Detection Engineer Use Case

Your Best People Are Fighting the Platform Instead of the Threats

Detection engineers are among the most skilled people in security. They understand attacker TTPs inside and out. They write complex detection logic. They map coverage to MITRE ATT&CK. And every day, their SIEM fights them at every step.

The ceiling hits fast: ~200 active detections before search performance degrades. That means triaging which rules stay on — not based on risk, but based on what the platform can handle. Every rule runs on cron — 15 to 60 minute intervals. A detection designed to catch lateral movement in real time becomes a scheduled search that fires an hour after the attacker has already moved. High-volume data sources — Windows Events, DNS, HTTP, and any high-cardinality voluminous data — get bypassed entirely because they'd blow up the license or crush the search tier. And multi-step attack chains? Good luck stitching together scheduled searches with lookup tables pretending to be state.

The result: engineers who know exactly what to detect, writing dumbed-down rules for a platform that won't let them. They have a backlog of detections they've already written but can't activate. They know the coverage gaps exist. They just can't fix them.

How spotr.io does it?

spotr.io gives the detection engineer a partner: the Detection Engineer Agent. It handles the grunt work — building, testing, and deploying detections — while the engineer guides it with their experience and expertise. Envision what you want to catch, and the agent brings it to life. Bring your custom rules and the Detection Engineer Agent will make them even better.

Every detection evaluates on the stream — sub-second, every event, no scheduling. Thousands of detections running simultaneously with no performance ceiling. Every data source is fair game — Windows Events, DNS, HTTP, and high-cardinality data — all streaming, all detectable. No more sources you "can't afford to look at."

The detection logic is full-fidelity. Multi-step sequences with ordering and time windows. Anomaly baselines per user, per host, continuously learning. Stateful correlation that tracks what's happening now, not what a lookup table cached an hour ago.

The Conversation

"How many detections did you have to turn off last quarter because search couldn't keep up?" If they have a backlog of rules they can't activate — and they will — that's the opening. "What if you could just describe what you want to catch, and the Detection Engineer Agent had it running in minutes?"

You envision it. spotr.io builds and runs it.