Detection Engineering for SOC Leaders — Part One: From Fragmented Rules to a Managed Detection Practice

Introduction
Detection work too often looks like firefighting: someone writes a rule, it goes live, and analysts shoulder the consequences: noisy alerts, unclear ownership, and rule rot. If you lead a SOC in 2025, your job is to replace ad-hoc firefighting with a managed practice—one where detection is treated as a lifecycle, and detections are assets with owners, tests, and retirement criteria. This first installment sets the tone: why a lifecycle matters and what your leadership priorities should be to make detection engineering sustainable.

Why a lifecycle — and why now
The tools got smarter, but operations didn’t. Vendors and open-source communities ship hundreds of detection templates, and modern EDR/SIEMs market broad ATT&CK “coverage”—yet SOCs still drown in false positives and suffer uneven coverage. The root cause isn’t tooling; it’s process. Without a defined lifecycle, detection content decays: telemetry changes, assets evolve, and rules stop working. A lifecycle makes the process visible, repeatable, and accountable.

Leadership priorities: three imperatives

Make detection ownership explicit
Every detection needs an owner. That could be a detection engineer, product owner, or a rotating stewardship role in the SOC. Owners are accountable for test coverage, tuning cadence, and retirement decisions.
Demand measurability
Replace subjective statements ("this rule works") with measurable acceptance criteria: required fields, expected true-positive scenarios, acceptable false-positive thresholds, and performance budgets.
Reduce blast radius with staged rollouts
No untested detection should ring the production alarm. Use staged states (dev, test, staging, prod) and shadow-mode rollouts so you can measure impact before waking analysts at 3 a.m.
Designing practical lifecycle states
Think of your lifecycle as lightweight but enforceable. Here’s a minimal set SOCs can adopt quickly:

Intake / Backlog: Capture requests, map to business impact and ATT&CK technique, and assign priority.
Development: Rule implementation in a feature branch with at least one unit/regression test.
Validation: Run against synthetic telemetry or adversary emulation; measure TP/FP and runtime cost.
Staging (Shadow): Run in the environment but route alerts to a dev triage queue.
Production: Alerts are actionable and tied to response playbooks.
Sunset: Formal retirement with rationale and archived artifacts.
Detection-as-code: practical adoption steps for leaders
You don’t need to rewrite your SOC overnight. Start with pragmatic, high-ROI milestones:

Put new detections into version control. Treat a rule change like a code change.
Enforce PR reviews for detection changes — this brings peer review and knowledge sharing.
Automate basic tests: synthetic telemetry runs, simple FP checks, and a linting pass.
Define a “minimum spec” for new detection requests so engineers have the data they need to implement and test effectively.
Vendor and community content: curate, don’t enable indiscriminately
Vendor bundles and community templates are useful, but flipping them all on creates noise and hides real gaps. Leaders should insist on a curation pipeline:

Tag vendor/community rules as “candidate” until they pass validation.
Shadow-run third-party rules and measure FP/TP before enabling.
Maintain a compact policy: criteria for enablement, sensor targeting, and owner assignment.
MSSPs/MDRs: set governance expectations
If an MSSP or MDR operates your detection stack, leadership must build governance into the engagement:

Require transparency: change logs, deployment evidence, and a mechanism to request rule changes.
Negotiate quality SLAs: false-positive remediation time and improvement plans for low-quality content.
Consider a hybrid model: let the provider handle operational execution but keep control of rule roadmaps and acceptance testing.
What leadership dashboards should show (not just “coverage”)
Vanity coverage numbers create false comfort. Dashboards that drive action report:

Validated ATT&CK coverage (techniques with a validated detection and a passing test).
Mean time to detection (MTTD) for critical techniques.
Analyst time per alert (triage cost) and false-positive rate.
Detection decay: percent of detections whose test outcomes degraded over time.
First 30–90 day plan

Days 1–7: Inventory your detection library and tag by owner, last-reviewed date, and test presence.
Weeks 2–4: Introduce lifecycle states and require a one-paragraph spec for new requests.
Month 2: Pilot detection-as-code for a small set of high-value rules, add basic CI tests.
Month 3: Shadow vendor rules and baseline key metrics (FP rate, MTTD). Publish first leadership dashboard with actionable metrics.
Conclusion and leadership mantra
Detection engineering is an organizational habit, not an individual talent show. Your role is to remove blockers—often data, sometimes budget, and always process—and to institutionalize modest engineering practices that reduce noise and improve reliability. Start by defining the lifecycle, insisting on ownership and measurability, and treating vendor content as candidates until validated. With those building blocks, your SOC will become less reactive and more predictive.