Knowledge Base · Detection Engineering
The discipline of designing, collecting, and converting raw system telemetry into high-fidelity detections — the engineering foundation beneath every effective SOC.
What It Is
The practice of building and maintaining systems that collect security-relevant data and transform it into reliable detection signals.
Why It Matters
Without quality telemetry and well-engineered detections, your security tools produce noise — not intelligence.
Who Needs It
Detection engineers, SOC leads, and security architects responsible for an organization's ability to find threats within its own environment.
CORE TELEMETRY PIPELINES
Enterprise Data Sources
Click any source to expand its 3 core best practices.
ENGINEERING PRINCIPLES
Must-Knows for Detection Engineers
Principles separating programs that find real threats from those that generate alert fatigue.
Collect With Purpose, Not Coverage
Ingesting everything is not a detection strategy — it is a storage problem. Define what attacker behaviors you need to detect, then work backwards to the telemetry required.
Normalize Early, Query Fast
Log normalization at ingestion time — using schemas like OCSF or ECS — dramatically reduces query complexity and enables cross-source correlation that ad hoc parsing cannot match.
Detection Logic Must Evolve
Adversaries change TTPs faster than most detection libraries are updated. Build a continuous rule review process into your operations — no static rule set stays effective long-term.
Alert Fidelity Over Alert Volume
A SOC overwhelmed by low-fidelity alerts effectively has no detection capability. Ruthlessly tune detections — accept fewer, higher-confidence alerts over broad, noisy coverage.
Test Detections Like Code
Detection-as-Code practices — version control, CI/CD pipelines, atomic test validation — ensure your detection logic works as intended and degrades gracefully as environments change.
Reference Architecture Designs
Visual diagrams illustrating telemetry collection, detection engineering, and correlation architecture patterns.
Telemetry Collection Pipeline
Multi-source collection architecture showing agent types, pipeline processing, and normalized output feeding SIEM and data lake tiers.
Detection Engineering Lifecycle
Full lifecycle from threat hypothesis to deployed detection rule, with ATT&CK coverage mapping and a continuous improvement feedback loop.
Tiered Log Ingestion Architecture
Hot/warm/cold storage tier design separating active detection data from investigation archives and long-term compliance retention.
Alert Correlation & Incident Creation
How low-fidelity individual alerts from multiple sources are correlated into high-confidence incidents with automated containment triggers.
Engineer Your
Detection Stack
Move from reactive alerting to proactive detection engineering. CyberNeurix helps teams build telemetry foundations that actually find adversaries.