MS-2.10 Privacy risk of the AI system as identified in the MAP function is examined and documented
Privacy risk of the AI system – as identified in the MAP function – is examined and documented. Privacy examination covers what the AI system makes possible, inference and re-identification from training data and outputs
4
artefacts
0
held by a system
1
at each review
hard
to go live
Policy repository / GRC workspace
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
- Assessment of re-identification and memorisation risk · Document repository
governing documentDocuments that govern the control
- Privacy risk examination covering training data, inference and outputs · HR system / LMS
- Privacy-enhancing measures applied and their assessed effect · Policy repository / GRC workspace
- Documentation traced to the privacy risks mapping identified · Policy repository / GRC workspace
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
- Privacy assessed as lawful basis for input data only
- Memorisation and training data extraction not considered
- Assessment completed for the original data set and not repeated after retraining
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 sheetMS-2.9 The AI model is explained, validated, and documented, and AI system output is interpreted within its context as identified in the MAP function and to inform responsible use and governance · MS-2.11 Fairness and bias as identified in the MAP function is evaluated and results are documented