Operationalizing MAESTRO for Agentic AI Threat Modeling in CI/CD
This piece shows how to take the MAESTRO agentic-AI threat model from theory to practice by integrating automated classification (TITO) into CI/CD to continuously flag LLM-driven tool actions, dynamic trust-boundary crossings, and prompt-injection chains in real codebases ([Applying MAESTRO to Real-World Agentic AI Threat Models](https://kenhuangus.substack.com/p/applying-maestro-to-real-world-agentic)[^1]). The core message: SAST alone misses agent behavior; you need runtime-aware threat modeling that treats prompts as untrusted code and audits every tool invocation end-to-end. [^1]: Adds: Walkthrough of wiring MAESTRO into an automated tool (TITO), examples of findings on agentic codebases, and guidance to embed threat modeling into CI/CD.