How should prompts, outputs and traces be governed in terms of retention and access?

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Logs of prompts and outputs often contain the most sensitive data in the system (user messages, retrieved documents, PII), so govern them like production data: classify them, redact PII before storage where possible, restrict access by role, set retention periods aligned with legal and business needs (shorter for raw payloads, longer for aggregated metrics), support deletion requests, encrypt them, and audit access. Also check the retention settings of your providers and observability vendors.

Policy example

Data	Retention	Access
Aggregated metrics (tokens, latency, costs)	2 years	Engineering, finance
Trace metadata (no payload)	90 days	Engineering
Redacted payload samples	30 days	AI team (need-to-know)
Raw payloads for flagged incidents	Per incident policy	Restricted, audited
Eval datasets from production	Until superseded; consented / anonymised	AI team

Also consider

User deletion requests: find and delete across logs, caches, eval sets and fine-tuning datasets (lineage needed).
Provider-side retention: enterprise settings for zero or limited retention.
Data residency: logs stored in the required region.
Training on logs: only with consent and policy approval.

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How should prompts, outputs and traces be governed in terms of retention and access?

Policy example

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