Detection Engineering
Custom detection rule development and validation to improve SOC effectiveness.
Detection Engineering
Detection Engineering is the discipline of systematically building, testing, maintaining, and improving the detection logic that drives a SOC's alerting capability. Where traditional rule management is ad hoc and reactive, detection engineering applies software engineering principles — version control, testing pipelines, peer review, and lifecycle management — to detection content, ensuring that the rule set is accurate, maintainable, and continuously aligned to the evolving threat landscape.
Intelliroot's Detection Engineering service implements a detection-as-code programme: developing Sigma rules for your priority detection gaps, building a coverage map aligned to MITRE ATT&CK, establishing quality metrics (MTTD, false positive rate, ATT&CK coverage percentage), designing SOAR automation workflows for high-frequency alert types, and creating a sustainable rule lifecycle management process. The result is a SOC that improves with every sprint rather than degrading under the weight of unmaintained, untested rules.
Why Detection Engineering Transforms SOC Effectiveness
Rule Debt Kills SOC Performance
Most SOCs accumulate thousands of untested, overlapping, and obsolete rules over time. Detection engineering introduces lifecycle management that prevents rule debt from building and keeps the rule set lean, accurate, and effective.
Measure and Improve Coverage
ATT&CK-aligned coverage mapping answers the critical question most SOCs cannot answer: what percentage of adversary techniques relevant to us do we actually detect? Detection engineering makes coverage a managed metric, not an unknown.
Automation Multiplies Analyst Capacity
SOAR automation for high-frequency, low-complexity alert types can reduce analyst triage time by 60 to 80 percent for those alert classes — freeing analysts for complex investigations that require human judgement and expertise.
Vendor-Agnostic Portability
Sigma-format detection rules are portable across SIEM platforms. If you migrate platforms, your detection investment moves with you — eliminating the costly rework that proprietary native rule formats require.
What Detection Engineering Covers
Detection Gap Analysis
- Current rule set audit and quality assessment
- ATT&CK coverage mapping (current state baseline)
- Detection gap identification and prioritisation
- False positive rate analysis per rule
- Rule overlap and redundancy identification
Sigma Rule Development
- Priority detection gap rule development
- Rule testing against sample and synthetic log data
- Platform translation (Splunk / Sentinel / QRadar / Elastic)
- Threshold calibration and false positive validation
- ATT&CK technique and data source tagging
Rule Lifecycle Management
- Detection-as-code workflow design (Git-based)
- Rule create, review, test, deploy, tune, and retire process
- Rule quality scoring framework
- Detection backlog management and sprint prioritisation
- Scheduled rule review and refresh cycle
SOAR Automation Development
- High-frequency alert type identification and prioritisation
- SOAR automation workflow design and development
- Enrichment automation (IP, domain, and hash lookups)
- Containment action automation (account disable, network block)
- Automation testing and L1 handover documentation
Our Detection Engineering Approach
Detection Audit & Coverage Baseline
Audit the existing rule set for quality, coverage, false positive rates, and maintenance status. Build the ATT&CK coverage baseline map and identify the highest-priority detection gaps based on your threat profile and sector.
Detection Backlog Development
Build a prioritised detection backlog of rules to be created, improved, or retired. Prioritise by ATT&CK technique criticality, threat intelligence relevance, and log source availability. Agree sprint cadence with your SOC team.
Rule Development Sprints
Execute detection engineering sprints: develop Sigma rules for priority backlog items, test against log data, translate to platform format, and deploy through the review and deploy pipeline. Track ATT&CK coverage improvement per sprint.
SOAR Automation Development
Identify the highest-frequency, lowest-complexity alert types in the SOC queue. Design and develop SOAR automation workflows for each, test end-to-end, and hand over to L1 analysts with full operational documentation.
Metrics Framework & Lifecycle Embedding
Establish detection quality metrics (MTTD, false positive rate, ATT&CK coverage %) and implement the rule lifecycle management process. Train the SOC team on detection-as-code workflows and hand over the detection backlog for ongoing management.
Frequently Asked Questions
Deliverables
ATT&CK Coverage Map
Before-and-after ATT&CK coverage heatmap showing detection coverage improvement, residual gaps, and priority areas for future development sprints.
Sigma Rule Library
All new and improved detection rules in Sigma format, version-controlled and tagged with ATT&CK techniques, data sources, severity levels, and false positive guidance.
SOAR Automation Workflows
Developed and tested SOAR automation workflows for high-frequency alert types, with operational documentation and analyst handover guide.
Detection Audit Report
Full audit of the existing rule set covering coverage gaps, false positive rates, redundant rules, and quality scoring — the foundation for the detection backlog and improvement roadmap.
Detection Backlog
Prioritised backlog of detection rules to be developed in future sprints, with ATT&CK mapping, required log sources, and complexity estimates for sprint planning.
Rule Lifecycle Process Documentation
Detection-as-code workflow documentation covering Git-based version control, review gates, testing procedures, deployment pipeline, and scheduled review cadence for sustainable rule maintenance.
Request a Security Assessment
Tell us about your environment and security objectives. We'll design a bespoke assessment and deliver a detailed proposal within 48 hours.