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AI Audit Walkthroughs and Interviews: Design and Evidence Capture
Evaluating an artificial intelligence system means more than reading written reports — it means sitting down with the people who design, build, and maintain the algorithms. This episode of the ISACA Advanced in AI Audit (AAIA) exam prep series explores the methodology behind guided conversations and interactive demonstrations, the categories of questions auditors should pose, and how to use this evidence to surface operational, ethical, and legal risk.
What this episode covers
- The phased walkthrough methodology — document review, live demonstrations, and targeted interviews repeated through the engagement.
- Business context and project risk — purpose, business case, intended users, accountability, and the risk register.
- Data collection and the data register — sourcing, legal consent, and inventory of every dataset used.
- Model assessment — preprocessing, anonymization, outliers, algorithm choice, explainability, and version management.
- Testing, security, and code management — adversarial testing, automated frameworks, and human-in-the-loop oversight.
- Bias, fairness, and edge cases — protected class data, high-stakes decisions, diverse user testing, and rare scenarios.
- Performance and outcomes — defining success metrics, real-world simulations, and stress testing.
- Compliance assessment — global law mapping, regulatory notifications, and consumer rights to contest or opt out.
Watch the full episode above for the worked examples and detailed explanations of each concept.
Frequently Asked Questions
What is the phased approach for AI audit walkthroughs and interviews?
Auditors do not rely only on written reports. They use a phased approach: first review the official documents, then request a live demonstration of the system in action, and finally ask targeted questions to the personnel responsible for the technology. Demonstrations are conducted multiple times throughout the evaluation to confirm the internal safety checks are actually working.
What is a risk register and a data register in an AI audit?
A risk register is a master list of all potential problems that could go wrong with a project, used to assess technical, ethical, regulatory, and societal risks. A data register is a detailed inventory cataloging all the information used to teach the model, including how the raw material was acquired and the methods used to evaluate its quality.
What is adversarial testing and why does it matter?
Adversarial testing is a security drill where friendly experts deliberately try to trick or break the system to find its hidden vulnerabilities. Auditors investigate security testing for data leakage, malicious injections, and adversarial prompts, and check whether automated testing frameworks were used and what the results were.
What questions do auditors ask about bias and fairness?
Auditors verify whether the system aligns with the organization’s ethical values, whether it processes protected class data like race, religion, or gender, and whether it makes high-stakes decisions in areas like law enforcement or hiring. They check what steps detected prejudice, how bias was remediated, whether the system was tested on a diverse group of users, and how edge cases were handled.
What consumer rights must auditors verify in an AI compliance assessment?
Auditors verify that citizens are clearly informed of their rights when a machine makes a decision about their life, that there are mechanisms to contest the outcome with a human being, and that users can completely opt out of the automated process. They also confirm procedures for reporting major security incidents or data breaches to the proper authorities, including any notification required under laws like the EU AI Act.
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Reference: This article is based on concepts discussed in AI Audit Walkthroughs & Interviews: Design & Evidence Capture.