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Monad Blog cover reading State of Detection Engineering 2026: What the Data Reveals.
Photo: Monad Blog, featured cover for its State of Detection Engineering 2026 coverage of the SANS and Anvilogic survey (fair use).

Opinion

Detection Engineering's False Positive Gap Is Ownership, Not Tooling

The 2026 SANS and Anvilogic State of Detection Engineering survey finds most teams track false positive rates while few prioritize cutting them, and vendor default rules remain the main noise source.

Elena Vos, OpinionLondon4 min read

LONDON - Detection engineering programs keep proving they can measure noise. The harder finding in the 2026 State of Detection Engineering Report, a SANS Institute survey developed with Anvilogic, is how few of those programs treat cutting that noise as work they own.

SANS and Anvilogic 2026 survey pie chart on whether teams are keeping pace with emerging threats.
Chart: SANS Institute and Anvilogic survey via Monad Blog, how well teams are keeping pace with emerging threats (fair use).

The survey draws on more than 300 security practitioners across more than 10 industries (SANS cites 307 respondents). On the Anvilogic report landing page, 59 percent of teams say they track false positive rates, yet only 14 percent prioritize reducing them. That is a 45-point gap between measurement and action. It is not a story about missing dashboards. It is a story about missing owners.

SANS and Anvilogic 2026 survey bar chart of top false positive sources, led by vendor-provided rules.
Chart: SANS Institute and Anvilogic survey via Monad Blog, top false positive sources; vendor-provided rules lead (fair use).

Vendor defaults explain much of the volume those dashboards record. Sixty-six percent of false positives originate from vendor-provided rules, a share that barely moved from 64 percent in the 2025 cycle. Monad's public read of the same SANS and Anvilogic dataset shows the pattern holds across organization sizes, with vendor rules the top false positive source for small, midmarket, and enterprise respondents. Generic content ships into environments it was never tuned for. Analysts inherit the residue.

SOCtember illustration of the measurement-versus-action gap: 59 percent track false positive rates, 14 percent prioritize reducing them.
Illustration: SOCtember, measurement versus action: 59 percent of teams track false positive rates, 14 percent prioritize reducing them, per Anvilogic.

AI does not close the ownership gap on its own. Eighty-three percent of practitioners use AI tools today, according to the Anvilogic landing summary, while only 42 percent trust those tools for core work such as tuning detections. Adoption without trust is another form of measurement theater: the capability is present, the accountability for production detection quality is not. Cloud-native environments, named by 43 percent as the top detection coverage gap, widen the same problem. Coverage debt and false positive debt compete for the same scarce engineering hours, and without an explicit reduction priority the easier choice is to keep scoring the metric.

SOCtember illustration of a detection ownership path from vendor defaults and AI use to named owners and false positive reduction priorities.
Illustration: SOCtember, conceptual detection ownership path from vendor defaults to named owners and false positive reduction priorities.

This is distinct from treating alert fatigue as an analyst character flaw. The live SOCtember Opinion on alert fatigue argued that fatigue is manufactured upstream in the detection pipeline. The Anvilogic and SANS numbers sharpen a narrower claim: even when teams instrument false positives, most still do not prioritize the engineering work that would shrink them. Pace data underlines the cost. Eighty percent of practitioners say they are barely keeping pace with or falling behind the evolving threat landscape; only 18 percent report staying ahead. Noise that is measured and not owned becomes permanent drag on that pace.

The operating fix is unglamorous. Assign named owners for high-volume vendor and internal rules. Put false positive reduction on the same backlog as new coverage. Regression-test noisy detections against representative telemetry before promotion. Treat AI output as a draft that still needs human validation against the local environment. Tooling can help each of those steps. Tooling cannot invent the priority that 86 percent of surveyed teams have not yet set.

Sources:


Elena Vos covers SOC strategy and detection operations for SOCtember from London.

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