Topic starter
20/08/2026 5:42 pm
TL;DR: Security teams still spend 50–80% of their AppSec effort manually triaging scanner findings, while 78% of alerts go uninvestigated, according to Pixee and the Nagomi CISO Pressure Index 2025. The real bottleneck is not detection quality but governance over what is worth human attention, and that changes how identity, secrets, and code-risk programmes should be run.
Editorial analysis by NHI Mgmt Group, based on content published by Pixee: “How to Reduce False Positives by 80%: A Triage Automation Framework”.
Key questions
Q: What breaks when application security teams rely on manual triage and ticketing for every finding?
A: Manual triage slows response, increases operational fatigue, and lets real risks linger while teams sort through noise.
Q: Why do false positives create governance risk in application security?
A: False positives create governance risk because they train developers to distrust security output.
Q: How do you know if triage automation is working?
A: You should see fewer findings sent to developers, a higher percentage of those findings proving real, and a shorter time from validated issue to fix.
Practitioner guidance
- Define closure categories for every AppSec finding Separate true false positives, accepted risk, and risk re-scoring so each outcome carries a different evidence standard and review path.
- Measure the proportion of findings that receive human review Track how many alerts are actually investigated, how many are auto-closed with evidence, and how much engineering time is spent adjudicating noise.
- Automate context checks before escalation Use reachability, authentication boundary, and deployment-context analysis to suppress findings that are not exploitable in the real environment.
Bottom line: AppSec teams do not merely need more findings ranked higher. They need a governance model that can decide which findings deserve human attention and which do not.
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20/08/2026 6:25 pm
AppSec triage has become an access-governance problem for security attention. When teams spend most of their time deciding what is worth reviewing, they are effectively managing privileged human attention as a scarce control. That shifts the governance question from detection coverage to decision rights, evidence thresholds, and closure policy. For IAM and NHI programmes, the lesson is direct: if a finding cannot be classified reliably, neither secrets hygiene nor code risk governance will scale cleanly.
A question worth separating out:
Q: What is the difference between triage automation and prioritisation?
A: Prioritisation ranks findings by importance, but triage automation decides whether a finding is worth human attention at all. Prioritisation still leaves the review burden in place. Triage automation removes false positives, accepted-risk items, and non-exploitable cases before they consume scarce security capacity.
👉 Read our full editorial: Triage automation for AppSec findings is now a governance problem