How to Audit What Your AI Has Been Doing

What an AI Audit Covers

A thorough AI audit examines four areas: actions taken, decisions made, data accessed, and rules followed. For each AI agent in your organization, you should be able to answer what it did during the audit period, why it made the choices it made, what information it used to make those choices, and whether any of its actions violated your governance rules.

The depth of the audit depends on the risk level. A customer-facing AI agent that sends emails on behalf of your company warrants detailed review of every communication. An internal AI that categorizes support tickets might only need a statistical review of accuracy rates with spot checks of individual categorizations.

Setting Up Audit-Ready Logging

Auditing is only possible if your AI system logs the right information. Before you can audit, you need logging that captures:

• Action logs: What the AI did, including the specific action type, the target (which customer, which system, which record), and the timestamp.
• Decision logs: Why the AI chose a particular action, including the confidence score, the rules that applied, and the data points it considered.
• Data access logs: What data the AI read or modified, from which systems, and for what purpose.
• Escalation logs: What the AI flagged for human review, who reviewed it, what they decided, and how long the review took.
• Error logs: When the AI encountered errors, what type of error, and how it was resolved.

These logs should be immutable, meaning the AI cannot modify or delete its own log entries. They should be stored separately from the AI's operational data so that a compromise of the AI system does not also compromise the audit trail.

Conducting a Regular AI Audit

Weekly Quick Review

A weekly review takes 15 to 30 minutes and focuses on anomalies. Look at summary metrics: total actions taken, error rates, escalation rates, and confidence score distributions. Compare these to the previous week. Significant changes in any metric warrant deeper investigation. For example, if the escalation rate doubled, something has changed in the AI's operating environment that is making it less confident, and you need to understand what.

Monthly Detailed Audit

A monthly audit goes deeper. Sample individual actions from each AI agent and review them in detail. Did the AI follow your rules? Was the output appropriate? Were the right data sources consulted? Focus your sampling on edge cases, escalated items, and any categories where errors have been reported. This is also the time to review whether your governance rules are still appropriate or need updating.

Quarterly Compliance Audit

For organizations in regulated industries, a quarterly audit checks that AI operations comply with applicable regulations. This includes verifying that data handling meets regulatory requirements, that required approvals were obtained for high-risk decisions, that audit trails are complete and properly retained, and that any AI incidents were reported and resolved according to your incident response plan.

What to Look For During an Audit

• Rule violations: Any instance where the AI acted outside its defined boundaries. Even if the outcome was fine, a rule violation indicates a governance gap that needs to be closed.
• Confidence drift: Changes in the AI's confidence patterns over time. If the average confidence score is decreasing, the AI may be encountering more unfamiliar situations, which could indicate a changing operating environment or data quality issues.
• Escalation patterns: Which types of situations generate the most escalations. If one category dominates, the AI may need better training or rules for that category.
• Outcome quality: Whether the AI's actions achieved the intended results. Actions that were technically correct but produced poor outcomes indicate a need for better rules or validation.
• Scope creep: Any evidence that the AI is handling tasks outside its defined scope. This can be subtle and only visible through careful log review.

Turning Audit Findings Into Improvements

An audit that identifies problems but does not lead to changes is wasted effort. Every audit finding should be categorized: is this a rule that needs tightening, a training gap that needs filling, a threshold that needs adjusting, or a scope that needs narrowing? Track your findings over time and measure whether previous adjustments had the intended effect. This continuous improvement cycle is what makes governance effective rather than just bureaucratic.