Episode 13 — Perform AI impact assessments with scope, evidence, and actionable results (Task 8)
This episode teaches how to execute an AI impact assessment so it produces decisions, controls, and evidence that stand up to audit rather than a vague narrative report. You will learn how to set scope boundaries, identify stakeholders, select evaluation criteria, and gather evidence across data sources, model behavior, deployment pathways, and user interaction patterns. We walk through what “actionable results” means in exam terms: prioritized risks, clear recommendations, assigned owners, deadlines, and acceptance criteria for residual risk. Practical examples include mapping harms to controls like access restrictions, human review thresholds, monitoring triggers, and incident playbooks. You will also learn how to spot low-quality assessments that rely on assumptions, ignore production realities, or fail to connect findings to governance approvals. 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.
