Episode 71 — Evaluate configuration management for AI across code, data, and models (Task 14)
This episode explains how configuration management for AI must cover more than application settings, because Task 14 expects you to control anything that can change outcomes, including code, data pipelines, and model artifacts. You’ll learn how to identify configuration items that matter most—feature logic, preprocessing rules, training parameters, thresholds, prompts or templates where applicable, and deployment settings—then confirm they are versioned, approved, and traceable to specific releases. We’ll cover why “small” configuration changes can be high-risk in AI, such as changing a cutoff score, altering a data normalization step, or switching a dependency version that shifts model behavior. You’ll also learn what evidence auditors rely on, including configuration baselines, change histories, access logs, and release records that link configuration states to observed outcomes in production. By the end, you should be able to answer exam scenarios by choosing the option that enforces controlled, auditable configuration across the full AI system, not just the code repository. 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.
