This episode examines the secure software development lifecycle (SDLC) for AI, emphasizing integration of security at each stage of system creation. Learners must understand that AI-specific risks require adapting traditional SDLC practices to include dataset vetting, model validation, and adversarial testing. For exams, candidates should know the differences between general secure development and AI-focused pipelines, particularly in areas such as data governance, model registries, and continuous retraining. The relevance lies in being able to explain how embedding security into AI development reduces long-term risk, cost, and compliance exposure.
Applied perspectives include adding checkpoints to verify dataset provenance during design, embedding adversarial robustness testing into continuous integration, and applying secure deployment practices to inference APIs. Best practices involve enforcing code reviews for preprocessing scripts, validating model reproducibility, and ensuring rollback options in case of compromised deployments. Troubleshooting considerations highlight risks when AI projects bypass structured SDLC in the pursuit of speed, often leading to technical debt and exploitable vulnerabilities. For certification readiness, learners must demonstrate how secure SDLC practices create sustainable, resilient AI systems that are aligned with industry standards. Produced by BareMetalCyber.com, where you’ll find more cyber audio courses, books, and information to strengthen your certification path.