Cross-Framework Mapping

Administrative Measures for the Security Assessment of Generative AI Services (2023) and Algorithmic Recommendation Management Provisions (2022)vsNIST AI Risk Management Framework (AI RMF 1.0)

See exactly how Administrative Measures for the Security Assessment of Generative AI Services (2023) and Algorithmic Recommendation Management Provisions (2022) controls map to NIST AI Risk Management Framework (AI RMF 1.0). Pre-computed mappings, identified gaps, and coverage analysis.

25
Controls Mapped
23
Gaps Found
38%
Coverage

According to the TheArtOfService Compliance Knowledge Graph:

Administrative Measures for the Security Assessment of Generative AI Services (2023) and Algorithmic Recommendation Management Provisions (2022) maps to NIST AI Risk Management Framework (AI RMF 1.0) with 38% coverage across 18 directly mapped controls. Analysis of 48 Administrative Measures for the Security Assessment of Generative AI Services (2023) and Algorithmic Recommendation Management Provisions (2022) controls identifies 30 compliance gaps — primarily concentrated in Algorithm Recommendation.

Source: TheArtOfService Knowledge Graph | 48 controls analysed | 723 frameworks | 332K+ cross-framework mappings

Control Mappings

Showing 20 of 25 mapped controls across 4 domains. Sign up to explore all 332K+ mappings across 723 frameworks.

Algorithm Recommendation(9 mappings)

CN-ALG-A16Algorithm Transparency Disclosure to Users
AIRMF-GV-1.2Trustworthy AI characteristics are integrated into organisational policies, processes, and procedures
CN-ALG-A24Algorithm Filing
AIRMF-GV-4.1Organisational culture and incentives prioritise AI risk management
CN-ALG-A27Algorithm Security Assessment2 targets
AIRMF-GV-4.1Organisational culture and incentives prioritise AI risk management
AIRMF-MS-1.1Appropriate methods and metrics for measuring AI risk are identified and applied
CN-ALG-A28Audit Cooperation and Log Retention
AIRMF-MEA-03AI Transparency and Explainability
CN-ALG-A7Algorithm Security Management System2 targets
AIRMF-GV-1.1Legal and regulatory requirements involving AI are understood, managed, and documented
AIRMF-MN-1.1AI risks are prioritised and resources are allocated to manage them
CN-ALG-A8Periodic Algorithm Review (Anti-Addiction)2 targets
AIRMF-MN-2.1Mechanisms for tracking identified risks over time are in place
AIRMF-MS-2.1Test sets, evaluation criteria, and ongoing tracking are documented

Deep Synthesis(5 mappings)

CN-DS-A14Training Data Security and Biometric Consent
AIRMF-MP-2.1Categorisation of AI systems is performed
CN-DS-A15Security Assessment of Editing Functions
AIRMF-MS-1.1Appropriate methods and metrics for measuring AI risk are identified and applied
CN-DS-A19Deep Synthesis Filing
AIRMF-GV-4.1Organisational culture and incentives prioritise AI risk management
CN-DS-A20Security Assessment Before New Functions2 targets
AIRMF-MP-1.1Context of AI system use is established and understood
AIRMF-MS-1.1Appropriate methods and metrics for measuring AI risk are identified and applied

Ethics(1 mappings)

CN-ETH-REVScience and Technology Ethics Review
AIRMF-GV-1.1Legal and regulatory requirements involving AI are understood, managed, and documented

Generative AI Measures(5 mappings)

CN-GAI-A14Illegal Content Disposal and Reporting
AIRMF-MN-4.1AI risk management documentation and processes are improved continuously
CN-GAI-A17Security Assessment and Algorithm Filing2 targets
AIRMF-GV-4.1Organisational culture and incentives prioritise AI risk management
AIRMF-MS-1.1Appropriate methods and metrics for measuring AI risk are identified and applied
CN-GAI-A19Regulatory Inspection Cooperation
AIRMF-GV-1.2Trustworthy AI characteristics are integrated into organisational policies, processes, and procedures
CN-GAI-A4Content Compliance and Prohibited Content
AIRMF-MP-1.1Context of AI system use is established and understood

+5 more mappings

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Related Comparisons

Other Administrative Measures for the Security Assessment of Generative AI Services (2023) and Algorithmic Recommendation Management Provisions (2022) comparisons

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

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What are the key differences between Administrative Measures for the Security Assessment of Generative AI Services (2023) and Algorithmic Recommendation Management Provisions (2022) and NIST AI Risk Management Framework (AI RMF 1.0)?

Administrative Measures for the Security Assessment of Generative AI Services (2023) and Algorithmic Recommendation Management Provisions (2022) has 48 controls across its framework, while NIST AI Risk Management Framework (AI RMF 1.0) covers 52 controls. Direct mapping analysis identifies 18 overlapping controls (38% coverage). The frameworks diverge most significantly in Algorithm Recommendation, where 13 Administrative Measures for the Security Assessment of Generative AI Services (2023) and Algorithmic Recommendation Management Provisions (2022) controls have no direct NIST AI Risk Management Framework (AI RMF 1.0) equivalent.

How many controls map between Administrative Measures for the Security Assessment of Generative AI Services (2023) and Algorithmic Recommendation Management Provisions (2022) and NIST AI Risk Management Framework (AI RMF 1.0)?

Of 48 total Administrative Measures for the Security Assessment of Generative AI Services (2023) and Algorithmic Recommendation Management Provisions (2022) controls, 18 map directly to NIST AI Risk Management Framework (AI RMF 1.0) controls — representing 38% coverage. The remaining 30 controls represent compliance gaps requiring additional documentation or compensating controls to satisfy both frameworks simultaneously.

What are the compliance gaps when mapping Administrative Measures for the Security Assessment of Generative AI Services (2023) and Algorithmic Recommendation Management Provisions (2022) to NIST AI Risk Management Framework (AI RMF 1.0)?

30 Administrative Measures for the Security Assessment of Generative AI Services (2023) and Algorithmic Recommendation Management Provisions (2022) controls have no direct equivalent in NIST AI Risk Management Framework (AI RMF 1.0). The highest concentration of gaps is in Algorithm Recommendation with 13 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 Administrative Measures for the Security Assessment of Generative AI Services (2023) and Algorithmic Recommendation Management Provisions (2022) and NIST AI Risk Management Framework (AI RMF 1.0)?

The domain with the highest gap count is Algorithm Recommendation (13 gaps). Export the full domain-by-domain gap breakdown via the Professional tier to generate a prioritised remediation roadmap.

This platform provides educational compliance tools, not legal, regulatory, or professional compliance advice. Cross-framework mappings are AI-assisted interpretations and do not reproduce or replace official standards. Framework names and trademarks belong to their respective owners. Consult qualified professionals for your specific compliance requirements. See our Terms of Service.