Logging
Logs are the memory of an AI system. They record events such as inputs, retrieved documents, model calls, tool calls, scores, thresholds, outputs, overrides, user actions, and errors. Good logs help teams debug, audit, monitor, and investigate incidents.
Logging is also a privacy and security risk. Raw prompts, documents, or user data can contain sensitive information. Transparent logging means recording what is needed for accountability while minimizing unnecessary exposure.
Design the Log Schema
For each event field, choose whether to log it in full, as a reference (hash, redacted excerpt, or pointer), or omit it entirely. Watch forensic usefulness and privacy exposure move together.
A useful AI trace is structured. Instead of one giant text blob, store event type, timestamp, actor, model or prompt version, input reference, retrieved sources, tool name, tool arguments where safe, output reference, and policy decision. This makes later search and replay possible.
Retention should match purpose and risk. Keep enough information to investigate foreseeable disputes, but avoid keeping sensitive data forever just because storage is cheap.
Log References When Raw Data Is Too Sensitive
Sometimes the best log stores a stable reference, hash, feature summary, or redacted excerpt instead of the full sensitive input. The key is preserving accountable reconstruction without reckless collection.
What makes an AI log useful for a later dispute?