CSA AICM v1.1: 247 control objectives across 18 domains

247 control objectives across 18 domains, spanning governance, data, model and runtime

By Jorge García Aibar Reviewed 2026-09-19

From clause to evidence Build the artefact, then file every output it produces as a record that names 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 28 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.

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)
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

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)
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)
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

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)
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)
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

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)
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

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)
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

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)
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

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

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)
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

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)
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

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)
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

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)
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

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)
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

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)
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

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

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)
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

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)
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)
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

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-005 Live Control Status from the Assurance Store (Assurance and evidence profile)
AIGE-CTL-ASSURE-012 AI 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}
}