Threat modeling is essential for prioritizing security defenses and monitoring efforts. It shifts the focus from reacting to trending threats to proactively addressing the most significant risks to a business. In 2025, security leaders emphasized the need for a strong foundation in threat modeling, especially with AI tools at their disposal. AI agents can break down silos within organizations, providing security teams with direct access to valuable context from engineering, product, and infrastructure teams to better understand and protect critical systems. The article introduces the Model Context Protocol (MCP) and AI agents as tools for building and operationalizing threat models. By using these technologies, security teams can answer key questions about where to focus detection efforts and identify blind spots. The process involves synthesizing five layers of intelligence: identities and assets, threat intelligence, logs and detection coverage, alerts and case history, and organizational context. Each layer provides critical insights necessary for effective threat modeling, allowing teams to prioritize based on business impact rather than just technical vulnerability. AI agents leverage the MCP to access these context layers seamlessly, eliminating the lengthy manual processes of data gathering. The article outlines practical steps to connect SIEM data, ticketing systems, and documentation tools to facilitate the generation of actionable threat models. By integrating these systems, organizations can create a continuous threat modeling practice rather than a sporadic exercise, ultimately enhancing their security posture and response capabilities.