Authorized versus unauthorized AI usage
Metadata can reveal changed or coordinated AI usage, but it cannot infer permission when authorized and unauthorized activity look identical. This guide explains the boundary between behavioral ranking and an authorization decision.
Can a classifier determine permission from metadata?
Not when permission is absent from the input. Identical events, infrastructure, and customer context must produce identical scores regardless of the hidden label.
What should an operator do next?
Preserve the relevant time window, attach customer and deployment context, and make the smallest reversible response that contains credible risk. Record why a case opened so another investigator can reproduce the decision.
Where does this approach fail?
Behavioral metadata cannot identify intent or permission by itself. Legitimate launches, failover, automation, and registered relays can resemble misuse. Treat the output as a ranked investigation queue, then resolve authorization with stronger identity and reconciliation evidence.
Frequently asked questions
What should the system say instead?
Use verification-required, then consult delegation, owner confirmation, or reconciliation evidence.
Does InferTrail read prompts or responses?
No. The investigation design uses provider-visible metadata and customer context, with content collection governed separately if a customer requires it.
What is the right first action?
Open a verification case, preserve evidence, and check credential state before making a destructive enforcement decision.
Related guides
- LLM API key abuse detection
- AI gateway credential misuse
- Investigating unexplained AI usage spikes
- LiteLLM credential exposure response
- LLM token resale risk
- AI gateway provenance and delegation
- AI spend anomaly detection
- Authorized versus unauthorized AI usage
- LLM gateway security logging
- Credential sharing detection for AI platforms