MEASURE-4.2 Measurement results regarding AI system trustworthiness in deployment context(s) and across the AI lifecycle are informed by input from domain experts and relevant AI actors to validate whether the system is performing consistently as intended. Results are documented.
What the standard requires
measurement results regarding AI system trustworthiness in deployment context(s) and across AI lifecycle are informed by input from domain experts and other relevant AI actors to validate whether the system is performing consistently as intended. Results are documented.
Quoted verbatim from NIST AI Risk Management Framework (AI RMF 1.0), which is in the public domain.
Cross-framework mappings
No cross-framework mapping is seeded for this control. That is stated rather than filled in: a mapping nobody authored is worse than a gap you can see.
Integrations that carry evidence for this family
These connectors produce data relevant to NIST AI Risk Management Framework 1.0’s MEASURE family, which is the granularity CMDash publishes — there is no pre-built per-control mapping, and claiming one would be inventing it. You decide which evidence answers which control.
In CMDash this control is a row with an owner, a status, uploaded evidence kept with its full history, and an export an assessor can read.