Methodology
DetectFlow — detections without operational debt
Detection-as-code architecture pattern for SOCs whose detection backlog grows faster than the team can maintain. Versioned, tested, deployable rules; CI/CD pipelines with regression tests; continuous performance and false-positive measurement. The discipline that lets a SOC scale past the hundreds-of-detections operational ceiling.
Detections in production with operational debt held flat. Most detection programs cap at low hundreds because each new rule adds maintenance load; DetectFlow keeps the per-rule maintenance cost near zero through automation.
The pipeline
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Author
Detection content
Versioned in Git; YAML, SPL, KQL, SQL per target engine.
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Test
CI/CD pipeline
Unit tests, regression suites, replay against synthetic and historical telemetry.
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Deploy
Detection engine
Splunk ES, Sentinel, Chronicle, custom — engine-agnostic.
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Measure
Feedback loop
Performance metrics, false-positive rate, MTTD — fed back to backlog.
What composes, what’s brittle
- Why this matters. The hundreds-of-detections operational ceiling is real; DetectFlow removes it.
- Reference patterns. Anvilogic, Panther, hand-rolled CI/CD on Detection-as-Code repos.
- Detection portability. Standard SQL detections move between engines without rewrite.
- Feedback loop. Every rule's performance and FP rate measured per deployment cycle.
- Best fit. SOCs where analyst time is consumed by tuning rather than hunting.
- What's hard. The cultural shift from detection content as personal craft to detection as production software.
Sources: Reference patterns: Anvilogic, Panther, Splunk Enterprise Security content packs; published Detection-as-Code repos including SigmaHQ.