Agentic systems expand risk beyond model quality. Once models can invoke tools, access MCP servers, trigger workflows, and interact with customer or operational data, the security boundary shifts to runtime.

This paper explains the minimum runtime controls required for production AI systems, especially in regulated environments where governance must be visible in operation and not only in policy documents.

The central lesson is that inventories and model cards are necessary but not sufficient. Real risk emerges when a live system interprets a prompt, chooses a tool, reaches data, produces an output, and potentially triggers an irreversible action.

Teams that treat runtime as the true control boundary gain a cleaner way to manage prompt injection, scope abuse, unsafe output, MCP-connected tooling, approval logic, and the evidence trail that reviewers need afterward.