Log Model Call Metadata Instead of Payloads to Reduce Data Leak Risk

The article argues that AI features often leak sensitive data through logs rather than request bodies, since logging receives less security scrutiny. It proposes logging only a "shape" record for each model call, including a payload digest, byte size, field names, latency, and attempt count, instead of message content. Encryption is deemed insufficient because logs are decrypted when searched, exported, or shared in support bundles. The approach is enforced with a CI test that scans log fixtures for disallowed fields and sensitive patterns such as emails, bearer tokens, and private keys. The author also recommends including a deliberately leaky fixture to confirm the scanner actually detects leaks.