Latency Budgets for Security Co-Pilots

Security analysts tolerate roughly 600 milliseconds of delay before their working memory collapses and they context-switch. Anything slower, and conversational co-pilots degrade into asynchronous ticket queues. Our mandate was simple: responses must land before the human looks away.

We instrumented the entire inference pipeline, from request ingress to token egress, and carved out an explicit latency budget. Retrieval gets 120 milliseconds to assemble context: log shards, code snippets, recent alerts. Model inference receives 280 milliseconds on dedicated GPU slots with dynamic batching capped at three simultaneous dialogues. The remaining time is reserved for streaming assembly, compliance filtering, and UI paint.

Every request carries a service-level contract. If the system detects budget pressure—say because retrieval is still fetching from cold storage—it begins graceful degradation. Non-critical tool calls are deferred, summaries are condensed, and auxiliary visualizations load asynchronously after the primary answer lands. The analyst always receives a functional response on time, even if some adornments follow a beat later.

This discipline changed behavior on the operations floor. Analysts now treat the co-pilot as a true pair partner. Usage shifted from passive report summarization to active hypothesis testing: "If I block this subnet, what happens two steps downstream?" Triage throughput jumped 32% without adding headcount, because the human never had to wait for the system to catch up. Microsoft’s March 2025 longitudinal study of Security Copilot users reported analogous double-digit efficiency gains, reinforcing that speed is the precondition for measurable productivity.

Latency engineering also hardened security posture. By keeping experiences snappy, we eliminated the shadow workflows where frustrated analysts export data to local notebooks. All sensitive context remains inside governed tooling with full audit trails, which simplified compliance attestation. The EU AI Act’s latest draft explicitly calls for observability over human-in-the-loop checkpoints; our telemetry satisfies that audit requirement out of the box.

Future work focuses on predictive scheduling. We are training models to anticipate analyst intent based on preceding interactions and pre-warm toolchains before the request arrives. The goal is to shrink perceived latency below 200 milliseconds so conversations feel instantaneous, even when the system is coordinating multiple back-end services. We are also collaborating with researchers exploring human-in-the-loop agentic interfaces, such as Microsoft Research’s Magentic-UI, to ensure the next generation of co-pilots blends instant responses with rich collaboration affordances instead of forcing analysts into passive roles.