Episode 35 — Manage AI Security Risks: Data Leakage, Prompt Abuse, and Model Misuse
This episode focuses on AI security risks that leaders must anticipate and control, including data leakage, prompt abuse, and misuse patterns, which connects to exam objectives around governance, privacy, and program controls. You will learn how sensitive data can escape through inputs, outputs, logs, retention policies, and third-party handling, and how prompt manipulation can influence behavior, extract information, or drive unsafe actions if guardrails are weak. We cover practical controls such as data classification rules for AI use, access tiering, monitoring for sensitive output, and incident handling pathways when AI-related events occur. A scenario explores an employee using an AI tool with customer data and the resulting exposure and response steps, while troubleshooting considerations address shadow AI adoption, unclear vendor retention terms, and the need for continuous review as models and features change. The episode emphasizes that controls must focus on both input and output pathways, plus oversight mechanisms that detect drift and abuse over time. 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.
