AI App Security Testing: SAST, DAST & Supply Chain Checks | Module 2.2

Securing an AI application is not just about testing the model—it means verifying every layer that touches code, data, APIs, containers, cloud configuration, and deployment artifacts. In this lesson, we move from secure development into practical verification: how to find risks before AI workloads reach production.

You’ll learn how security testing fits into the AI SDLC and why checkpoints across build and runtime environments matter.

Key takeaways:
- Why AI applications must be tested as full software systems, not isolated models
- How SAST helps detect insecure code, prompt construction issues, unsafe SDK usage, secrets, and authorization gaps
- Where dependency scanning, container scanning, IaC scanning, and DAST fit in the release pipeline
- How to prioritize findings using exploitability, sensitive data exposure, reachable code, and business impact
- Why supply chain visibility is essential for AI agents, plugins, open-source packages, and cloud deployments
- How to turn scan results into clear decisions: fix, approve risk, or proceed

Course progression estimate: Spend the first 5 minutes understanding the AI application attack surface, the next 10–15 minutes mapping testing checkpoints across the SDLC, and the final section reviewing how findings should be prioritized, assigned, fixed, and rescanned.

If your organization is building or deploying AI systems, this training helps teams create visible, testable, and governable release practices.

For corporate training, visit https://kryptomindz.com or contact mustafa@kryptomindz.com | +91-9873062228.

#AISecurity #ApplicationSecurity #SAST #DAST #DevSecOps #SupplyChainSecurity #SecureSDLC #CorporateTraining

Видео AI App Security Testing: SAST, DAST & Supply Chain Checks | Module 2.2 канала KryptoMindz Technologies