Dynamic model routing is an AI control-plane feature that automatically chooses which approved model handles each request. The choice is driven by policy signals such as cost, latency, task type, security constraints, and expected output quality. Instead of sending every prompt to the most powerful model, the gateway can route routine work to cheaper or faster options.
In cyber security, this matters because the routing layer becomes a governance and risk control point. Defenders can use it to keep sensitive workloads on approved models, enforce quotas, and reduce shadow AI spending. At the same time, attackers may try to exploit weak routing logic, such as policy bypasses, model substitution, or input patterns that push requests to less suitable models. For that reason, routing needs logging, testing, and change control so teams can see which model answered, why it was selected, and whether the decision was safe.