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Cross-Framework Mapping

EU AI ActvsNIST AI Risk Management Framework (AI RMF 1.0)

See exactly how EU AI Act controls map to NIST AI Risk Management Framework (AI RMF 1.0). Pre-computed mappings, identified gaps, and coverage analysis.

82
Controls Mapped
0
Gaps Found
56%
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:

EU AI Act maps to NIST AI Risk Management Framework (AI RMF 1.0) with 56% coverage across 25 directly mapped controls. Analysis of 43 EU AI Act controls identifies 39 compliance gaps, primarily concentrated in EU AI Act - General-Purpose AI Models.

Source: TheArtOfService Knowledge Graph | 43 controls analysed | 686 frameworks | 310K+ cross-framework mappings

Control Mappings

Showing 20 of 82 mapped controls across 8 domains. Sign up to explore all 310K+ mappings across 686 frameworks.

EU AI Act - High-Risk Classification and Requirements(20 mappings)

EUAI-Art.10Data and data governance3 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-2.3Scientific integrity and TEVV considerations are identified and documented, including those related to experimental design, data collection and selection, system trustworthiness, and construct validation
→AIRMF-MS-2.11Fairness and bias as identified in the MAP function is evaluated and results are documented
EUAI-Art.11Technical documentation4 targets
→AIRMF-GV-1.1Legal and regulatory requirements involving AI are understood, managed, and documented
→AIRMF-MN-4.1Post-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
→AIRMF-MP-2.1The specific task, and methods used to implement the task, that the AI system will support is defined
→AIRMF-MS-2.1Test sets, metrics, and details about the tools used during test, evaluation, validation, and verification are documented
EUAI-Art.13Transparency and provision of information to deployers5 targets
→AIRMF-MN-1.4Negative residual risks, defined as the sum of all unmitigated risks, to both downstream acquirers of AI systems and end users are documented
→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-2.2Information about the AI system's knowledge limits and how system output may be utilized and overseen by humans is documented
→AIRMF-MS-2.8Risks associated with transparency and accountability as identified in the MAP function are examined and documented
→AIRMF-MS-2.9The AI model is explained, validated, and documented, and AI system output is interpreted within its context as identified in the MAP function and to inform responsible use and governance
EUAI-Art.14Human oversight4 targets
→AIRMF-GV-1.2The characteristics of trustworthy AI are integrated into organizational policies, processes, and procedures
→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
→AIRMF-MN-2.4Mechanisms 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
→AIRMF-MP-3.5Processes for human oversight are defined, assessed, and documented in accordance with organizational policies from the GOVERN function
EUAI-Art.15Accuracy, robustness and cybersecurity4 targets
→AIRMF-MS-1.1Approaches and metrics for measurement of AI risks enumerated during the MAP function are selected for implementation starting with the most significant AI risks, and the risks or trustworthiness characteristics that will not or cannot be measured are properly documented
→AIRMF-MS-2.5The AI system to be deployed is demonstrated to be valid and reliable, and limitations of the generalizability beyond the conditions under which the technology was developed are documented
→AIRMF-MS-2.6AI system is evaluated regularly for safety risks as identified in the MAP function, is demonstrated to be safe, its residual negative risk does not exceed the risk tolerance, and it can fail safely, particularly if made to operate beyond its knowledge limits
→AIRMF-MS-2.7AI system security and resilience as identified in the MAP function are evaluated and documented

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Coverage crosswalk

The EU AI Act 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 EU AI Act 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. EU AI Act into NIST AI Risk Management Framework (AI RMF 1.0) lands at 66.7%, while NIST AI Risk Management Framework (AI RMF 1.0) into EU AI Act lands at 25.6%, 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 EU AI Act evidence buys you for NIST AI Risk Management Framework (AI RMF 1.0), the other asks the reverse.

EU AI Act into NIST AI Risk Management Framework (AI RMF 1.0)
66.7%

48 of 72 NIST AI Risk Management Framework (AI RMF 1.0) controls are evidenced by work you have already done for EU AI Act. 24 are genuine gaps.

72.7%MEASURE - NIST AI RMF 1.0
72.2%MAP - NIST AI RMF 1.0
57.9%GOVERN - NIST AI RMF 1.0
61.5%MANAGE - NIST AI RMF 1.0
Machine verified. Claude Code on the Max plan, judged in context, signed off 2026-08-20. 72 candidate mappings were examined and 10 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-1.1 Legal and regulatory requirements involving AI are understood, managed, and documented

The recorded high-risk determination per system is documented understanding of which legal regime binds it.

Grounded in EUAI-Art.6 Classification rules for high-risk AI systems. Confidence high, survived the refutation pass.

Gap: AIRMF-GV-1.5 Ongoing monitoring and periodic review of the risk management process and its outcomes...

Ongoing 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. Monitoring and...

Every one of the 48 evidenced controls and 24 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 EU AI Act
25.6%

11 of 43 EU AI Act controls are evidenced by work you have already done for NIST AI Risk Management Framework (AI RMF 1.0). 32 are genuine gaps.

55.6%EU AI Act - High-Risk Classification and Requirements
16.7%EU AI Act - High-Risk Operator Obligations
50%EU AI Act - Post-Market Monitoring, Market Surveillance and Rights
50%EU AI Act - General Provisions and Prohibited Practices
Machine verified. Claude Code on the Max plan, judged in context, signed off 2026-08-20. 41 candidate mappings were examined and 9 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: EUAI-Art.10 Data and data governance

Documented data collection and selection with representativeness and suitability is the data governance limb.

Grounded in AIRMF-MP-2.3 Scientific integrity and TEVV considerations are identified and documented, including those related to experimental design, data collection and selection, system trustworthiness, and construct validation. Confidence high, survived the refutation pass.

Gap: EUAI-Art.11 Technical documentation

Technical documentation for a high-risk AI system shall be drawn up before the system is placed on the market or put into service and kept up to date. It shall be drawn up in such a way as to demonstrate that the high-risk AI system...

Every one of the 11 evidenced controls and 32 gaps in the report carries this much reasoning, so you can check the claim rather than take it on trust.

EU AI Act 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 56% in the header counts how many EU AI Act 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.

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What are the key differences between EU AI Act and NIST AI Risk Management Framework (AI RMF 1.0)?

EU AI Act has 43 controls across its framework, while NIST AI Risk Management Framework (AI RMF 1.0) covers 72 controls. Direct mapping analysis identifies 25 overlapping controls (56% coverage). The frameworks diverge most significantly in EU AI Act - General-Purpose AI Models, where 7 EU AI Act controls have no direct NIST AI Risk Management Framework (AI RMF 1.0) equivalent.

How many controls map between EU AI Act and NIST AI Risk Management Framework (AI RMF 1.0)?

Of 43 total EU AI Act controls, 25 map directly to NIST AI Risk Management Framework (AI RMF 1.0) controls, representing 56% coverage. The remaining 39 controls represent compliance gaps requiring additional documentation or compensating controls to satisfy both frameworks simultaneously.

What are the compliance gaps when mapping EU AI Act to NIST AI Risk Management Framework (AI RMF 1.0)?

39 EU AI Act controls have no direct equivalent in NIST AI Risk Management Framework (AI RMF 1.0). The highest concentration of gaps is in EU AI Act - General-Purpose AI Models with 7 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 EU AI Act and NIST AI Risk Management Framework (AI RMF 1.0)?

The domain with the highest gap count is EU AI Act - General-Purpose AI Models (7 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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