AI Agent Audit Log Checklist: What Your AI Agent Should Log

An AI agent audit log is a permanent record of what an AI agent did: what it was asked, which tools it called, what it produced, and who was involved. Good logs let you explain, debug, and prove the behavior of an agent long after it runs.

This page is a working checklist, not an overview. Use the checklist below to decide exactly what your agent should log, how long to retain each record, and how to lock the logs down. The final sections map every item to SOC 2 and EU AI Act evidence. Download the PDF from the callout to run this as a live audit.

Why AI Agents Need Audit Logs

AI agents need audit logs because they take actions, not just answer questions, and every action needs to be explainable after the fact.

When an agent sends an email, updates a record, or approves a request, you must be able to reconstruct why. Logs give you that record for debugging, security, customer trust, and compliance.

• Debugging: trace exactly what the agent did when something goes wrong.
• Security: detect misuse, prompt injection, or unauthorized access.
• Accountability: show who or what triggered a decision.
• Compliance: provide the evidence auditors ask for under SOC 2 and the EU AI Act.

Checklist — Events Your Agent Must Log

Log every event below for each agent run so you can reconstruct the full sequence of what happened.

Tick each item. If your agent does not capture it today, that is a gap to close.

• Inputs and prompts: the user request and the full prompt sent to the model (redact sensitive fields where needed).
• System context: the system prompt, instructions, and any retrieved documents used.
• Tool calls: every tool or API the agent called, with the parameters it passed.
• Tool results: what each tool returned to the agent.
• Outputs: the agent’s final response or action.
• Model and version: the exact model name and version used for the run.
• User / actor: the person or system that triggered the run, with their identity.
• Timestamps: start and end time for the run and for each tool call.
• Decisions and approvals: any decision made and any human approval or override.
• Errors and retries: failures, timeouts, retries, and how they were handled.
• PII access: a record whenever the agent read or wrote personal data, and which fields.
• Session / trace ID: a unique ID that links every event in one run together.

Checklist — Retention Periods

Set a clear retention period for each log type so you keep evidence long enough without holding data forever.

These are common defaults. Match them to your industry rules and your data minimization duties under GDPR.

• Operational / debug logs: retain [30–90 days].
• Security and access logs: retain [1 year] to support investigations.
• Compliance evidence (decisions, approvals, PII access): retain [1–3 years] or as your regulator requires.
• High-risk AI system logs (EU AI Act): retain for at least the period the regulation requires, often [6 months minimum] and longer by sector.
• Deletion: automatically delete logs when their retention period ends, and log the deletion itself.
• Minimization: do not store full PII in logs when a reference or hash will do.

Checklist — Access Controls on Logs

Restrict who can read, change, or delete audit logs so the logs themselves stay trustworthy.

A log that anyone can edit is not evidence.

• Role-based access: only named roles can read audit logs, and even fewer can export them.
• Immutability: store logs so they cannot be edited or deleted before their retention period ends (write-once or append-only).
• Separation of duties: the people who run the agents should not be able to alter the logs.
• Encryption: encrypt logs at rest and in transit.
• Meta-logging: log every access to the logs themselves.
• Backups: back up logs and test that you can restore them.

Checklist — Review Cadence

Review your logs on a set schedule so problems surface early instead of during an incident.

Logs only add value when someone actually looks at them.

• Automated alerts: flag errors, unusual tool calls, and PII access in real time.
• Weekly: review error and retry trends and any flagged events.
• Monthly: sample agent runs and confirm decisions and approvals were logged correctly.
• Quarterly: review access to the logs and confirm retention and deletion are working.
• After incidents: review the full trace and record lessons learned.

How Logs Map to SOC 2 Evidence

Your audit logs supply direct evidence for several SOC 2 Trust Services Criteria.

Auditors want proof that controls work, and logs are that proof.

• Security: access logs and immutability show you control and monitor access to systems and data.
• Availability: error, retry, and uptime logs show you monitor and respond to failures.
• Processing integrity: input, output, and decision logs show the agent processed data correctly and completely.
• Confidentiality: PII access logs and encryption show you protect sensitive data.
• Monitoring: your review cadence and alerts show ongoing control operation, which SOC 2 auditors sample.

How Logs Map to EU AI Act Evidence

Your audit logs help meet the record-keeping and human-oversight duties in the EU AI Act, especially for high-risk systems.

The Act expects providers and deployers of high-risk AI to keep automatic logs and enable oversight.

• Record-keeping: automatic logging of events over the system’s lifetime supports the Act’s logging requirement for high-risk AI.
• Traceability: session IDs and timestamps let you trace an outcome back to its inputs and model version.
• Human oversight: decision and approval logs show a human was able to review and override the AI.
• Transparency: model and version logs support your duty to document how the system works.
• Incident tracking: error and PII-access logs support reporting duties when something goes wrong.