Security and observability
AI API Request Logging Redaction Checklist for SaaS Teams
A practical checklist for logging AI API requests safely: redact prompts, keys, tool payloads, customer identifiers, provider traces, and billing metadata without losing observability.
Why AI API logs need a stricter policy
AI API traffic carries more sensitive data than ordinary REST calls. A single request can include customer prompts, uploaded document snippets, tool arguments, internal knowledge-base text, user identifiers, API keys, provider request ids, model choices, and billing metadata. If everything is written to a generic application log, the observability layer can become the highest-risk data store in the product.
The goal is not to stop logging. SaaS teams still need enough detail to debug latency, routing, fallback, quota decisions, and cost anomalies. The safer pattern is to record operational facts by default, keep sensitive payloads out of routine logs, and require explicit short-lived access for deeper troubleshooting.
What to never log in plaintext
| Data type | Risk | Safer default |
|---|---|---|
| Customer API keys and provider keys | Credential leakage and account takeover | Store only a hash and a short prefix such as sk_live_abc.... |
| Raw prompts and completions | PII, trade secrets, legal data, source code, support transcripts | Disable by default; sample only with explicit tenant/admin consent and retention limits. |
| Tool call arguments | Can contain emails, addresses, order ids, database filters, or webhook secrets | Schema-aware redaction for known fields plus size-limited summaries. |
| Files and RAG context chunks | Often contain proprietary documents or customer records | Log document ids, chunk ids, token counts, and retrieval scores, not full text. |
| Provider traces and error bodies | May echo request content or account details | Normalize provider errors into safe categories before persistence. |
Recommended request log schema
For routine observability, store operational metadata that helps debugging and billing without exposing payload content: tenant id, workspace id, customer key id prefix, model alias, resolved provider, route policy id, fallback attempt count, input/output token counts, estimated cost, settled cost, quota decision, latency, status code, provider request id hash, and error category.
This schema works well with an AI API usage ledger: logs explain what happened operationally, while the ledger stays immutable for billing and reconciliation.
Redaction checklist
- Redact before persistence. Do not rely on dashboards or log viewers to hide sensitive fields after they are already stored.
- Use allowlists for structured fields. Prefer explicitly permitted metadata over trying to block every possible sensitive key name.
- Hash stable identifiers when raw values are not needed. This keeps joinability for debugging without exposing emails, API keys, or provider ids.
- Separate billing records from payload debugging. Cost, quota, balance, and invoice fields should not require retaining prompts.
- Make raw payload capture temporary. If a customer asks for deep debugging, enable a scoped capture window with retention, access logs, and an automatic shutdown time.
- Normalize provider errors. Map raw provider responses into safe error classes such as auth, rate_limit, model_unavailable, content_policy, timeout, or billing_block.
Debugging without sensitive logs
Most production incidents can be investigated with metadata: a spike in fallback ratio, a specific customer key prefix, a model alias, a provider route, a quota policy id, and hourly token spend. Pair this with a cost anomaly runbook and a clear API key rotation policy so incidents do not turn into data exposure events.
Retention and access rules
- Keep routine request metadata long enough for billing disputes and reliability analysis.
- Keep raw payload captures off by default and expire them quickly when temporarily enabled.
- Require role-based access for tenant-level logs and separate access for provider account data.
- Record who viewed sensitive debug sessions, why they were opened, and when they expired.
- Document what customers can export, delete, or redact from support-visible records.
A useful test: if a log export were attached to a support ticket, would it reveal customer prompts, credentials, private documents, or provider account secrets? If yes, the default logging policy is too broad.
Related: pair redacted request logs with AI API admin audit logs so policy changes, exports, and refunds are reviewable without leaking secrets.