Threat Model and Security-Control Matrix
Explore how to develop a comprehensive threat model and security-control matrix to secure AI systems. Learn to map threats to controls, assign ownership, define evidence signals, and manage residual risks with actionable procedures. This lesson guides you in creating traceable, testable security frameworks vital for enterprise AI system reviews and operational compliance.
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 ...