MP-3.2 Potential costs, including non-monetary costs, which result from expected or realized AI errors or system functionality and trustworthiness are examined and documented, as connected to organizational risk tolerance
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. C
4
artefacts
0
held by a system
0
at each review
hard
to go live
Document repository
where the evidence lives
teal = a system already holds it · olive = produced at each review
system holds itEvidence a system already holds
none for this control
periodic reviewEvidence produced at each review
none for this control
governing documentDocuments that govern the control
- Documented analysis of costs arising from system error or failure · Document repository
- 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
This control is evidenced by people and documents, not systems. Put the document under version control with an owner and review date, and log each review as a record with reviewer and date. Do not try to automate it.
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, and AI system categorization