## The Problem
AI systems make decisions. Sometimes those decisions are wrong. When they are, you need to know:
- **What** decision was made
- **Why** the AI made that decision
- **Who** was affected
- **When** it happened
Without comprehensive audit logs, you're flying blind.
## What to Log
At minimum, every AI inference should capture:
1. **Input data** (or a hash/reference)
2. **Model version** used
3. **Output** generated
4. **Confidence scores** if applicable
5. **User context** (who triggered, on behalf of whom)
6. **Timestamp** with timezone
## Implementation Tips
- Use append-only storage for immutability
- Include log retention policies from day one
- Make logs queryable, not just archivable
- Plan for log volume—AI systems can be chatty
## Conclusion
Audit logs aren't overhead—they're insurance. Build them in from the start.