How can you optimize policy evaluation performance in a large Zero Trust deployment?

At scale, evaluating every request against a full policy set can introduce latency that hurts user experience, so several techniques are commonly combined:

1. Cache low-risk, recently-approved decisions for a short TTL instead of re-evaluating identical low-sensitivity requests from scratch every time.
2. Deploy policy engine instances closer to the enforcement points, reducing round-trip latency instead of routing every decision through one central, distant service.
3. Index policies by resource and subject attributes so the engine narrows to relevant rules quickly instead of scanning the entire policy set per request.
4. Run slower, non-blocking risk signals, like deep behavioral analytics, asynchronously, updating the trust score for future requests rather than delaying the current one.

Evaluation latency should be tracked as an explicit SLO alongside security metrics, since a policy engine that is "secure but too slow" tends to get bypassed by frustrated users or teams.