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NIST AI Risk Management Framework - Footnote 8

Verdictcontradicted30%
1 check · 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.

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contradicted30%Haiku 4.5 · 4/3/2026

NoteWRONG 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.

Case № page:nist-ai-rmf:fn8Filed 4/3/2026Confidence 30%
Source Check: NIST AI Risk Management Framework - Footnote 8 | Longterm Wiki