Delivered by Microsoft's Secure AI GBB team to enterprise security and platform engineering audiences. A full day, grounded in real incidents (Samsung's ChatGPT leak, the Amazon Q supply-chain attack) and a running multi-agent case study — not slideware.
Topics: AI threat modeling · agent governance · identity security · data protection · secure AI architecture.
Overview
This workshop treats AI security as a systems problem, not a checklist. It's built around one running example — a FinOps multi-agent application — that each chapter layers its controls onto, so identity, data protection, platform hardening, and runtime observability are shown solving the same architecture's problems rather than four disconnected topics. Two deep-dive chapters (Model Context Protocol security and RAG security) go further into two specific technical areas participants consistently ask about.
Who this is for
CISOs evaluating how existing security programs need to change for agentic AI.
Security architects designing identity, data, and runtime controls for AI systems.
Enterprise architects deciding where AI security controls should live in an existing platform.
AI engineers who need to understand the security implications of the systems they're building.