Threat Model and Security-Control Matrix
Explore how to construct a comprehensive threat model and security-control matrix for AI systems. Learn to prioritize threats, assign enforceable controls, document ownership, and define evidence signals that demonstrate control operation and residual risk management. This lesson guides you to create a traceable security framework ensuring compliance and measurable risk acceptance for enterprise AI deployments.
This chapter has named a control at every boundary: the instruction boundary, the retrieval boundary, the tool wrapper, the dependency register, and the gateway boundary against shadow AI. Put every one of them in one place a reviewer can trace end to end. Build a threat model and a security-control matrix that makes traceability explicit: threat to control to enforcement point to owner to evidence to residual risk.
Define acceptance criteria as testable operational requirements rather than stylistic guidance. For each prioritized threat, specify at least one concrete control and map it to an existing enforcement point, such as the gateway, retrieval boundary, agent runtime, tool wrapper, logging layer, or evaluation layer. Assign each control an accountable owner and an observable evidence signal that can be collected in accordance with data-minimization and redaction requirements.
This matrix also sets up a clean handoff to the cost and performance trade-offs the next chapter covers. When a control states its placement, owner, and evidence plainly, that later discussion can bound what’s optimizable without quietly weakening security. It can also tell an intentional residual risk apart from an unowned gap. Treat the matrix as a phase gate: never expand autonomy, tools, or corpus scope unless the evidence signals defined here are actually produced and ...