Abstract

Security operations centers continue to wrestle with alert volumes that dwarf analyst capacity. High false-positive rates erode trust in detection pipelines, cause genuine threats to be overlooked, and accelerate analyst burnout. Detection quality engineering applies software engineering discipline to the alert lifecycle - treating detections as code with tests, versioning, and performance metrics - and has emerged as the most reliable path toward signal-to-noise ratios that operational teams can sustain.

The Structural Causes of Alert Fatigue

Alert fatigue is not simply a tuning problem. It is a systemic consequence of how detections are created and maintained. Most organizations accumulate rules over years without deprecation: a rule written for a vulnerability patched in 2022 continues firing in 2025 because no one owns the lifecycle. Vendor-supplied content bundles often ship with intentionally broad logic to maximize coverage, pushing false-positive responsibility onto buyers. Log schema drift - fields renamed or dropped by infrastructure upgrades - silently invalidates match conditions, producing floods of null-field matches that look like detections.

Splunk, Microsoft Sentinel, and Elastic Security all provide large default rule libraries. Without a quality gate, teams enable rule packs wholesale and inherit alert volumes they cannot triage.

Detection-as-Code and Performance Baselines

Detection-as-code frameworks such as Sigma provide vendor-neutral rule syntax, enabling version control in git and peer review via pull requests. Sigma rules can be transpiled to Splunk SPL, KQL, or Elastic EQL using the sigma-cli toolchain. This allows detections to be tested against sample log datasets in CI before deployment, catching schema mismatches before they reach production.

Key performance indicators for individual detections include: true positive rate (validated by purple team exercises or unit tests with replay data), daily volume, and mean time to close. Rules firing more than a configurable threshold - say 50 alerts per analyst per shift with a close rate below 5 percent - are candidates for suppression or rework. MITRE ATT&CK annotation on each rule helps prioritize: a rule mapping to a high-impact technique with zero true positives in six months warrants review, not perpetual operation.

Panther and Chronicle both offer rule testing workflows with historical replay. For teams on Elastic, the detection-rules repository maintained by Elastic Security provides a reference implementation of CI-tested, versioned detection content.

Suppression Strategy and Contextual Enrichment

Suppression should be dynamic, not static. Static allow-lists for known-noisy sources become stale when infrastructure changes. Instead, enrichment pipelines can attach asset context - whether a host is a CI runner, a production database, or a developer workstation - and route alerts through risk scoring before they reach the analyst queue.

Techniques from the SANS Detection Innovation group recommend clustering related alerts into incidents rather than surfacing each raw alert. Grouping ten authentication failure events from the same source IP into a single “brute-force pattern” incident reduces queue depth without losing signal. Elastic Security’s correlation rules and Splunk’s Risk-Based Alerting (RBA) implement this model.

Governance and Continuous Improvement

Detection quality requires ownership. Assigning a detection engineer to each rule family - with SLO accountability for false positive rate - closes the lifecycle gap. Quarterly “detection retrospectives” that review which rules contributed true positives during real incidents, and which fired without ever producing a valid finding, provide the data needed to justify deprecations. The goal is not zero alerts but a queue where each alert has a credible path to a real finding.