Audit Trails & Explainability
Regulated industries require audit trails for every AI decision. Learn what to log (inputs, outputs, model version, latency, cost, tool calls), how to structure traces for querying, how to provide explainability to end users, and retention policies that balance compliance with cost.
Quick Reference
- →Log every LLM interaction: input, output, model version, latency, token count, cost, trace ID
- →Use structured logging (JSON) so traces are queryable — not unstructured text logs
- →Trace correlation: link every LLM call to the user request, session, and user ID
- →Explainability: store the chain of reasoning so you can explain any AI decision after the fact
- →Retention policies vary by regulation: GDPR requires deletion on request, SOC 2 requires minimum 1 year retention
- →Separate audit logs from application logs — audit logs are immutable and have different access controls
What to Log for AI Systems
AI audit trails capture more than traditional application logs. Beyond the request/response, you need to log the model version (which can change without notice), the full prompt (including system prompt and context), tool calls and their results, and the reasoning chain. This data serves three purposes: regulatory compliance, debugging production issues, and demonstrating fairness.
| Data Point | Why It Matters | Retention Consideration |
|---|---|---|
| Input (user message) | Reproduce and investigate any interaction | May contain PII — subject to deletion requests |
| Full prompt (system + context + user) | Understand what the model actually saw | May contain PII — redact before long-term storage |
| Output (model response) | Verify what was shown to the user | Keep for dispute resolution |
| Model name + version | Identify if a model update caused a regression | Small, keep indefinitely |
| System fingerprint | Detect silent model updates from providers | Small, keep indefinitely |
| Latency (TTFT, total) | SLA compliance, performance monitoring | Aggregate after 90 days |
| Token count (input + output) | Cost attribution, budget tracking | Aggregate after 90 days |
| Tool calls + results | Trace which tools were invoked and what they returned | May contain sensitive data — filter |
| Trace ID + parent ID | Correlate all calls in a single user interaction | Essential for debugging |
| User ID + session ID | Attribute interactions to users and sessions | Subject to deletion requests |
| Confidence score | Assess reliability of the AI's decision | Keep for fairness audits |
Full prompts often contain user data, proprietary system instructions, and business logic. Treat prompt logs with the same security as database backups: encrypted at rest, access-controlled, and subject to data retention policies. Do not store full prompts in general application logs.