Do we need a dedicated detection engineer?

Organizations with more than a handful of detection rules benefit significantly from dedicated detection engineering. If you cannot justify a full-time role, assign detection engineering responsibilities to a senior analyst with protected time and treat it as a formal discipline.

Why Sigma over SIEM-native rules?

Sigma is vendor-agnostic, meaning rules can be converted to any SIEM query language. This protects your detection investment if you change SIEM platforms and allows you to share detections with the broader community.

How do we test detection rules without impacting production?

Use a detection testing framework with synthetic or replayed log data. Tools like Atomic Red Team generate telemetry for specific ATT&CK techniques. Run new rules in audit or shadow mode before enabling production alerting.

How many detection rules do we need?

Quality over quantity. Start with 20 to 30 well-tested rules covering the most critical techniques for your environment. Scale to 200 or more as your program matures. Every rule should have evidence of testing and a documented false positive profile.

What is the role of AI in detection engineering?

AI and ML can enhance detection through behavioral anomaly detection, reducing false positives through contextual scoring, and accelerating rule development. However, they supplement rather than replace deterministic rules. A strong detection program uses both approaches.

How often should we assess SOC maturity?

Annually for a full assessment. Conduct lighter quarterly check-ins on priority domains to track improvement against your roadmap. Trigger an ad-hoc assessment after major incidents or significant organizational changes.

What maturity level should we target?

Most organizations should target level 3 to 4 within two years. Level 5 is aspirational and requires significant investment in automation and analytics. The right target depends on your threat landscape, regulatory requirements, and budget.

How do we justify SOC investment to leadership?

Translate maturity gaps into business risk. Show what a level-2 detection capability means in terms of dwell time and potential breach impact. Use industry benchmarks and breach cost data to quantify the ROI of closing specific gaps.

Can a small SOC achieve high maturity?

Yes, but it requires smart prioritization and heavy use of automation. A team of five analysts with strong SOAR automation could outperform a team of twenty with manual processes. Focus on force-multiplying investments.

What frameworks can we use alongside this assessment?

The MITRE ATT&CK framework for detection coverage, SOC-CMM (SOC Capability Maturity Model) for process maturity, and the NICE Cybersecurity Workforce Framework for people capabilities. Use them as complementary lenses.

SOC Maturity Assessment | Hunto AI

Overview

Detection engineering is the discipline of systematically building, testing, deploying, and maintaining threat detection capabilities. It moves detection from an art practiced by a few senior analysts to an engineering discipline with version control, testing, and measurable quality metrics. This guide covers the detection lifecycle, Sigma rule development, ATT&CK coverage mapping, and the practices that make detection-as-code work at scale.

Detection Lifecycle

Threat research: identify techniques to detect based on intelligence and risk
Rule development: write detection logic in a vendor-agnostic format (Sigma)
Testing: validate against known-good and known-bad data in a test environment
Peer review: have another engineer review the rule for logic, performance, and false positive potential
Deployment: push to production SIEM with appropriate severity and response tags
Tuning: monitor false positive rate and refine allowlists and thresholds
Retirement: decommission rules that are no longer relevant or effective

Sigma Rule Structure

Component	Purpose	Example
title	Human-readable name	Suspicious PowerShell Encoded Command Execution
id	Unique UUID for tracking	a1b2c3d4-e5f6-7890-abcd-ef1234567890
status	Development stage	test, experimental, stable
logsource	Data source specification	category: process_creation, product: windows
detection	Rule logic with conditions	selection, filter, condition combinations
level	Alert severity	critical, high, medium, low, informational
tags	MITRE ATT&CK mapping	attack.execution, attack.t1059.001
falsepositives	Expected benign triggers	Administrative scripts, software deployment tools

Detection-as-Code Practices

Treat detection rules like software code. Store them in a Git repository with version control, branching, and pull request reviews. Automate testing using a CI/CD pipeline that validates syntax, runs against test data, and checks for performance impact before deployment. Maintain test cases for each rule: both expected-to-fire samples and expected-not-to-fire samples. Use infrastructure-as-code tools to deploy rules consistently across SIEM environments. This approach enables collaboration, auditability, and rollback when a rule causes problems in production.

ATT&CK Coverage Mapping

Map every detection rule to the specific MITRE ATT&CK technique and sub-technique it covers. Use the ATT&CK Navigator to visualize your coverage and identify gaps. Prioritize coverage for the techniques most commonly used against your industry, as identified by threat intelligence reports like Mandiant M-Trends and CrowdStrike Global Threat Report. Do not aim for 100% coverage immediately. Focus on the techniques that matter most and build outward. A few high-quality detections for critical techniques are worth more than hundreds of untested rules.

Quality Metrics

Track detection coverage as a percentage of prioritized ATT&CK techniques
Measure true positive rate per rule to identify your highest-value detections
Monitor false positive rate and time to tune for each new rule
Track mean time from rule creation to production deployment
Measure detection rule half-life: how long before a rule needs updating or retirement
Report on detection-driven incident discoveries to demonstrate program value

Detection Engineering Guide