Audit Logs API Developer Documentation

An API audit is a structured review of API activity, behavior, and controls. It typically examines request history, response patterns, authentication events, usage trends, errors, and policy enforcement. Teams use API audits to investigate incidents, validate compliance processes, identify inefficiencies, and confirm that integrations are operating as expected across production environments.

An API log is a record of an API interaction or related system event. It can include timestamps, endpoints, request and response details, status codes, latency, authentication context, and metadata about the caller. API logs help developers troubleshoot issues, trace failures, monitor usage, and maintain a historical record of how systems are being accessed.

Audit logging is the practice of recording important system and user actions in a way that supports accountability and traceability. Unlike basic operational logs, audit logs focus on who did what, when it happened, and what changed. They are commonly used for security reviews, compliance checks, incident investigations, and internal governance across applications and APIs.

API logs are collections of records generated by API traffic and related events. They often capture requests, responses, errors, rate-limit events, authentication attempts, and performance data. In practice, API logs give teams the visibility needed to debug integrations, analyze usage patterns, monitor service health, and maintain an evidence trail for operational or compliance purposes.

The two common categories are system audit logs and user audit logs. System audit logs record automated events such as service actions, configuration changes, failures, or policy triggers. User audit logs capture human actions like logins, permission changes, API key usage, or administrative updates. Together, they provide a fuller picture of accountability and system behavior.

A useful API audit log should include timestamps, request identifiers, endpoint details, status codes, latency, caller identity, authentication context, and relevant metadata about the action performed. For stronger traceability, teams often also log policy decisions, access-control events, and error details. The goal is to create records that are searchable, consistent, and meaningful during debugging or reviews.

Audit logs provide event-level records that show exactly what happened, while monitoring metrics summarize system behavior over time. Metrics are ideal for spotting trends like rising latency or error rates, whereas audit logs help trace a specific request, user action, or configuration change. Most mature teams use both together for observability, incident response, and governance.

Audit logs are especially important for AI APIs because teams need visibility into model usage, request patterns, outputs, access controls, and provider behavior. They support debugging prompt or response issues, reviewing policy enforcement, tracking spend-related activity, and documenting operational decisions. In multi-provider environments, centralized audit logs also reduce blind spots created by fragmented tooling.