Signal Drift in Detector Pipelines
Detector quality drops quietly when traffic behavior changes; this note covers how we watch drift before precision falls off.
The worst detector failures are gradual. Nothing breaks in one day, but confidence scores keep spreading until clean and synthetic traffic overlap in uncomfortable ways.
We split drift checks into cadence tiers: hourly distribution checks, daily feature attribution movement, and weekly replay against pinned benchmark slices.
When multiple tiers move in the same direction, we trigger a retraining candidate. If movement is isolated to one feature family, we patch that extractor instead of retraining the full model.
This policy reduced unnecessary retrains and improved rollback confidence. We can explain why a model changed rather than only observing that metrics recovered.
Drift is expected, not exceptional. The pipeline is healthier when it treats change as routine maintenance instead of emergency response.