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NIST AI Risk Management Framework (AI RMF 1.0)vsISO/IEC 42001:2023

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

68
Controls Mapped
4
Gaps Found
72%
Coverage

A reviewed coverage crosswalk for this pair is available. See which ISO/IEC 42001:2023 controls you already evidence.

According to the TheArtOfService Compliance Knowledge Graph:

NIST AI Risk Management Framework (AI RMF 1.0) maps to ISO/IEC 42001:2023 with 72% coverage across 52 directly mapped controls. Analysis of 72 NIST AI Risk Management Framework (AI RMF 1.0) controls identifies 20 compliance gaps, primarily concentrated in MEASURE - NIST AI RMF 1.0.

Source: TheArtOfService Knowledge Graph | 72 controls analysed | 686 frameworks | 309K+ cross-framework mappings

Control Mappings

Showing 20 of 68 mapped controls across 4 domains. Sign up to explore all 309K+ mappings across 686 frameworks.

GOVERN - NIST AI RMF 1.0(18 mappings)

AIRMF-GV-1.2The characteristics of trustworthy AI are integrated into organizational policies, processes, and procedures2 targets
→iso-iec-42001-2023::A.2.2AI policy
→iso-iec-42001-2023::A.2.3Alignment with other organizational policies
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.2.2AI 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
→iso-iec-42001-2023::A.2.4Review of the AI policy
AIRMF-GV-1.6Mechanisms are in place to inventory AI systems and are resourced according to organizational risk priorities
→iso-iec-42001-2023::A.4.2Resource documentation
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
→iso-iec-42001-2023::A.3.2AI roles and responsibilities
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
→iso-iec-42001-2023::A.4.6Human resources
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.3.2AI roles and responsibilities
AIRMF-GV-3.2Policies and procedures are in place to define and differentiate roles and responsibilities for human-AI configurations and oversight of AI systems
→iso-iec-42001-2023::A.9.2Processes for responsible use of AI systems
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
→iso-iec-42001-2023::A.3.3Reporting of concerns
AIRMF-GV-4.2Organizational teams document the risks and potential impacts of the AI technology they design, develop, deploy, evaluate and use, and communicate about the impacts more broadly2 targets
→iso-iec-42001-2023::A.5.2AI system impact assessment process
→iso-iec-42001-2023::A.5.3Documentation of AI system impact assessments
AIRMF-GV-4.3Organizational practices are in place to enable AI testing, identification of incidents, and information sharing
→iso-iec-42001-2023::A.6.2.8AI system event logging
AIRMF-GV-5.1Organizational policies and practices are in place to collect, consider, prioritize, and integrate feedback from those external to the team that developed or deployed the AI system regarding the potential individual and societal impacts related to AI risks
→iso-iec-42001-2023::A.8.3External reporting
AIRMF-GV-5.2Mechanisms are established to enable AI actors to regularly incorporate adjudicated feedback from relevant AI actors into system design and implementation
→iso-iec-42001-2023::A.8.3External reporting
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 rights3 targets
→iso-iec-42001-2023::A.10.2Allocating responsibilities
→iso-iec-42001-2023::A.10.3Suppliers
→iso-iec-42001-2023::A.9.2Processes for responsible use of AI systems

MANAGE - NIST AI RMF 1.0(2 mappings)

AIRMF-MN-1.1A determination is made as to whether the AI system achieves its intended purpose and stated objectives and whether its development or deployment should proceed
→iso-iec-42001-2023::A.6.2.5AI system deployment
AIRMF-MN-1.3Responses to the AI risks deemed high priority as identified by the MAP function are developed, planned, and documented, and risk response options can include mitigating, transferring, avoiding, or accepting
→iso-iec-42001-2023::A.5.2AI system impact assessment process

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Coverage crosswalk

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

The table above lists candidate mappings. A crosswalk answers the narrower question you are probably here for: which ISO/IEC 42001:2023 controls your existing NIST AI Risk Management Framework (AI RMF 1.0) 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. NIST AI Risk Management Framework (AI RMF 1.0) into ISO/IEC 42001:2023 lands at 78.9%, while ISO/IEC 42001:2023 into NIST AI Risk Management Framework (AI RMF 1.0) lands at 47.2%, 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 NIST AI Risk Management Framework (AI RMF 1.0) evidence buys you for ISO/IEC 42001:2023, the other asks the reverse.

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 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) to ISO/IEC 42001:2023
$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 72% in the header counts how many NIST AI Risk Management Framework (AI RMF 1.0) controls carry at least one candidate mapping in the graph, before any review. The crosswalk percentage counts something stricter: how many ISO/IEC 42001:2023 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.

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Other ISO/IEC 42001:2023 comparisons

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

NIST AI Risk Management Framework (AI RMF 1.0) has 72 controls across its framework, while ISO/IEC 42001:2023 covers 38 controls. Direct mapping analysis identifies 52 overlapping controls (72% coverage). The frameworks diverge most significantly in MEASURE - NIST AI RMF 1.0, where 8 NIST AI Risk Management Framework (AI RMF 1.0) controls have no direct ISO/IEC 42001:2023 equivalent.

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

Of 72 total NIST AI Risk Management Framework (AI RMF 1.0) controls, 52 map directly to ISO/IEC 42001:2023 controls, representing 72% coverage. The remaining 20 controls represent compliance gaps requiring additional documentation or compensating controls to satisfy both frameworks simultaneously.

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

20 NIST AI Risk Management Framework (AI RMF 1.0) controls have no direct equivalent in ISO/IEC 42001:2023. The highest concentration of gaps is in MEASURE - NIST AI RMF 1.0 with 8 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 NIST AI Risk Management Framework (AI RMF 1.0) and ISO/IEC 42001:2023?

The domain with the highest gap count is MEASURE - NIST AI RMF 1.0 (8 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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