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Cross-Framework Mapping

ISO/IEC 42001:2023vsNIST AI Risk Management Framework (AI RMF 1.0)

See exactly how ISO/IEC 42001:2023 controls map to NIST AI Risk Management Framework (AI RMF 1.0). Pre-computed mappings, identified gaps, and coverage analysis.

68
Controls Mapped
0
Gaps Found
100%
Coverage

A reviewed coverage crosswalk for this pair is available. See which NIST AI Risk Management Framework (AI RMF 1.0) controls you already evidence.

According to the TheArtOfService Compliance Knowledge Graph:

ISO/IEC 42001:2023 maps to NIST AI Risk Management Framework (AI RMF 1.0) with 100% coverage across 38 directly mapped controls. Analysis of 38 ISO/IEC 42001:2023 controls identifies 45 compliance gaps, primarily concentrated in Clause A – ISO/IEC 42001:2023.

Source: TheArtOfService Knowledge Graph | 38 controls analysed | 686 frameworks | 309K+ cross-framework mappings

Control Mappings

Showing 20 of 68 mapped controls across 9 domains. Sign up to explore all 309K+ mappings across 686 frameworks.

Annex A AIMS controls - A.10 Third-party and customer relationships(7 mappings)

iso-iec-42001-2023::A.10.2Allocating responsibilities
→AIRMF-GV-6.1Policies 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
iso-iec-42001-2023::A.10.3Suppliers3 targets
→AIRMF-GV-6.1Policies 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
→AIRMF-MN-3.1AI risks and benefits from third-party resources are regularly monitored, and risk controls are applied and documented
→AIRMF-MP-4.2Internal risk controls for components of the AI system including third-party AI technologies are identified and documented
iso-iec-42001-2023::A.10.4Customers3 targets
→AIRMF-MP-1.1Intended purpose, potentially beneficial uses, context-specific laws, norms and expectations, and prospective settings in which the AI system will be deployed are understood and documented
→AIRMF-MP-5.2Practices and personnel for supporting regular engagement with relevant AI actors and integrating feedback about positive, negative, and unanticipated impacts are in place and documented
→AIRMF-MS-4.1Measurement approaches for identifying AI risks are connected to deployment contexts and informed through consultation with domain experts and other end users, and approaches are documented

Annex A AIMS controls - A.2 Policies related to AI(4 mappings)

iso-iec-42001-2023::A.2.2AI policy2 targets
→AIRMF-GV-1.2The characteristics of trustworthy AI are integrated into organizational policies, processes, and procedures
→AIRMF-GV-1.4The risk management process and its outcomes are established through transparent policies, procedures, and other controls based on organizational risk priorities
iso-iec-42001-2023::A.2.3Alignment with other organizational policies
→AIRMF-GV-1.2The characteristics of trustworthy AI are integrated into organizational policies, processes, and procedures
iso-iec-42001-2023::A.2.4Review of the AI policy
→AIRMF-GV-1.5Ongoing monitoring and periodic review of the risk management process and its outcomes are planned, organizational roles and responsibilities are clearly defined, including determining the frequency of periodic review

Annex A AIMS controls - A.3 Internal organization(3 mappings)

iso-iec-42001-2023::A.3.2AI roles and responsibilities2 targets
→AIRMF-GV-2.1Roles and responsibilities and lines of communication related to mapping, measuring, and managing AI risks are documented and are clear to individuals and teams throughout the organization
→AIRMF-GV-2.3Executive leadership of the organization takes responsibility for decisions about risks associated with AI system development and deployment
iso-iec-42001-2023::A.3.3Reporting of concerns
→AIRMF-GV-4.1Organizational policies and practices are in place to foster a critical thinking and safety-first mindset in the design, development, deployment, and uses of AI systems to minimize negative impacts

Annex A AIMS controls - A.4 Resources for AI systems(6 mappings)

iso-iec-42001-2023::A.4.2Resource documentation2 targets
→AIRMF-GV-1.6Mechanisms are in place to inventory AI systems and are resourced according to organizational risk priorities
→AIRMF-MN-2.1Resources required to manage AI risks are taken into account, along with viable non-AI alternative systems, approaches, or methods, to reduce the magnitude or likelihood of potential impacts
iso-iec-42001-2023::A.4.3Data resources
→AIRMF-MS-2.1Test sets, metrics, and details about the tools used during test, evaluation, validation, and verification are documented
iso-iec-42001-2023::A.4.4Tooling resources
→AIRMF-MS-2.1Test sets, metrics, and details about the tools used during test, evaluation, validation, and verification are documented
iso-iec-42001-2023::A.4.5System and computing resources
→AIRMF-MS-2.12Environmental impact and sustainability of AI model training and management activities as identified in the MAP function are assessed and documented
iso-iec-42001-2023::A.4.6Human resources
→AIRMF-GV-2.2The 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

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Coverage crosswalk

The ISO/IEC 42001:2023 to NIST AI Risk Management Framework (AI RMF 1.0) crosswalk

The table above lists candidate mappings. A crosswalk answers the narrower question you are probably here for: which NIST AI Risk Management Framework (AI RMF 1.0) controls your existing ISO/IEC 42001:2023 work already satisfies, which are real gaps, and the reasoning behind every claim so you can check it. One pair, $299, one time.

Coverage does not run both ways. ISO/IEC 42001:2023 into NIST AI Risk Management Framework (AI RMF 1.0) lands at 47.2%, while NIST AI Risk Management Framework (AI RMF 1.0) into ISO/IEC 42001:2023 lands at 78.9%, on the same two control sets. That is not a rounding difference. It is the whole reason these are two separate reports: one asks what your ISO/IEC 42001:2023 evidence buys you for NIST AI Risk Management Framework (AI RMF 1.0), the other asks the reverse.

ISO/IEC 42001:2023 into NIST AI Risk Management Framework (AI RMF 1.0)
47.2%

34 of 72 NIST AI Risk Management Framework (AI RMF 1.0) controls are evidenced by work you have already done for ISO/IEC 42001:2023. 38 are genuine gaps.

77.8%MAP - NIST AI RMF 1.0
47.4%GOVERN - NIST AI RMF 1.0
27.3%MEASURE - NIST AI RMF 1.0
38.5%MANAGE - NIST AI RMF 1.0
Machine verified. Claude Code on the Max plan, judged in context, signed off 2026-08-20. 52 candidate mappings were examined and 0 were removed by a pass whose job was to argue against them.Mappings were judged by Claude Code rather than read line by line by a practitioner. Every claim shows its reasoning so you can check it. Ask and a practitioner will review this pair.

A sample of what the report says

Evidenced: AIRMF-GV-1.2 The characteristics of trustworthy AI are integrated into organizational policies,...

A documented AI policy aligned to strategic direction is the integration into organisational policy.

Grounded in iso-iec-42001-2023::A.2.2 AI policy. Confidence high, survived the refutation pass.

Gap: AIRMF-GV-1.1 Legal and regulatory requirements involving AI are understood, managed, and documented

Legal and regulatory requirements involving AI are understood, managed, and documented. The organisation identifies which AI-specific and AI-adjacent legal duties bind each system, records how each is discharged, and keeps that record...

Every one of the 34 evidenced controls and 38 gaps in the report carries this much reasoning, so you can check the claim rather than take it on trust.

NIST AI Risk Management Framework (AI RMF 1.0) into ISO/IEC 42001:2023
78.9%

30 of 38 ISO/IEC 42001:2023 controls are evidenced by work you have already done for NIST AI Risk Management Framework (AI RMF 1.0). 8 are genuine gaps.

77.8%Annex A AIMS controls - A.6 AI system life cycle
100%Annex A AIMS controls - A.5 Assessing impacts of AI systems
80%Annex A AIMS controls - A.7 Data for AI systems
100%Annex A AIMS controls - A.8 Information for interested parties of AI systems
Machine verified. Claude Code on the Max plan, judged in context, signed off 2026-08-20. 39 candidate mappings were examined and 0 were removed by a pass whose job was to argue against them.Mappings were judged by Claude Code rather than read line by line by a practitioner. Every claim shows its reasoning so you can check it. Ask and a practitioner will review this pair.

A sample of what the report says

Evidenced: A.10.2 Allocating responsibilities

Contract terms addressing data rights and liability for AI components allocate responsibility across parties.

Grounded in AIRMF-GV-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. Confidence high, survived the refutation pass.

Gap: A.2.3 Alignment with other organizational policies

The AI policy shall be aligned with other organizational policies (privacy, security, quality, ethics, HR).

Every one of the 30 evidenced controls and 8 gaps in the report carries this much reasoning, so you can check the claim rather than take it on trust.

ISO/IEC 42001:2023 to NIST AI Risk Management Framework (AI RMF 1.0)
$299
per framework pair, one time
  • Every evidenced control, with the reasoning behind it
  • Every gap, with what it requires
  • Its level of review stated plainly, not a bare number

Why this page shows two different percentages. The 100% in the header counts how many ISO/IEC 42001:2023 controls carry at least one candidate mapping in the graph, before any review. The crosswalk percentage counts something stricter: how many NIST AI Risk Management Framework (AI RMF 1.0) controls are actually evidenced, after a pass that argued against each mapping and kept only what survived. They answer different questions and they are not meant to agree.

A crosswalk narrows the work. It does not replace an audit, and your assessor may take a different view on individual controls. Mappings between frameworks are judgements, not text printed in either standard, which is why every claim in the report shows its reasoning. Questions go to support@theartofservice.com.

Related Comparisons

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Other NIST AI Risk Management Framework (AI RMF 1.0) comparisons

EU AI Act vs NIST AI Risk Management Framework (AI RMF 1.0)62 mappingsAdministrative Measures for the Security Assessment of Generative AI Services (2023) and Algorithmic Recommendation Management Provisions (2022) vs NIST AI Risk Management Framework (AI RMF 1.0)10 mappingsASEAN Guide on AI Governance and Ethics vs NIST AI Risk Management Framework (AI RMF 1.0)9 mappingsCanada Artificial Intelligence and Data Act (AIDA) vs NIST AI Risk Management Framework (AI RMF 1.0)6 mappingsColorado Artificial Intelligence Act (proposed SB 24-205) vs NIST AI Risk Management Framework (AI RMF 1.0)4 mappingsAustralia AI Ethics Framework vs NIST AI Risk Management Framework (AI RMF 1.0)2 mappingsISO/IEC 23894:2023 vs NIST AI Risk Management Framework (AI RMF 1.0)1 mappings

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What are the key differences between ISO/IEC 42001:2023 and NIST AI Risk Management Framework (AI RMF 1.0)?

ISO/IEC 42001:2023 has 38 controls across its framework, while NIST AI Risk Management Framework (AI RMF 1.0) covers 72 controls. Direct mapping analysis identifies 38 overlapping controls (100% coverage). The frameworks diverge most significantly in Clause A – ISO/IEC 42001:2023, where 9 ISO/IEC 42001:2023 controls have no direct NIST AI Risk Management Framework (AI RMF 1.0) equivalent.

How many controls map between ISO/IEC 42001:2023 and NIST AI Risk Management Framework (AI RMF 1.0)?

Of 38 total ISO/IEC 42001:2023 controls, 38 map directly to NIST AI Risk Management Framework (AI RMF 1.0) controls, representing 100% coverage. The remaining 45 controls represent compliance gaps requiring additional documentation or compensating controls to satisfy both frameworks simultaneously.

What are the compliance gaps when mapping ISO/IEC 42001:2023 to NIST AI Risk Management Framework (AI RMF 1.0)?

45 ISO/IEC 42001:2023 controls have no direct equivalent in NIST AI Risk Management Framework (AI RMF 1.0). The highest concentration of gaps is in Clause A – ISO/IEC 42001:2023 with 9 unmapped controls. These gaps represent areas where additional controls, policies, or documentation must be created to achieve compliance with both frameworks.

Which control domains have the most gaps between ISO/IEC 42001:2023 and NIST AI Risk Management Framework (AI RMF 1.0)?

The domain with the highest gap count is Clause A – ISO/IEC 42001:2023 (9 gaps). Export the full domain-by-domain gap breakdown via the Professional tier to generate a prioritised remediation roadmap.

Related Resources

How to Map Controls Across Frameworks|Multi-Framework Compliance Guide|Compliance Glossary

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