Episode 106 — Prevent AI-in-audit blind spots: bias, leakage, and overreliance risks (Task 22)
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 data or biased prompts, leading to uneven scrutiny across teams or systems, and how to counter that with review practices, diverse sampling, and validation against independent evidence. We’ll cover leakage risks where sensitive audit information is exposed through tool usage, storage, or vendor handling, and what controls reduce exposure, including data minimization, access restrictions, redaction, and clear tool configuration. Overreliance will be treated as a professional risk: trusting AI-generated conclusions, missing contradictions in evidence, or skipping interviews and testing because outputs “seem right.” By the end, you should be able to answer AAIA scenarios by choosing safeguards that keep auditors accountable, protect confidentiality, and ensure AI outputs are verified before they influence audit judgments. Produced by BareMetalCyber.com, where you’ll find more cyber audio courses, books, and information to strengthen your educational path. Also, if you want to stay up to date with the latest news, visit DailyCyber.News for a newsletter you can use, and a daily podcast you can commute with.
