Detection-as-Code
Treat detections as versioned engineering artifacts with ownership, test cases, dependencies, deployment status and rollback procedures.
CYBERINTEL HUB / TOPIC MODULE
SOC & Detection
Design practical SOC workflows, detections, analytics and triage processes.
Explore SOC & Detection
Topic-focused references and direct links. This page stays focused on soc & detection rather than repeating the main portal.
A modern SOC connects telemetry, triage, detection engineering and investigation. The aim is to produce high-quality signals that analysts can validate quickly and investigate consistently.
Detection lifecycle
Define the behavior, identify required telemetry, write the analytic, test it, measure false positives, tune it and map it to the relevant threat behavior.
Portable detections
Sigma and YARA help create reusable detection logic for different use cases. Pair rules with clear data requirements and validation tests rather than deploying untested rules.
SOC improvement
Track detection coverage, alert quality, investigation time, recurring false positives and telemetry gaps. Feed lessons from incidents and hunts back into the detection lifecycle.
This module presents the technical concepts, operational considerations and defensive engineering practices relevant to the topic.
Detection Engineering
Build detections from a defined threat behavior, data source, analytic logic, test cases and expected outcome. Version detection content and document assumptions, dependencies and known blind spots.
Signal Quality
Measure precision, false-positive rate, alert volume, analyst effort and detection latency. A technically sophisticated rule that generates unusable noise is an operational failure.
Correlation & Enrichment
Combine identity, endpoint, network, DNS, cloud and threat-intelligence context to increase analytical confidence. Correlation should preserve the evidence chain so analysts can reproduce the reasoning.
ATT&CK Coverage
Map detections to tactics and techniques, then identify telemetry and analytic gaps. Coverage should be treated as risk-informed visibility rather than a percentage target by itself.
Technical Perspective
Architecture, operational and engineering considerations relevant to this topic.
Telemetry Quality
Detection accuracy is constrained by field availability, normalization, clock accuracy, retention and collection coverage. Measure data quality before blaming analytic logic.
Adversary Simulation
Use safe test cases and controlled emulation to validate analytic behavior. Map expected telemetry to the detection and document what cannot be observed.
Response Integration
A detection should define investigation context, enrichment sources, severity logic and response options. Avoid alerts that provide no path from signal to decision.
Alert Triage
Validate signal quality, scope impact and capture evidence before escalation.
Open related CyberIntel section →Detection Engineering
Build, test and tune detections against known behaviors.
Open related CyberIntel section →Sigma
Use portable detection rules and convert them for supported SIEM platforms.
Open related CyberIntel section →YARA
Support malware and file-based detection with controlled, tested rules.
Open related CyberIntel section →ATT&CK Coverage
Map detections to adversary behaviors and identify blind spots.
Open related CyberIntel section →Defensive-use: use security tools and techniques only on systems and environments you are authorized to assess. Use intentionally vulnerable labs or isolated test environments for experimentation.