EvidenceSheet
NIST AI RMF 1.0 · MAP - NIST AI RMF 1.0

MP-5.1 Likelihood and magnitude of each identified impact are identified and documented, based on expected use, past…

Likelihood and magnitude of each identified impact (both potentially beneficial and harmful) based on expected use, past uses of AI systems in similar contexts, public incident reports, feedback from those external to the team that developed or deployed the AI system, or other data are identified and documented. Each identified impact carries a likelihood and a magnitude derived from stated evidence, including comparable past deployments and public incident reports, not from unsupported estimation.

Source: NIST AI RMF 1.0, MP-5.1. 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

  • The evidence base cited for each estimate, including comparable systems and incident reports · Document repository

governing documentDocuments that govern the control

  • Impact register with likelihood and magnitude recorded per impact · Policy repository / GRC workspace
  • Beneficial as well as harmful impacts characterised · Document repository
  • The use of these estimates in a go or no-go decision · 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

MP-4.2 Internal risk controls for components of the AI system including third-party AI technologies are identified… · MP-5.2 Practices and personnel for supporting regular engagement with relevant AI actors and integrating feedback…