Episode 103 — Write AI findings that tie cause, risk, evidence, and remediation together (Domain 3E)
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 learn how to describe the condition clearly, reference the criteria it violates, and present evidence that is traceable to model versions, data states, and control operation records. We’ll cover how to identify root cause without guessing, using signals like missing approvals, incomplete lineage, weak monitoring triggers, unclear ownership, or inadequate reviewer capacity that leads to unchecked harmful outcomes. You’ll also learn how to express risk in outcome terms—who could be harmed, how quickly harm is detected, how reversible it is—and how to propose remediation that closes the control gap with measurable steps and ownership. By the end, you should be able to answer AAIA scenarios by selecting the finding approach that is complete, evidence-driven, and directly actionable, rather than writing vague observations that cannot be fixed or retested. 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.
