MP-5.1 Likelihood and magnitude of each identified impact are identified and documented, based on expected use, past uses of AI systems in similar contexts, public incident reports, feedback from those external to the team, or other data
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 th
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
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
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
- Likelihood assigned by consensus in a workshop with no evidence cited
- Only harmful impacts characterised, so trade-offs cannot be weighed
- Estimates produced and never used in any decision
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-4.2 Internal risk controls for components of the AI system including third-party AI technologies are identified and documented · MP-5.2 Practices and personnel for supporting regular engagement with relevant AI actors and integrating feedback about positive, negative, and unanticipated impacts are in place and documented