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NIST AI Risk Management Framework - Footnote 8

contradicted30% confidence

1 evidence check

Last checked: 4/3/2026

WRONG NUMBERS: The claim states that the research analyzed 499 publicly reported generative AI incidents, which is accurate. However, it then claims that analysis of 133 documented AI incidents from 2025 demonstrates that frameworks like NIST AI RMF, when properly implemented with controls aligned to ISO/IEC 42001, can provide 100% classification coverage of known incident types. This is not supported by the source text. UNSUPPORTED: The source does not mention the analysis of 133 documented AI incidents from 2025. UNSUPPORTED: The source does not mention that frameworks like NIST AI RMF, when properly implemented with controls aligned to ISO/IEC 42001, can provide 100% classification coverage of known incident types.

Evidence — 1 source, 1 check

contradicted30%Haiku 4.5 · 4/3/2026
Found: Recent research analyzing 499 publicly reported generative AI incidents provides empirical evidence of sociotechnical failure modes that the framework addresses. Analysis of 133 documented AI incident

Note: WRONG NUMBERS: The claim states that the research analyzed 499 publicly reported generative AI incidents, which is accurate. However, it then claims that analysis of 133 documented AI incidents from 2025 demonstrates that frameworks like NIST AI RMF, when properly implemented with controls aligned to ISO/IEC 42001, can provide 100% classification coverage of known incident types. This is not supported by the source text. UNSUPPORTED: The source does not mention the analysis of 133 documented AI incidents from 2025. UNSUPPORTED: The source does not mention that frameworks like NIST AI RMF, when properly implemented with controls aligned to ISO/IEC 42001, can provide 100% classification coverage of known incident types.

Debug info

Record type: citation

Record ID: page:nist-ai-rmf:fn8

Source Check: NIST AI Risk Management Framework - Footnote 8 | Longterm Wiki