AI Act Art. 49 Registration (core)
- EU AI Act Art. 71 EU database for high-risk AI systems (core)
- ISO 42001 A.4 Resources for AI systems (core)
- NIST AI RMF GOVERN 1.6 GOVERN 1.6: Mechanisms are in place to inventory AI systems and are resourced according to organizational risk priorities (core)
- China Algo. Rec. AlgoRec Art. 24 Algorithm filing (core)
- China Deep Synthesis DeepSyn Art. 19 Filing for public-opinion services (core)
- China GenAI Measures GenAI Art. 17 Algorithm filing (core)
- UK ATRS ATRS Tier 1 Summary information (the published record) (core)
- AI Verify 9.12.1 Accountability: inventory AI systems and their data, tooling, computing and human resources according to risk priorities (core)
- EU AI Act Art. 6 Classification rules for high-risk AI systems
- TC260 Framework 3.0 TC260 App. 2 II.2 Identity and access management
- TC260 Framework 3.0 TC260 4.4.1 CII registration and filing
- EU AI Act Art. 3(1) Definition of an AI system
- EU AI Act Art. 52 Procedure
- NIST AI RMF GOVERN 1.7 GOVERN 1.7: Processes and procedures are in place for decommissioning and phasing out AI systems safely and in a manner that does not increase risks or decrease the organization's trustworthiness
- Korea AI Act Art. 33 Confirmation of high-impact AI
- GAO AI Accountability 3.1 Documentation: catalog model and non-model components, along with operating specifications and parameters
- AI Verify 5.5.1 Security: identify, track and protect AI-related assets (models, data, prompts, logs, assessments)
Logging and traceability
- EU AI Act Art. 12 Record-keeping (core)
- ISO 42001 A.6 AI system life cycle (core)
- TC260 Framework 3.0 TC260 App. 2 II.6 Continuous monitoring and auditing (core)
- TC260 Framework 3.0 TC260 5.3.6 Logs kept and audited (core)
- EU AI Act Art. 26(6) Deployers keep the automatically generated logs (core)
- ISO 42001 A.6.2.8 AI system recording of event logs (core) (clause not verified)
- Korea AI Act Art. 34(1)(5) Documents showing the measures taken (core)
- OECD AI Principles OECD 1.5(b) Traceability of datasets, processes and decisions (core)
- prEN 18229-1 prEN 18229-1 AI trustworthiness framework, Part 1: logging (draft; supports Art. 12) (core) (clause not verified)
- GAO AI Accountability 4.3 Traceability: document results of monitoring activities and any corrective actions taken (core)
- AI Verify 3.4.1 Reproducibility: log all inputs and inference outputs of the model and keep them for a reasonable time for review (core)
- AI Verify 9.8.1 Accountability: process or governance-by-design technology for auditability: versioned code, models and data, and an audit trail of deployments and inputs/outputs (core)
- EU AI Act Art. 26 Obligations of deployers of high-risk AI systems
- NIST AI RMF MANAGE 4 MANAGE 4: Risk treatments, including response and recovery, and communication plans for the identified and measured AI risks are documented and monitored
- NIST AI RMF MEASURE 3 MEASURE 3: Mechanisms for tracking identified AI risks over time are in place
- China AI Labelling Label Art. 5 Implicit metadata labels
- EU AI Act Art. 19 Automatically generated logs
- Singapore Agentic Agentic 2.3.3 When deploying, continuously monitor and test
- AI Verify 5.10.1 Security: monitor and log inputs to the AI system (queries, prompts, requests)
Human oversight
- EU AI Act Art. 14 Human oversight (core)
- ISO 42001 A.9 Use of AI systems (core)
- NIST AI RMF MANAGE 2.4 MANAGE 2.4: Mechanisms are in place and applied, and responsibilities are assigned and understood, to supersede, disengage, or deactivate AI systems that demonstrate performance or outcomes inconsistent with intended use (core)
- TC260 Framework 3.0 TC260 App. 2 II.3 Strengthen human approval (core)
- GDPR Art. 22 Automated individual decision-making, including profiling (core)
- NIST AI RMF MAP 3.5 MAP 3.5: Processes for human oversight are defined, assessed, and documented in accordance with organizational policies from the GOVERN function (core)
- Korea AI Act Art. 34(1)(4) Human management and supervision (core)
- UK DUAA UK GDPR Art. 22C Safeguards for automated decision-making (core)
- UK ATRS ATRS 2.3.2 Human review (core)
- Singapore Agentic Agentic 2.2.2 Design for meaningful human oversight (core)
- GAO AI Accountability 3.9 Human supervision: define and develop procedures for human supervision of the AI system (core)
- AI Verify 10.3.1 Human Agency and Oversight: define the role of the human in oversight and control of the AI system (in, over or out of the loop) (core)
- AI Verify 10.3.2 Human Agency and Oversight: flag decisions the model is significantly unsure of for human review, and monitor human overrides (core)
- EU AI Act Art. 26 Obligations of deployers of high-risk AI systems
- NIST AI RMF GOVERN 3.2 GOVERN 3.2: Policies and procedures are in place to define and differentiate roles and responsibilities for human-AI configurations and oversight of AI systems
- China Algo. Rec. AlgoRec Art. 17 User option to switch off
- China GenAI Measures GenAI Art. 10 User guidance and protection
- EU AI Act Art. 14(4)(b) Awareness of automation bias
- OWASP Agentic ASI09 Human-Agent Trust Exploitation
- CoE Convention CoE Art. 8 Transparency and oversight
- OECD AI Principles OECD 1.2(b) Human agency and oversight safeguards
- AI Verify 9.4.1 Accountability: a manual to deactivate the AI model and a communications plan for disengagement
Runtime guardrails
- ISO 42001 A.9 Use of AI systems (core)
- NIST AI RMF MANAGE 2 MANAGE 2: Strategies to maximize AI benefits and minimize negative impacts are planned, prepared, implemented, documented, and informed by relevant AI actors (core)
- TC260 Framework 3.0 TC260 App. 2 II.5 Dynamic runtime management (core)
- TC260 Framework 3.0 TC260 3.2.1 Technological countermeasures for agentic AI (core)
- China GenAI Measures GenAI Art. 10 Guided, bounded use (core)
- China GenAI Measures GenAI Art. 14 Stop unlawful generation (core)
- China Deep Synthesis DeepSyn Art. 10 Input and output review (core)
- OWASP LLM LLM01:2026 Prompt Injection (core)
- OWASP LLM LLM10:2026 Improper Output Handling (core)
- Singapore Agentic Agentic 2.3.1 During design and development, use technical controls (core)
- EU AI Act Art. 5 Prohibited AI practices
- EU AI Act Art. 15 Accuracy, robustness and cybersecurity
- ISO 42001 A.6 AI system life cycle
- China Algo. Rec. AlgoRec Art. 8 Periodic algorithm review
- China Algo. Rec. AlgoRec Art. 9 Feature database for unlawful content
- EU AI Act Art. 5(1)(a)–(b) Manipulative techniques; exploitation of vulnerabilities
- GPAI Code Safety C5 Commitment 5: Safety mitigations
- OWASP LLM LLM06:2026 Unbounded Consumption
- AI Verify 4.8.1 Safety: assess harms and implement measures that prevent the model generating unlawful or harmful content (CSAM, NCII, CBRN, offensive cyber)
Robustness, security and evaluations
- EU AI Act Art. 15 Accuracy, robustness and cybersecurity (core)
- EU AI Act Art. 55 Obligations for providers of general-purpose AI models with systemic risk (core)
- ISO 42001 A.6 AI system life cycle (core)
- NIST AI RMF MEASURE 2 MEASURE 2: AI systems are evaluated for trustworthy characteristics (core)
- TC260 Framework 3.0 TC260 3 Technological countermeasures (core)
- TC260 Framework 3.0 TC260 App. 2 II.6 Sandbox validation and red teaming (core)
- China Deep Synthesis DeepSyn Art. 15 Technology management and algorithm verification (core)
- China Deep Synthesis DeepSyn Art. 20 Security assessment of new products (core)
- GB/T 45654 GB/T 45654 Security assessment Security-assessment requirements for generative AI services (core) (clause not verified)
- GPAI Code Safety 3.2 Measure 3.2: Model evaluations (core)
- NIST AI RMF MEASURE 2.7 MEASURE 2.7: AI system security and resilience as identified in the MAP function are evaluated and documented (core)
- Singapore GenAI GenAI 5 Testing and Assurance (core)
- Singapore GenAI GenAI 6 Security (core)
- Singapore Agentic Agentic 2.3.2 Before deploying, test agents (core)
- CoE Convention CoE Art. 16(2)(g) Testing before first use and when significantly modified (core)
- OECD AI Principles OECD 1.4 Robustness, security and safety (core)
- G7 Code G7 Action 1 Identify, evaluate and mitigate risks across the lifecycle, including testing (core)
- GAO AI Accountability 3.7 Assessment: assess performance against defined metrics to ensure the AI system functions as intended and is sufficiently robust (core)
- AI Verify 4.1.1 Safety (technical test): run use-case benchmarks and red-teaming to evaluate for safety and possible harms (e.g. hallucination, toxicity) (core)
- AI Verify 6.1.1 Robustness (technical test): compare accuracy with and without added noise to document and monitor performance (core)
- AI Verify 5.2.1 Security: conduct security risk assessments that model threats and risks to the AI system (core)
- EU AI Act Art. 60 Testing of high-risk AI systems in real-world conditions outside AI regulatory sandboxes
- ISO 42001 9.1 Monitoring, measurement, analysis and evaluation
- TC260 Framework 3.0 TC260 5.3.14 Resilience
- China GenAI Measures GenAI Art. 17 Security assessment
- EU AI Act Art. 15(3) Declared accuracy levels and metrics
- EU AI Act Art. 9 Risk management system
- EU AI Act Art. 42(3) Presumption of conformity for cybersecurity (Cyber Resilience Act)
- GPAI Code Safety C6 Commitment 6: Security mitigations
- GDPR Art. 32 Security of processing
- NIST AI RMF MEASURE 2.1 MEASURE 2.1: Test sets, metrics, and details about the tools used during TEVV are documented
- NIST AI RMF MEASURE 1 MEASURE 1: Appropriate methods and metrics are identified and applied
- OWASP LLM LLM01:2026 Prompt Injection
- OWASP Agentic ASI05 Unexpected Code Execution (RCE)
- Korea AI Act Art. 32(1) Safety duties for AI above the compute threshold
- GAO AI Accountability 3.2 Metrics: define performance metrics that are precise, consistent, and reproducible
- AI Verify 5.9.1 Security: release AI systems only after appropriate and effective security checks and evaluation
Incident response and monitoring
- EU AI Act Art. 72 Post-market monitoring by providers and post-market monitoring plan (core)
- EU AI Act Art. 73 Reporting of serious incidents (core)
- ISO 42001 A.8 Information for interested parties (core)
- ISO 42001 10.2 Nonconformity and corrective action (core)
- NIST AI RMF MANAGE 4 MANAGE 4: Risk treatments, including response and recovery, and communication plans for the identified and measured AI risks are documented and monitored (core)
- TC260 Framework 3.0 TC260 5.3.7 Real-time risk monitoring (core)
- TC260 Framework 3.0 TC260 5.3.18 Incident reporting (core)
- China GenAI Measures GenAI Art. 14 Handle and report unlawful content (core)
- China GenAI Measures GenAI Art. 15 Complaint and reporting mechanism (core)
- EU AI Act Art. 26(5) Deployer monitoring, informing the provider and suspending use (core)
- GPAI Code Safety C9 Commitment 9: Serious incident reporting (core)
- GDPR Arts. 33–34 Notification and communication of a personal data breach (core)
- NIST AI RMF MANAGE 4.3 MANAGE 4.3: Incidents and errors are communicated to relevant AI actors, including affected communities. Processes for tracking, responding to, and recovering from incidents and errors are followed and documented (core)
- Korea AI Act Art. 32(1) Safety duties for AI above the compute threshold (core)
- Singapore GenAI GenAI 4 Incident Reporting (core)
- Singapore Agentic Agentic 2.3.3 When deploying, continuously monitor and test (core)
- G7 Code G7 Action 2 Identify and mitigate vulnerabilities, incidents and misuse after deployment (core)
- G7 Code G7 Action 4 Responsible information sharing and reporting of incidents (core)
- GAO AI Accountability 4.1 Planning: develop plans for continuous or routine monitoring of the AI system (core)
- GAO AI Accountability 4.2 Drift: establish the range of data and model drift that is acceptable (core)
- AI Verify 9.5.4 Accountability: enable an incident management process to report and respond to actions or decisions that affect the AI system's outcome (core)
- AI Verify 1.5.1 Transparency: communicate to end users whenever an incident has occurred (core)
- EU AI Act Art. 55 Obligations for providers of general-purpose AI models with systemic risk
- TC260 Framework 3.0 TC260 App. 2 II.6 Emergency plans
- China Algo. Rec. AlgoRec Art. 7 Security management and emergency response
- EU AI Act Art. 3(49) Definition of serious incident
- EU AI Act Art. 20 Corrective actions and duty of information
- GPAI Code Safety 3.5 Measure 3.5: Post-market monitoring
- NIST AI RMF MANAGE 2.4 MANAGE 2.4: Mechanisms are in place and applied, and responsibilities are assigned and understood, to supersede, disengage, or deactivate AI systems that demonstrate performance or outcomes inconsistent with intended use
- NIST AI RMF GOVERN 4.3 GOVERN 4.3: Organizational practices are in place to enable AI testing, identification of incidents, and information sharing
- AI Verify 1.6.1 Transparency: give end users and external parties the means to report adverse impacts, with whistleblower protection
- AI Verify 5.11.1 Security: monitor AI system outputs and behaviour after deployment and alert system owners to issues
Supply chain and third parties
- EU AI Act Art. 25 Responsibilities along the AI value chain (core)
- ISO 42001 A.10 Third-party and customer relationships (core)
- NIST AI RMF GOVERN 6 GOVERN 6: Policies and procedures are in place to address AI risks and benefits arising from third-party software and data and other supply chain issues (core)
- NIST AI RMF MAP 4 MAP 4: Risks and benefits are mapped for all AI system components including third-party software and data (core)
- NIST AI RMF MANAGE 3 MANAGE 3: AI risks and benefits from third-party entities are managed (core)
- TC260 Framework 3.0 TC260 App. 2 II.4 Supply chain and tool management (core)
- EU AI Act Art. 25(4) Written agreement with third-party suppliers (core)
- GDPR Art. 28 Processor (core)
- NIST AI RMF MANAGE 3.1 MANAGE 3.1: AI risks and benefits from third-party resources are regularly monitored, and risk controls are applied and documented (core)
- OWASP LLM LLM04:2026 Supply Chain (core)
- OWASP Agentic ASI04 Agentic Supply Chain Vulnerabilities (core)
- AI Verify 9.7.1 Accountability: clear responsibilities between parties in the supply chain, set in service level agreements and contracts (core)
- AI Verify 5.3.1 Security: secure the supply chain by assessing and monitoring its security risks across the life cycle (core)
- AI Verify 4.5.2 Safety: continuously monitor third-party systems, with a fallback plan and contracts that assign liability (core)
- EU AI Act Art. 26 Obligations of deployers of high-risk AI systems
- TC260 Framework 3.0 TC260 4.4.4 Open-source ecosystem
- China GenAI Measures GenAI Art. 7 Lawful data and model sources
- China Deep Synthesis DeepSyn Art. 14 Providers and technical supporters
- EU AI Act Art. 22 Authorised representatives of providers of high-risk AI systems
- EU AI Act Art. 23 Obligations of importers
- EU AI Act Art. 24 Obligations of distributors
- EU AI Act Art. 54 Authorised representatives of providers of general-purpose AI models
- GPAI Code Transparency 1.2 Providing relevant information
- GDPR Arts. 44–46 Transfers to third countries
- NIST AI RMF GOVERN 6.2 GOVERN 6.2: Contingency processes are in place to handle failures or incidents in third-party data or AI systems deemed to be high-risk
- UK ATRS ATRS 2.1.4 Third party involvement
- G7 Code G7 Action 11 Implement data input measures and protect personal data and intellectual property
- GAO AI Accountability 2.6 Dependency: assess interconnectivities and dependencies of data streams that operationalize the AI system
- AI Verify 9.3.1 Accountability: select AI system suppliers aligned with the organisation's approach, with remedial action when they underperform
Prohibited practices
- EU AI Act Art. 5 Prohibited AI practices (core)
- CoE Convention CoE Art. 16(4) Assess the need for a moratorium, ban or other measures for incompatible uses (core)
- ISO 42001 A.9.4 Intended use of the AI system (clause not verified)
- NIST AI RMF GOVERN 1.1 GOVERN 1.1: Legal and regulatory requirements involving AI are understood, managed, and documented
- Singapore Agentic Agentic 2.1.1 Determine suitable use cases for agent deployment
- China GenAI Measures GenAI Art. 4 Prohibited content and baseline duties
Fairness and non-discrimination
- EU AI Act Art. 10(2)(f)–(g) Examination for possible biases; measures to detect, prevent and mitigate them (core)
- EU AI Act Art. 4a Special-category data for bias detection (core)
- GDPR Art. 5(1)(a) Lawfulness, fairness and transparency (core)
- NIST AI RMF MEASURE 2.11 MEASURE 2.11: Fairness and bias as identified in the MAP function are evaluated and results are documented (core)
- CoE Convention CoE Art. 10 Equality and non-discrimination (core)
- China GenAI Measures GenAI Art. 4(2) Prevent discrimination in design, data, training and service (core)
- GAO AI Accountability 2.7 Bias: assess reliability, quality, and representativeness of the data used in operation, including potential biases (core)
- GAO AI Accountability 3.8 Bias: identify potential biases, inequities, and other societal concerns resulting from the AI system (core)
- AI Verify 7.1.1 Fairness (technical test): measure algorithmic fairness of predictions against ground truth, with metrics chosen for the sensitive feature (core)
- AI Verify 7.2.1 Fairness (technical test): benchmark and red-team for harms against individuals or groups (generative AI) (core)
- GDPR Art. 9 Processing of special categories of personal data
- ISO 42001 A.5.4 Assessing AI system impact on individuals or groups of individuals (clause not verified)
- NIST AI RMF GOVERN 3.1 GOVERN 3.1: Decision-making related to mapping, measuring, and managing AI risks throughout the lifecycle is informed by a diverse team
- UK ATRS ATRS 2.4.2 Model specification
- OECD AI Principles OECD 1.2 Rule of law, human rights and democratic values, including fairness and privacy
- China Algo. Rec. AlgoRec Art. 21 No unreasonable differential treatment in trading conditions
- AI Verify 7.8.1 Fairness: check that training data is representative of the end-user population, resampling or collecting more data where needed
Privacy and data protection
- GDPR Art. 5 Principles relating to processing of personal data (core)
- GDPR Art. 6 Lawfulness of processing (core)
- GDPR Art. 25 Data protection by design and by default (core)
- NIST AI RMF MEASURE 2.10 MEASURE 2.10: Privacy risk of the AI system as identified in the MAP function is examined and documented (core)
- OWASP LLM LLM02:2026 Sensitive Information Disclosure (core)
- CoE Convention CoE Art. 11 Privacy and personal data protection (core)
- China GenAI Measures GenAI Art. 7(3) Consent or another lawful basis for personal information in training data (core)
- China GenAI Measures GenAI Art. 11 Protection of user input and records (core)
- GAO AI Accountability 2.8 Security and privacy: assess data security and privacy for the AI system (core)
- AI Verify 8.3.1 Data Governance: data protection, consent, collection, use, withdrawal and retention comply with regulatory requirements or industry standards (core)
- AI Verify 8.3.2 Data Governance: detect personal or sensitive data in generated output and apply anonymisation and privacy-enhancing technologies (core)
- EU AI Act Art. 59 Further processing of personal data in the AI regulatory sandbox
- EU AI Act Art. 4a Special-category data for bias detection
- GDPR Art. 35 Data protection impact assessment
- ISO 42001 A.7 Data for AI systems
- UK DUAA UK GDPR Art. 22B Restrictions on automated decision-making
- Singapore GenAI GenAI 2 Data
- OECD AI Principles OECD 1.2 Rule of law, human rights and democratic values, including fairness and privacy
- G7 Code G7 Action 11 Implement data input measures and protect personal data and intellectual property
- AI Verify 1.4.1 Transparency: publish a privacy policy on the use of personal data in the AI system
Explainability and right to explanation
- EU AI Act Art. 86 Right to explanation of individual decision-making (core)
- EU AI Act Art. 13(3)(b)(iv)–(v) Information relevant to explain output; performance for specific persons or groups (core)
- GDPR Art. 15(1)(h) Meaningful information about the logic involved (core)
- NIST AI RMF MEASURE 2.9 MEASURE 2.9: The AI model is explained, validated, and documented, and AI system output is interpreted within its context as identified in the MAP function to inform responsible use and governance (core)
- Korea AI Act Art. 34(1)(2) Explanation plan: result, main criteria, training-data overview (core)
- UK DUAA UK GDPR Art. 22C Safeguards for automated decision-making (core)
- OECD AI Principles OECD 1.3 Transparency and explainability (core)
- AI Verify 2.2.1 Explainability: document the rationale and trade-offs of a less explainable model and apply explainable AI techniques (core)
- AI Verify 1.1.4 Transparency: tell people using or affected by the system the factors behind a decision and that it was made wholly or partly by an automated system (core)
- GDPR Art. 13(2)(f) Existence of automated decision-making
- GDPR Art. 22(3) Right to obtain human intervention and to contest the decision
- ISO 42001 A.8.2 System documentation and information for users (clause not verified)
- NIST AI RMF MEASURE 2.8 MEASURE 2.8: Risks associated with transparency and accountability as identified in the MAP function are examined and documented
- UK ATRS ATRS 2.3.5 Appeals and review
- Singapore GenAI GenAI 3 Trusted Development and Deployment
- CoE Convention CoE Art. 14(2) Documentation sufficient to contest decisions; complaint to authorities
- China Algo. Rec. AlgoRec Art. 17 Explain where an algorithm significantly affects user rights
AI literacy and competence
- EU AI Act Art. 4 AI literacy (core)
- ISO 42001 7.2 Competence (core) (clause not verified)
- NIST AI RMF GOVERN 2.2 GOVERN 2.2: The organization's personnel and partners receive AI risk management training to enable them to perform their duties and responsibilities consistent with related policies, procedures, and agreements (core)
- Singapore Agentic Agentic 2.4 Enable end-user responsibility (core)
- EU AI Act Art. 26(2) Oversight by people with the competence, training and authority it needs
- EU AI Act Art. 95(2)(c) Codes of conduct: promoting AI literacy
- GDPR Art. 39(1)(b) DPO tasks: awareness-raising and training of staff (clause not verified)
- ISO 42001 7.3 Awareness (clause not verified)
- NIST AI RMF MAP 3.4 MAP 3.4: Processes for operator and practitioner proficiency with AI system performance and trustworthiness, and relevant technical standards and certifications, are defined, assessed, and documented
- UK ATRS ATRS 2.3.4 Required training
- Singapore GenAI GenAI 9 AI for Public Good
- China GenAI Measures GenAI Art. 10 Guide users to understand and use generative AI rationally
- GAO AI Accountability 1.4 Workforce: recruit, develop, and retain personnel with multidisciplinary skills and experiences
- AI Verify 9.5.3 Accountability: policies, procedures and training so that staff know their duties and the organisation's risk management practices
- AI Verify 5.1.1 Security: training and guidance on AI security risks for all personnel, including developers, system owners and senior leaders
- AI Verify 8.4.1 Data Governance: ensure team competency in data governance roles and responsibilities
Conformity assessment and certification
- EU AI Act Art. 43 Conformity assessment (core)
- ISO 42006 ISO/IEC 42006 Requirements for bodies providing audit and certification of AI management systems (core)
- EU AI Act Art. 47 EU declaration of conformity
- EU AI Act Art. 48 CE marking
- EU AI Act Art. 40 Harmonised standards and standardisation deliverables
- GDPR Art. 42 Certification (clause not verified)
- ISO 42001 9.2 Internal audit (clause not verified)
- NIST AI RMF MEASURE 1.3 MEASURE 1.3: Internal experts who did not serve as front-line developers for the system and/or independent assessors are involved in regular assessments and updates
- Korea AI Act Art. 33 Confirmation of high-impact AI
- Singapore GenAI GenAI 5 Testing and Assurance
- EN 18286 EN 18286 Quality management system for EU AI Act regulatory purposes
- China GenAI Measures GenAI Art. 17 Security assessment and algorithm filing
- GAO AI Accountability 1.8 Compliance: ensure the AI system complies with relevant laws, regulations, standards, and guidance
GPAI and foundation models
- EU AI Act Art. 53 Obligations for providers of general-purpose AI models (core)
- EU AI Act Art. 55 Obligations of providers of general-purpose AI models with systemic risk (core)
- GPAI Code Transparency 1.1 Drawing up and keeping up-to-date model documentation (core)
- GPAI Code Safety C1 Commitment 1: Safety and Security Framework (core)
- Korea AI Act Art. 32 Safety duties for AI above the compute threshold (core)
- EU AI Act Art. 51 Classification of general-purpose AI models as general-purpose AI models with systemic risk
- EU AI Act Art. 56 Codes of practice
- GPAI Code Safety 3.2 Measure 3.2: Model evaluations
- GPAI Code Safety C7 Commitment 7: Safety and Security Model Reports
- Singapore GenAI GenAI 8 Safety and Alignment R&D
- G7 Code G7 Action 1 Identify, evaluate and mitigate risks across the lifecycle, including testing
- China GenAI Measures GenAI Art. 7 Lawful data and foundation-model sources
- AI Verify 4.1.1 Safety (technical test): run benchmarks and red-teaming to evaluate a generative model for safety and possible harms
- AI Verify 4.8.1 Safety: identify and prevent content that violates laws or is harmful, including CBRN information and offensive cyber capabilities
IP and copyright
- EU AI Act Art. 53(1)(c) Copyright policy, including rights reservations (core)
- GPAI Code Copyright 1.1 Draw up, keep up-to-date and implement a copyright policy (core)
- GPAI Code Copyright 1.2 Reproduce and extract only lawfully accessible copyright-protected content (core)
- GPAI Code Copyright 1.3 Identify and comply with rights reservations when crawling the World Wide Web (core)
- GPAI Code Copyright 1.4 Mitigate the risk of copyright-infringing outputs (core)
- NIST AI RMF GOVERN 6.1 GOVERN 6.1: Policies and procedures are in place that address AI risks associated with third-party entities, including risks of infringement of a third-party's intellectual property or other rights (core)
- G7 Code G7 Action 11 Implement data input measures and protect personal data and intellectual property (core)
- China GenAI Measures GenAI Art. 7(2) No infringement of IP rights in training data (core)
- AI Verify 8.5.1 Data Governance: address risks from third parties, including infringement of their rights (copyright, intellectual property) (core)
- EU AI Act Art. 53(1)(d) Public summary of the content used for training
- GPAI Code Copyright 1.5 Designate a point of contact and enable the lodging of complaints
- NIST AI RMF MAP 4.1 MAP 4.1: Approaches for mapping AI technology and legal risks of its components, including the use of third-party data or software, are in place, followed, and documented, as are risks of infringement of a third party's intellectual property or other rights
- Singapore GenAI GenAI 2 Data
- China GenAI Measures GenAI Art. 4(3) Respect IP rights and business ethics
- AI Verify 8.1.1 Data Governance: verify data quality, including intellectual property and privacy risks
Agent identity and autonomy
- OWASP Agentic ASI03 Identity and Privilege Abuse (core)
- OWASP LLM LLM03:2026 Excessive Agency (core)
- Singapore Agentic Agentic 2.1.2 Bound risks through design by defining agents limits and permissions (core)
- TC260 Framework 3.0 TC260 App. 2 II.2 Identity and access management (core)
- EU AI Act Art. 14 Human oversight
- NIST AI RMF GOVERN 3.2 GOVERN 3.2: Policies and procedures are in place to define and differentiate roles and responsibilities for human-AI configurations and oversight of AI systems
- OWASP Agentic ASI02 Tool Misuse and Exploitation
- OWASP Agentic ASI07 Insecure Inter-Agent Communication
- OWASP Agentic ASI10 Rogue Agents
- Singapore Agentic Agentic 2.2.2 Design for meaningful human oversight
- TC260 Framework 3.0 TC260 App. 2 II.3 Strengthen human approval
Content provenance and deepfakes
- EU AI Act Art. 50(2) Machine-readable marking of synthetic content (core)
- EU AI Act Art. 50(4) Disclosure of deep fakes (core)
- Korea AI Act Art. 31 Transparency: prior notice, output labelling, realistic synthetic content (core)
- Singapore GenAI GenAI 7 Content Provenance (core)
- G7 Code G7 Action 7 Deploy content authentication and provenance mechanisms where feasible (core)
- China AI Labelling Label Art. 4 Explicit labels for generated content (core)
- China AI Labelling Label Art. 5 Implicit (metadata) labels (core)
- China Deep Synthesis DeepSyn Art. 17 Conspicuous labels for confusable content (core)
- AI Verify 4.9.1 Safety: establish policies for content provenance: identify provenance harms such as deepfakes and trace content origin (core)
- AI Verify 4.9.2 Safety: evaluate content provenance mechanisms, including the effectiveness and reliability of watermarks (core)
- AI Verify 1.1.3 Transparency: labelling or disclaimers so users know when they interact with an AI system and understand content lineage (core)
- EU AI Act Art. 3(60) Definition of deep fake
- OWASP LLM LLM07:2026 Misinformation
- China GenAI Measures GenAI Art. 12 Labelling of generated content
Sandboxes and real-world testing
- EU AI Act Art. 57 AI regulatory sandboxes (core)
- EU AI Act Art. 60 Testing of high-risk AI systems in real world conditions outside AI regulatory sandboxes (core)
- CoE Convention CoE Art. 13 Safe innovation (controlled testing environments) (core)
- EU AI Act Art. 58 Detailed arrangements for, and functioning of, AI regulatory sandboxes
- EU AI Act Art. 59 Further processing of personal data in the AI regulatory sandbox
- EU AI Act Art. 61 Informed consent to participate in testing in real world conditions
- ISO 42001 A.6.2.4 AI system verification and validation (clause not verified)
- NIST AI RMF MEASURE 2.3 MEASURE 2.3: AI system performance or assurance criteria are measured qualitatively or quantitatively and demonstrated for conditions similar to deployment setting(s)
- Singapore Agentic Agentic 2.3.2 Before deploying, test agents
- TC260 Framework 3.0 TC260 App. 2 II.6 Sandbox validation and red teaming
Deployment, change and decommissioning
- EU AI Act Art. 26 Obligations of deployers of high-risk AI systems (core)
- ISO 42001 A.6.2.5 AI system deployment (core) (clause not verified)
- ISO 42001 A.6.2.6 AI system operation and monitoring (core) (clause not verified)
- NIST AI RMF MANAGE 2.4 MANAGE 2.4: Mechanisms are in place and applied, and responsibilities are assigned and understood, to supersede, disengage, or deactivate AI systems that demonstrate performance or outcomes inconsistent with intended use (core)
- NIST AI RMF MANAGE 4.1 MANAGE 4.1: Post-deployment AI system monitoring plans are implemented, including mechanisms for capturing and evaluating input from users and other relevant AI actors, appeal and override, decommissioning, incident response, recovery, and change management (core)
- NIST AI RMF GOVERN 1.7 GOVERN 1.7: Processes and procedures are in place for decommissioning and phasing out AI systems safely and in a manner that does not increase risks or decrease the organization's trustworthiness (core)
- AI Verify 9.4.1 Accountability: a policy to deactivate, decommission and phase out AI systems safely (core)
- AI Verify 5.14.1 Security: proper and secure disposal of data and models (core)
- AI Verify 6.5.1 Robustness: review changes in code, input data and output data to decide when a new robustness review is due (core)
- EU AI Act Art. 25 Responsibilities along the AI value chain
- EU AI Act Art. 43(4) New conformity assessment on substantial modification
- EU AI Act Art. 20 Corrective actions and duty of information
- EU AI Act Art. 79 Procedure at national level for dealing with AI systems presenting a risk
- EU AI Act Art. 86 Right to explanation of individual decision-making
- ISO 42001 A.9 Use of AI systems
- Singapore Agentic Agentic 2.3.3 When deploying, continuously monitor and test
- CoE Convention CoE Art. 16(2)(g) Testing before first use and when significantly modified
- OECD AI Principles OECD 1.4 Robustness, security and safety
- TC260 Framework 3.0 TC260 5.3 Operators' safety guidelines
- TC260 Framework 3.0 TC260 5.3.19 Re-assessment on material change
- GAO AI Accountability 4.4 Ongoing assessment: assess the utility of the AI system to ensure its relevance to the current context
- GAO AI Accountability 4.5 Scaling: identify conditions, if any, under which the AI system may be scaled or expanded beyond its current use
- AI Verify 4.6.1 Safety: deployment strategies such as blue-green and canary deployments
Open controls that evidence it
Draft controls in the open control profiles that map to this row: each states a requirement and the evidence it must leave behind.
-
AIGE-CTL-ASSURE-005Live Control Status from the Assurance Store (Assurance and evidence profile) -
AIGE-CTL-ASSURE-012AI Bill of Materials per Build (Assurance and evidence profile)
Source
Chapter 08, section CSA AICM and STAR for AI, checked against its sources on the review date above.
Machine-readable
- This obligation:
/api/v1/obligations/aige-obl-csa-aicm.json - The register:
/api/v1/obligations.json· CSV - Schema and stability promise: open data and API
Cite this obligation
García Aibar, J. (2026). CSA AICM v1.1: 247 control objectives across 18 domains (AIGE-OBL-CSA-AICM). In AI Governance Engineering: The Thesis & Body of Knowledge (v0.5.0). https://doi.org/10.5281/zenodo.22956197. https://aigovernanceengineer.com/obligations/aige-obl-csa-aicm. CC BY 4.0
BibTeX
@misc{aige2026obligation,
author = {Jorge García Aibar},
title = {{CSA AICM v1.1: 247 control objectives across 18 domains (AIGE-OBL-CSA-AICM)}},
howpublished = {In AI Governance Engineering: The Thesis \& Body of Knowledge},
year = {2026},
version = {0.5.0},
doi = {10.5281/zenodo.22956197},
url = {https://aigovernanceengineer.com/obligations/aige-obl-csa-aicm},
note = {Version 0.5.0}
}