Detection Engineering as a Feedback Loop
Detection rules improve fastest when every alert is treated as feedback on assumptions.
Key takeaways
- A detection rule should state the behavior it expects to catch and the assumption behind that behavior.
- False positives are useful when they identify missing context, weak thresholds, or ambiguous telemetry.
- Review notes should track rule changes alongside the incident or hunt that motivated each change.
Detection engineering often fails when a rule is treated as finished after it first lands in the SIEM. The better model is a loop: write the hypothesis, deploy the rule, observe the alerts, inspect misses and noise, then revise the hypothesis. That loop makes a detection library more like a living system than a pile of queries.
Every weekly review should ask what the rule taught us. If analysts keep closing an alert as benign, the rule may need more context. If an incident escaped the rule, the assumption may be wrong or the telemetry may be absent. Both outcomes are useful when they are captured clearly.
The review note should explain why a rule changed. Six months later, that reason is often more valuable than the query itself.
Related notes
Backlinks
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