About this video
Most organizations are running AI in their security workflows. Few have it governed. This walkthrough shows what governance looks like in practice — not as a compliance checkbox, but as a design constraint that makes AI-generated threat outputs defensible, repeatable, and traceable back to real architecture.
The session covers all four Nexus MCP use cases with live product demonstration: IDE threat modeling, document-based model generation, CI/CD pipeline integration, and portfolio-level analysis. Each demo uses the same underlying system — ThreatModeler as the governed system of record, AI agents as the workflow layer.
What you'll learn
- Why architecture context is the prerequisite for AI outputs worth acting on
- ✓How the MCP protocol connects AI agents to ThreatModeler's rules engine and threat libraries
- What continuous threat modeling looks like embedded in a real CI/CD pipeline
- How security and engineering leaders can report on portfolio-wide risk from a single governed source
Presenters
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