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NIST AI RMF · Subcategory 2.7

AI system security and resilience – as identified in the MAP function – are evaluated and documented.

Summary

AI system security and resilience – as identified in the MAP function – are evaluated and documented.

adversarialrisky emergent behaviorsecure and resilienttevvtrustworthy characteristics

Read the full official text: https://airc.nist.gov/airmf-resources/playbook/

← Subcategory 2.6

AI system is evaluated regularly for safety risks – as identified in the MAP function. The AI system to be deployed is demonstrated to be safe, its residual negative risk does not exceed the risk tolerance, and can fail safely, particularly if made to operate beyond its knowledge limits. Safety metrics implicate system reliability and robustness, real-time monitoring, and response times for AI system failures.

Subcategory 2.8 →

Risks associated with transparency and accountability – as identified in the MAP function – are examined and documented.

Track NIST AI RMF subcategory 2.7 as evidence

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More from NIST AI RMF

Subcategory 1.1

Legal and regulatory requirements involving AI are understood, managed, and documented.

Subcategory 1.1

A determination is made as to whether the AI system achieves its intended purpose and stated objectives and whether its development or deployment should proceed.

Subcategory 1.1

Intended 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. Considerations include: specific set or types of users along with their expectations; potential positive and negative impacts of system uses to individuals, communities, organizations, society, and the planet; assumptions and related limitations about AI system purposes; uses and risks across the development or product AI lifecycle; TEVV and system metrics.

Subcategory 1.1

Approaches and metrics for measurement of AI risks enumerated during the Map function are selected for implementation starting with the most significant AI risks. The risks or trustworthiness characteristics that will not – or cannot – be measured are properly documented.