Episode 21 — Evals & Test Pipelines
This episode examines evaluations and test pipelines as essential processes for maintaining AI system security and reliability. Evaluations, or “evals,” are structured tests that measure a model’s behavior against known benchmarks or adversarial scenarios, while pipelines provide the automated flow of regression testing, scorecards, and service-level objectives. For certification purposes, learners must be able to define these concepts, explain how they ensure system reliability, and describe how evals fit into continuous integration and deployment processes. Understanding evals prepares candidates to explain not only quality assurance but also security-driven testing, which is increasingly required in real-world deployments.
In practice, test pipelines simulate adversarial prompts, verify policy compliance, and track performance over time to ensure that new updates do not reintroduce vulnerabilities. Examples include running regression suites against known jailbreak patterns, validating robustness to data drift, and applying fairness or privacy metrics during model promotion. Best practices highlight automation of evals within CI/CD systems, use of red team–derived adversarial inputs, and clear scorecard reporting for leadership. Troubleshooting considerations emphasize the risks of insufficient coverage, poor baselines, or untracked performance drift. For exam readiness, learners should be able to articulate the role of evals and pipelines as structured, repeatable safeguards for secure AI deployment. Produced by BareMetalCyber.com, where you’ll find more cyber audio courses, books, and information to strengthen your certification path.
