AI Security Logs: What to Record and What to Leave Out

Start with investigation questions

Determine what evidence is actually needed to detect abuse, reproduce failures, review tool execution or answer regulatory requests. Typically useful fields include event time, application version, model routing identifier, anonymized user reference, tool action type, decision status and approval record.

Separate metadata from sensitive content

Redact or tokenize personal data when feasible and do not put raw uploaded documents in default analytics. Where content capture is justified, limit it to targeted cases, minimize duration and restrict access to a small authorized group. Logs and analytics copies must appear in the data-flow map and deletion plan.

Make logs trustworthy

Use access controls, tamper detection, synchronized timestamps and documented retention. Test whether administrators can quietly alter or export sensitive records. Correlate incidents across prompt, retrieval and tool steps using opaque identifiers, not the user’s email address in every field.

Check third-party destinations

Monitoring vendors, error trackers and support tools are potential subprocessors. Their retention and geographic handling may differ from the primary model service. The ICO’s controller–processor contract guidance is relevant to UK-regulated data, while OWASP covers sensitive disclosure in LLM applications.

Practical rule

For a failed AI invoice approval, preserve the authorized invoice identifier, model release, rule check and human approver. A secret field or unrelated customer note should not appear in a general performance dashboard. Practice a deletion request and an incident reconstruction to verify both accountability and minimization.

Evidence checked 25 October 2026. Applicability: Global; UK privacy illustration. This material is general education, not legal, medical, investment or other professional advice. No vendor performance test is implied.