AI Security Engineer Foundations + Certificate — Javier Garza, Snyk

An AI bill of materials turns a repository into a searchable inventory of models, datasets and agents. Javier Garza demonstrates the workflow from a CLI scan to a visual dashboard, then asks it which models the project uses. That inventory anchors a broader security problem: teams cannot assess the AI components they do not know are there. The workshop connects shadow AI to prompt injection, supply chain risk, sensitive information disclosure and excessive agency, using Gandalf challenges and malicious instructions embedded in external content to show how an application's intended rules can be bypassed. Garza works through defenses at several layers. A database view can restrict what an assistant is allowed to retrieve; limited permissions, output validation and human approval constrain what it can do next. Threat modeling maps assets, trust boundaries and possible attacks before deployment, with an intentionally vulnerable student assistant offered for practice. For skills and MCP servers, he separates scanning tool definitions for hidden instructions from scanning the underlying code for vulnerabilities, then explains a scan, fix and rescan loop driven by coding agent rules and hooks. The final sections examine secure prompting, dependency verification, separate development and production environments, IDE configuration and developer review. Governance depends on explaining guardrails as well as enforcing them, because unexplained restrictions invite workarounds. He closes with offensive agent workflows, training resources and challenges that let participants test these ideas themselves. Speaker info: - https://www.linkedin.com/in/jjgarza - https://twitter.com/jjaviergarza - https://snyk.io/lp/ai-sec-eng-foundations/ - https://github.com/snyk-workshops/vulnerable-student-assistant Timestamps: 0:00 - Workshop overview 2:16 - Meet Javier Garza 5:30 - OWASP risks for LLM applications 6:42 - Prompt injection and Gandalf 9:01 - Indirect injection through external content 12:02 - Sensitive information disclosure 15:32 - Restrict database access 18:18 - Supply chain risks 23:14 - Poisoning and AI component inventories 24:11 - Output handling and excessive agency 27:26 - Prompt leakage and vector weaknesses 29:04 - Resource consumption and training modules 31:07 - Shadow AI and unsanctioned tools 36:06 - AI bills of materials 38:32 - Inventory…