Most breaches start with something nobody knew was exposed. If you cannot see your environment clearly, you cannot defend it.

In this session I build an MCP (Model Context Protocol) server that scans an Azure subscription and renders it as an interactive graph, then I turn that graph into a security tool. We look at how resources actually connect, where the exposed edges are, and how to reason about blast radius when one resource is compromised.

I cover the practical safeguards that matter when you point AI at a real tenant. Keeping the scan strictly read-only, handling large subscriptions without noise, and building in leak-detection so sensitive data never lands in a prompt. I also share the precision rule I enforce throughout: every security finding has to come from actual scan data, with gaps marked unknown rather than inferred from a resource name or tag. No guessing your way to a false sense of safety.

I close on where this goes next, using the same map to reason about Defender for Cloud coverage and which logs are actually flowing into Sentinel.

You will leave understanding what MCP is, why it fits security tooling so well, and how to start building an Azure-aware assistant that helps you see what you are actually exposing.

Who it is for: security and cloud engineers who want a clearer, defensible picture of their Azure attack surface.