Logging AI usage without creating a new breach target
Work backwards from what an audit actually asks to see.
AI Tools Hub Editorial · published · updated
Pick one purpose first
| Purpose | Data needed | Retention |
|---|---|---|
| Cost control | Who, how often, which model | Match the accounting period |
| Policy violation detection | Records of suspected misuse | As long as an investigation needs |
| Incident response | Input and output at the time | Cover the likely discovery lag |
| Quality improvement | Output ratings and corrections | Match the improvement cycle |
What not to keep
Full prompts retained indefinitely accumulate personal data, customer data and unannounced business information. The impact of that store leaking can exceed the risk of the original usage. Keep metadata — who, when, which model, how many tokens — which covers cost and adoption analysis entirely.
When you do need full text
- Cap retention at 30 to 90 days with automatic deletion
- Name the individuals who may read it
- Log the reads themselves
- Store it separately, assuming it contains personal data
Observability tools in production
Langfuse and Helicone store traces including inputs and outputs. That is useful in development and reproduces the whole problem if you point it at production user data with the same settings. Most such tools can disable payload capture while keeping cost and latency analysis.
FAQ
What retention is defensible?
One to two years of metadata, 30 to 90 days of full text, as a working default. Sector regulation overrides this.