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Sample AI Audit Process: Plan, Execute, and Report
What does a complete artificial intelligence audit actually look like from start to finish? This episode of the ISACA Advanced in AI Audit (AAIA) exam prep series provides a comprehensive walkthrough of the AI audit lifecycle, taking auditors through every phase required to evaluate a high-risk automated system from planning through long-term follow-up, using a hospital claims-approval system as a running example.
What this episode covers
- Phase 1: Scoping — drawing clear evaluation boundaries, listing data sources, and mapping regulatory obligations.
- Phase 2: Data collection — scrutinizing training data for completeness, diversity, accuracy, and lawful consent.
- Phase 3: Model assessment — reviewing architecture, hyperparameters, and the metrics chosen to define success.
- Phase 4: Bias and fairness assessment — checking outputs against protected characteristics for unfair disparities.
- Phase 5: Transparency and explainability — verifying the system can show its work to human decision-makers.
- Phase 6: Compliance assessment — confirming lawful processing and extra-jurisdictional obligations for global systems.
- Phase 7: Performance and outcome assessment — measuring long-term real-world results against prior baselines.
- Phase 8: Reporting — formally documenting findings, recommendations, and obtaining management sign-off.
- Phase 9: Remediation and monitoring — tracking corrective actions to completion through an independent group.
Watch the full episode above for the worked examples and detailed explanations of each concept.
Frequently Asked Questions
What happens during the scoping phase of an AI audit?
Scoping means defining exactly what you will evaluate and what you will ignore, like drawing property lines before building a fence. For a hospital claims-approval system you identify the specific algorithms making the decisions, pinpoint every data source such as medical histories and physician notes, map the exact logic the system uses over time, and define the regulatory boundaries including civil rights guidelines and emerging AI laws.
What are the phases of the AI audit lifecycle?
The lifecycle phases are scoping, data collection, model assessment, bias and fairness assessment, transparency and explainability, compliance assessment, performance and outcome assessment, reporting, and remediation and monitoring. Together they establish boundaries, verify inputs, inspect internal mechanics, check equality, demand clear logic, ensure legal adherence, monitor long-term outcomes, document findings, and track corrective actions to completion.
How does an AI audit detect bias and unfairness?
During the bias and fairness assessment, you ensure the system treats all groups equally and does not illegally discriminate, like spotting a toll booth that charges red cars double for no reason. You ask how the team embedded ethics into workflows and scrutinize outputs for disparities tied to protected characteristics. For example, if patients over sixty are denied seventy percent of the time but are only thirty percent of applicants, that is a massive red flag of a biased system.
What happens after the AI audit report is written?
After reporting comes remediation and monitoring, because identifying problems is useless if no one fixes them. The recommendations become mandatory tasks assigned to management, a dedicated tracking application oversees the actions, and the process should be controlled by an independent group like risk management or internal audit. They track progress until every issue is resolved and report the final status to key stakeholders.
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Reference: This article is based on concepts discussed in Sample AI Audit Process: Plan, Execute & Report.