Cross-Framework Mapping

NIST AI Risk Management Framework (AI RMF 1.0)vsOwn Risk and Solvency Assessment (ORSA) - NAIC Model Act

See exactly how NIST AI Risk Management Framework (AI RMF 1.0) controls map to Own Risk and Solvency Assessment (ORSA) - NAIC Model Act. Pre-computed mappings, identified gaps, and coverage analysis.

8
Controls Mapped
44
Gaps Found
10%
Coverage

According to the TheArtOfService Compliance Knowledge Graph:

NIST AI Risk Management Framework (AI RMF 1.0) maps to Own Risk and Solvency Assessment (ORSA) - NAIC Model Act with 10% coverage across 5 directly mapped controls. Analysis of 52 NIST AI Risk Management Framework (AI RMF 1.0) controls identifies 47 compliance gaps — primarily concentrated in AI RMF Functions.

Source: TheArtOfService Knowledge Graph | 52 controls analysed | 718 frameworks | 332K+ cross-framework mappings

Control Mappings

Showing 8 of 8 mapped controls across 1 domains. Sign up to explore all 332K+ mappings across 718 frameworks.

AI RMF Functions(8 mappings)

AIRMF-GOV-01AI Risk Management Policies
ORSA-S1ORSA Manual Section 1: Description of Insurer's Risk Management Framework
AIRMF-MAN-01AI Risk Treatment
ORSA-S1ORSA Manual Section 1: Description of Insurer's Risk Management Framework
AIRMF-MAP-02AI Risk Identification2 targets
ORSA-S1ORSA Manual Section 1: Description of Insurer's Risk Management Framework
ORSA-S2ORSA Manual Section 2: Insurer's Assessment of Risk Exposure
AIRMF-MAP-03AI Impact Assessment2 targets
ORSA-S1ORSA Manual Section 1: Description of Insurer's Risk Management Framework
ORSA-S2ORSA Manual Section 2: Insurer's Assessment of Risk Exposure
NIST-AI600-MAP-2Stakeholder Impact Assessment2 targets
ORSA-S1ORSA Manual Section 1: Description of Insurer's Risk Management Framework
ORSA-S2ORSA Manual Section 2: Insurer's Assessment of Risk Exposure

Related Comparisons

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

Other Own Risk and Solvency Assessment (ORSA) - NAIC Model Act comparisons

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What are the key differences between NIST AI Risk Management Framework (AI RMF 1.0) and Own Risk and Solvency Assessment (ORSA) - NAIC Model Act?

NIST AI Risk Management Framework (AI RMF 1.0) has 52 controls across its framework, while Own Risk and Solvency Assessment (ORSA) - NAIC Model Act covers 4 controls. Direct mapping analysis identifies 5 overlapping controls (10% coverage). The frameworks diverge most significantly in AI RMF Functions, where 26 NIST AI Risk Management Framework (AI RMF 1.0) controls have no direct Own Risk and Solvency Assessment (ORSA) - NAIC Model Act equivalent.

How many controls map between NIST AI Risk Management Framework (AI RMF 1.0) and Own Risk and Solvency Assessment (ORSA) - NAIC Model Act?

Of 52 total NIST AI Risk Management Framework (AI RMF 1.0) controls, 5 map directly to Own Risk and Solvency Assessment (ORSA) - NAIC Model Act controls — representing 10% coverage. The remaining 47 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 Own Risk and Solvency Assessment (ORSA) - NAIC Model Act?

47 NIST AI Risk Management Framework (AI RMF 1.0) controls have no direct equivalent in Own Risk and Solvency Assessment (ORSA) - NAIC Model Act. The highest concentration of gaps is in AI RMF Functions with 26 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 Own Risk and Solvency Assessment (ORSA) - NAIC Model Act?

The domain with the highest gap count is AI RMF Functions (26 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.