AI systems generate new forms of data: prompts that include customer context, model outputs that may contain sensitive information, and tool traces that reveal operational details. If retention is unclear, risk accumulates silently—especially through logs and caches.
The goal is to balance three needs: operational debugging, regulatory compliance, and privacy/security risk reduction.
Classify AI telemetry as data
Start by defining what you store:
- Prompts and contexts. User inputs, retrieved evidence, and system instructions.
- Outputs. Model responses and refusal decisions.
- Tool traces. Arguments, results, and side effects.
- Metadata. Model versions, prompt versions, latency, token counts.
Not all telemetry needs the same retention. Metadata can often be retained longer than raw prompts.
Reduce what you store in the first place
Retention policy is not your first control. Data minimisation comes first:
- Redact or tokenise sensitive content before logging (see data minimisation).
- Prefer storing source IDs and citations over storing full retrieved content.
- Store structured fields rather than full text where possible.
Set retention by risk tier
A pragmatic approach is tiered retention:
- Low-risk metadata. Longer retention for auditing and FinOps.
- Moderate-risk content. Shorter retention with strong access controls.
- High-risk prompts. Minimise storage; keep only what is needed for incident investigation.
For tool-enabled agents, treat tool outputs as potentially sensitive operational data and restrict access accordingly (see tool authorisation).
Deletion must be real, not aspirational
Deletion policies often fail because data is duplicated across systems. Map where data goes:
- Application logs and tracing systems.
- Model provider logs and retention settings.
- Caches (see caching strategies).
- Analytics and BI exports.
Build deletion workflows that can locate and delete across those stores, and test them periodically.
Keep observability without storing everything
You do not need to store every prompt forever to operate safely. Focus on:
- Sampling strategies for debugging.
- Redacted prompt capture for incident replay.
- Strong version logging (model, prompt, policy) for reproducibility.
Well-designed retention and deletion controls reduce risk while preserving the ability to operate AI systems confidently.