NIST AI RMF MAP
Context and risk framing for each AI system
AIGE-OBL-NISTRMF-MAP. Drawn from chapter 08.
Text alternative
- Clause: NIST AI RMF, MAP.
- Duty holder: Not stated.
- Applies from: No date, Voluntary.
- Artefact: Threat models.
- Layers: Layer 02, Layer 03.
- Evidence record: Use-case record, +8 more.
- Record schemas: Use-case record , Instructions for use , Model card , Impact assessment , Dataset admission record , Risk register entry , Dataset card , Training record , Design record .
- The same topic in 27 other frameworks; the crosswalk section below links each clause.
- Id
AIGE-OBL-NISTRMF-MAP- Instrument
- NIST AI RMF framework
- Compared side by side
- NIST AI RMF vs ISO 42001 · NIST AI RMF vs EU AI Act
- Clause
- MAP
- Applies from
- No date Voluntary · Voluntary (AI RMF 1.0, January 2023)
The artefact that evidences it
Threat models; use-case and impact mapping; data/model cards.
Data table: every relation
| Family | Item | Relation |
|---|---|---|
| Patterns | AI Threat Model | Direct |
| Patterns | AIBOM | Direct |
| Patterns | Agent Registry | Direct |
| Patterns | Dataset Admission Gate | Direct |
| Patterns | Decision Notice & Contest Path | Direct |
| Patterns | Downstream Use Register | Direct |
| Patterns | FRIA-as-Code | Direct |
| Patterns | Model Card as Control Evidence | Direct |
| Patterns | Shadow-AI Discovery | Direct |
| Patterns | Training-Data Rights Ledger | Direct |
| Patterns | Use-Case Intake & Risk Tiering | Direct |
| Patterns | Vendor / Model Due-Diligence Gate | Direct |
| Open controls | AIGE-CTL-ASSURE-012 AI Bill of Materials per Build | Direct |
| Same crosswalk topic | AI Verify 4.3.1, 4.7.1, 4.2.1, 11.2.1, 8.1.1, 8.2.1, 1.1.6, 1.7.2, 1.1.1, 9.7.1, 5.3.1, 4.5.2, 9.3.1, 10.3.1, 10.3.2, 9.4.1, 9.5.3, 5.1.1, 8.4.1, 8.5.1 | Core clause |
| Same crosswalk topic | CSA AICM GRC-02, MDS-12, GRC-10, DSP-09, DSP-20, DSP-21, MDS-03, MDS-04, STA-10, STA-09, GRC-15, HRS-14, HRS-11 | Core clause |
| Same crosswalk topic | China AI Labelling Label Art. 4, Label Art. 5 | Core clause |
| Same crosswalk topic | China Deep Synthesis DeepSyn Art. 14, DeepSyn Art. 16, DeepSyn Art. 17 | Core clause |
| Same crosswalk topic | China GenAI Measures GenAI Art. 17, GenAI Art. 7, GenAI Art. 8, GenAI Art. 11, GenAI Art. 12, GenAI Art. 19, GenAI Art. 10, GenAI Art. 7(2), GenAI Art. 4(3) | Core clause |
| Same crosswalk topic | CoE Convention CoE Art. 16, CoE Art. 14(2), CoE Art. 15(2), CoE Art. 8 | Core clause |
| Same crosswalk topic | EU AI Act Art. 9, Art. 3, 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. 25, Art. 25(4), Art. 26, Art. 22, Art. 23, Art. 24, Art. 54, Art. 14, Art. 14(4)(b), Art. 4, Art. 26(2), Art. 95(2)(c) | Core clause |
| Same crosswalk topic | G7 Code G7 Action 3, G7 Action 11 | Core clause |
| Same crosswalk topic | GAO AI Accountability 1.6, 1.5, 2.1, 2.2, 2.4, 2.5, 1.9, 3.5, 1.7, 2.6, 3.9, 1.4 | Core clause |
| Same crosswalk topic | GB/T 45654 GB/T 45654 Corpus security, GB/T 45654 Content labelling | Core clause |
| Same crosswalk topic | GDPR Art. 35, Art. 36, Art. 5(1)(c), Art. 25, Art. 9, Arts. 13–14, Art. 30, Art. 28, Arts. 44–46, Art. 22, Art. 39(1)(b) | Core clause |
| Same crosswalk topic | GPAI Code Safety C1, Safety C3, Copyright 1.1–1.5, Transparency 1.1, Transparency 1.2, 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, 8.4, A.5, A.7, A.7.3, A.4, 7.5, A.8, A.10, A.9, 7.2, 7.3 | Core clause |
| Same crosswalk topic | ISO 42005 42005 5.8, 42005 6.8, 42005 5.12 | Core clause |
| Same crosswalk topic | Korea AI Act Art. 34(1)(1), Art. 35, Art. 31, Art. 34(1)(2), Art. 34(1)(4) | Core clause |
| Same crosswalk topic | NIST AI RMF MANAGE 1, GOVERN 1.3, MANAGE 1.3, MANAGE 1.4, MEASURE 3, MEASURE 2, MEASURE 2.10, MEASURE 2.11, MEASURE 2.8, MEASURE 2.9, GOVERN 6, MANAGE 3, MANAGE 3.1, GOVERN 6.2, MANAGE 2.4, GOVERN 3.2, GOVERN 2.2, GOVERN 6.1 | Core clause |
| Same crosswalk topic | OECD AI Principles OECD 1.5(c), OECD 1.3, OECD 1.2(b) | Core clause |
| Same crosswalk topic | OWASP Agentic ASI04, ASI09 | Core clause |
| Same crosswalk topic | OWASP LLM LLM05:2026, LLM04:2026 | Core clause |
| Same crosswalk topic | Singapore Agentic Agentic 2.1, Agentic 2.2.2, Agentic 2.4 | Core clause |
| Same crosswalk topic | Singapore GenAI GenAI 2, GenAI 3, GenAI 9 | Core clause |
| Same crosswalk topic | TC260 Framework 3.0 TC260 2, TC260 Summary table, TC260 5.3.19, TC260 Appendix 1, TC260 2.2, TC260 2.1.3, TC260 5.1, TC260 App. 2 II.4, TC260 4.4.4, TC260 App. 2 II.3 | Core clause |
| Same crosswalk topic | UK ATRS ATRS 2.5.2, ATRS 2.5.1, ATRS 2.4.3, ATRS Tier 1, ATRS 2.2, ATRS 2.1.4, ATRS 2.3.2, ATRS 2.3.4 | Core clause |
| Same crosswalk topic | UK DUAA UK GDPR Art. 22C | Core clause |
| Same crosswalk topic | prEN 18228 prEN 18228 | Core clause |
| Same crosswalk topic | China Algo. Rec. AlgoRec Art. 27, AlgoRec Art. 16, AlgoRec Art. 17 | Related clause |
Patterns that build it
- Agent Registry (layer 2)
- AIBOM (layer 2)
- Model Card as Control Evidence (layer 2)
- FRIA-as-Code (layer 1 and 2)
- Shadow-AI Discovery (layer 2)
- Vendor / Model Due-Diligence Gate (layer 2 and 5)
- Use-Case Intake & Risk Tiering (layer 1 and 2)
- AI Threat Model (layer 1 and 3)
- Training-Data Rights Ledger (layer 2)
- Dataset Admission Gate (layer 1 and 2)
- Decision Notice & Contest Path (layer 4 and 5)
- Downstream Use Register (layer 2 and 1)
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 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 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)
- CSA AICM GRC-02 Risk Management Program (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
- CSA AICM MDS-12 Open Model Risk Assessment
- 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
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)
- 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)
- CSA AICM GRC-10 AI Impact Assessment (core)
- 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)
- CSA AICM DSP-09 Data Protection Impact Assessment
- 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)
- CSA AICM DSP-20 Data Provenance and Transparency (core)
- 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 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
- CSA AICM DSP-21 Data Poisoning Prevention & Detection
- 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)
- CSA AICM MDS-03 Model Documentation (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 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 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
- CSA AICM MDS-04 Model Documentation Requirements
- 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
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 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)
- CSA AICM STA-10 Supply Chain Risk Management (core)
- CSA AICM STA-09 Service Bill of Material (BOM) (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
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)
- CSA AICM GRC-15 Human supervision (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
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)
- CSA AICM HRS-14 AI Competency Training (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)
- CSA AICM HRS-11 Security Awareness Training
- 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
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
- CSA AICM DSP-20 Data Provenance and Transparency
- 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
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-012AI Bill of Materials per Build (Assurance and evidence profile)
Source
Chapter 08, section NIST AI RMF, checked against its sources on the review date above.
Machine-readable
- This obligation:
/api/v1/obligations/aige-obl-nistrmf-map.json - The register:
/api/v1/obligations.json· CSV - Schema and stability promise: open data and API
Cite this obligation
García Aibar, J. (2026). NIST AI RMF MAP (AIGE-OBL-NISTRMF-MAP). 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-nistrmf-map. CC BY 4.0
BibTeX
@misc{aige2026obligation,
author = {Jorge García Aibar},
title = {{NIST AI RMF MAP (AIGE-OBL-NISTRMF-MAP)}},
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-nistrmf-map},
note = {Version 0.5.0}
}