MP-3.2 Potential costs, including non-monetary costs, which result from expected or realized AI errors or system…
Potential costs, including non-monetary costs, which result from expected or realized AI errors or system functionality and trustworthiness - as connected to organizational risk tolerance - are examined and documented. Costs of error are examined including non-monetary ones such as harm to individuals and communities, and connected explicitly to the tolerance the organisation has set.
Source: NIST AI RMF 1.0, MP-3.2. 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.
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
- Documented analysis of costs arising from system error or failure · Document repository
governing documentDocuments that govern the control
- Non-monetary costs identified, including harm to individuals and communities · Document repository
- The connection drawn between those costs and the stated risk tolerance · Document repository
- Input from parties outside the delivery team on what costs matter · Document repository
First move
Common gaps auditors find
- Only direct financial and remediation cost considered
- Costs listed with no reference to tolerance, so no threshold is crossed or not crossed
- Analysis performed once, before the deployment scope widened
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 sheetMP-3.1 Potential benefits of intended AI system functionality and performance are examined and documented · MP-3.3 Targeted application scope is specified and documented based on the system's capability, established context…