Certified: The ISACA AAIA Audio Course

<p>This episode teaches you how to use AI to enhance audit execution while preserving evidence quality, because Task 23 scenarios often test whether efficiency improvements still produce defensible workpapers and...

<p>This episode focuses on Task 23 by showing how to use AI to enhance audit planning without outsourcing professional judgment, because AAIA expects you to treat AI as an assistant to thinking, not a replacement for...

<p>This episode teaches you how to prevent AI-in-audit blind spots, with a focus on three risks that show up in Task 22 scenarios: bias, leakage, and overreliance. You’ll learn how audit AI can reflect biased training...

<p>This episode focuses on Task 22 by evaluating impacts and risk when AI is integrated into the audit process itself, because AAIA expects you to govern AI use in assurance work with the same discipline you audit in...

<p>This episode explains how to follow up AI audits so remediation actually sticks and risk stays reduced, because Domain 3E recognizes that AI environments change quickly and “we fixed it” can evaporate after the next...

<p>This episode focuses on writing AI audit findings that tie cause, risk, evidence, and remediation into one coherent story, because Domain 3E expects findings to be defensible and useful, not just critical. You’ll...

<p>This episode teaches you how to deliver AI audit reports that executives understand and teams can act on, because Domain 3E often tests whether you can translate technical and governance issues into clear, risk-based...

<p>This episode focuses on using analytics as an audit technique to detect drift, anomalies, and control breakdown trends, because Domain 3D expects you to go beyond spot checks and prove what is happening over time...

<p>This episode teaches you why auditing data quality must happen before you trust any AI output or model score, because Domain 3D scenarios often hinge on the fact that “good models” fail when inputs are wrong...

<p>This episode focuses on validating evidence integrity in environments where models and data change over time, because AI auditing fails quickly when you cannot prove which version produced which outcome. You’ll learn...

<p>This episode explains how to collect AI audit evidence across logs, lineage, artifacts, and change records, because Domain 3C expects you to prove what happened, when it happened, and under which model and data...

<p>This episode teaches you how to test AI controls using evidence, because Domain 3B scenarios often tempt you to accept “trust me” statements, impressive demos, or subjective opinions as proof. You’ll learn how to...

<p>This episode focuses on designing sampling approaches that reveal bias and failure modes in AI decisions, because AAIA questions often ask what sampling plan best supports a defensible conclusion. You’ll learn how to...

<p>This episode teaches audit techniques that are tailored to AI systems, because Domain 3B often tests whether you can select methods that match AI realities like data dependence, model updates, and outcome...

<p>This episode explains how to choose audit criteria for AI by using policy, risk, and outcomes, because AAIA expects you to build criteria that can be proven with evidence, not just referenced as “best practice.”...