EvidenceSheet
NIST AI RMF 1.0 · MEASURE - NIST AI RMF 1.0

MS-4.2 Measurement results regarding AI system trustworthiness in deployment contexts and across the AI lifecycle…

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. Measured results are validated against the judgement of people who know the context, so a result inside its operational limits on paper is confirmed as adequate in practice.

Source: NIST AI RMF 1.0, MS-4.2. The statement above is our statement of the clause, read from the published text and cited so you can check it against the official text, not the instrument verbatim. Open NIST AI RMF 1.0 in the compliance library.

4
artefacts
0
held by a system
1
at each review
hard
to go live
Document repository
where the evidence lives
teal = a system already holds it · olive = produced at each review. To go live: easy when half or more of the artefacts are already held by a system, moderate from a quarter to a half, hard below a quarter.

These are the artefacts auditors commonly ask for on this requirement, our guidance rather than text from the standard. Your assessor decides what is sufficient in your context, and asks for samples across the period when a report covers one.

system holds itEvidence a system already holds

none for this control

periodic reviewEvidence produced at each review

  • Measurement results with domain expert and AI actor input recorded against them · Document repository

governing documentDocuments that govern the control

  • The pre-defined operational limits the results are judged against · Document repository
  • Documentation of whether the system is performing consistently as intended · Document repository
  • Disposition of any expert view that conflicted with the measured result · Document repository

First move

No system holds this evidence for you: it is people and documents. Reminders and routing to the owner still save time. Put the document under version control with an owner and review date, and log each review as a record with reviewer and date.

Common gaps auditors find

Do this for your whole sheet

Paste the rows you run your controls from and get this mapping for every control at once, with the periodic-review ones flagged and a first move per row. No account for the first run.

Build my evidence sheet

MS-4.1 Measurement approaches for identifying AI risks are connected to deployment contexts and informed through… · MS-4.3 Measurable performance improvements or declines based on consultations with relevant AI actors including…