AI Security Engineer Foundations + Certificate — Javier Garza, Snyk

This talk details the foundations of AI security engineering, focusing on identifying and mitigating risks associated with AI components within software repositories. It introduces the concept of an AI Bill of Materials (BOM) derived from CLI scans, enabling the inventory of models, datasets, and agents. The content covers OWASP risks for LLM applications, including prompt injection (direct and indirect), sensitive information disclosure, supply chain risks (poisoning, hallucinated dependencies), and excessive agency. Defenses discussed include database view restrictions, limited permissions, output validation, human approval, threat modeling (assets, trust boundaries, AI threat mapping via MITRE ATLAS), securing agent skills and MCP servers, and a scan-fix-rescan loop for vulnerabilities. It also emphasizes secure prompting, dependency verification, environment separation (dev/prod), IDE hardening, developer review, and governance through explained guardrails. Offensive agent workflows and challenges are presented for practical testing.