MEASURE-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.
What the standard requires
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.
Quoted verbatim from NIST AI Risk Management Framework (AI RMF 1.0), which is in the public domain.
Maps to 1 control in 1 other framework
| Framework | Control |
|---|---|
| NIST Cybersecurity Framework 2.0 | ID.RA-05 Threats, vulnerabilities, likelihoods, and impacts are used to understand inherent risk and inform risk response prioritization |
Mappings are CMDash’s own, seeded with the product and shown on both frameworks they connect. In the product they mean evidence you attach here also counts against the controls listed above.
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.