Analysis & detection
What is AI Triage / False-Positive Filtering?
Using a model to assess which raw findings are real and worth acting on, and suppressing or deprioritising the rest.
Scanners over-report by design, since they would rather flag a maybe than miss a real issue. Triage sits on top, reading each finding in the context of surrounding code to judge whether it is genuinely exploitable or reachable.
It is the most direct answer to the reason security tools get abandoned. The risk is symmetrical: a triage layer that wrongly suppresses a real vulnerability has done more damage than the noise it removed, so look for transparency over silent filtering.
Top 18 AI Triage / False-Positive Filtering tools
Every tool in the directory documented as supporting AI Triage / False-Positive Filtering, 13 fully, 5 partially, with what its own documentation says.
Support is not the same as parity, since some implementations are narrower in scope, gated to a higher plan tier, or maintained only for existing customers. The note under each tool is what its own documentation describes.
- 1
AI scores false-positive likelihood with reasoning; opt-in, enabled via support request.
- 2
AutoTriage ranks by exploitability, effort, false-positive probability; Secrets AutoTriage included
- 3
Cloud validates findings in isolation before surfacing; CLI accepts false-positive marks.
- 4Checkmarx OneCode Security Platform
Per-project 'AI Triage & Remediation' auto-analyses eligible new vulnerabilities during PR scans; triage predicates persist across scans.
- 5Claude CodeAgent Coding Tool
Independent verifier agents reproduce findings before reporting, in Code Review, ultrareview and Claude Security.
- 6CodeAnt AIPR Review
Suggestions threshold controls alert sensitivity; cloud findings prioritized by attack path and exploitability
- 7
Deep Scan Verify stage rejects duplicate, mitigated, speculative, test-only findings; Learnings suppress noise
- 8CorgeaCode Security Platform
AI classifies findings as false positive with reasoning; AST fingerprinting dedupes across scans
- 9
Narada model filters false-positive secrets (Team+); AI Review has a dedicated false-positive reporting flow.
- 10GitHub Advanced SecurityCode Security PlatformFree (default rules); add-on: Code Security for custom rules
Dependabot default and custom auto-triage rules filter false positives; curated rules, not documented as AI
- 11GreptilePR Review
Learning suppresses consistently-ignored comment types; security, leaks, null derefs never suppressed
- 12
Autotriage recommends safe-to-ignore with explanation; noise filtering (beta) suppresses likely false positives; consumes credits.
- 13SnykCode Security Platform
Triage Assistant checks exploit conditions for Java Gradle/Maven on GitHub only; reachability de-prioritizes unreachable vulns.
- 14
Prioritizes by organizational impact to avoid treating all issues equally; ML ranks review risk. Not false-positive filtering
- 15
Severity threshold, max-comment cap and ignore_patterns tune noise; no false-positive triage documented.
- 16
Manual triage rules and ignore reasons; aggregated non-identifying data trains smart issue prioritization and grouping
- 17
Finding Recommendation Agent scores finding relevance from PR history; in beta on paid plans.
- 18
Vulnerable-methods reachability and VRM dedup/correlation cut noise; mitigation workflow is manual, not AI triage
What to look for
- Whether suppressed findings remain visible and auditable
- Explanations for each triage decision rather than a bare confidence score
- Measured false-negative rate, not just noise reduction claims
- Whether triage decisions can be overridden and learned from
Related terms
FAQ
What is AI Triage / False-Positive Filtering?
Using a model to assess which raw findings are real and worth acting on, and suppressing or deprioritising the rest.
How many tools support AI Triage / False-Positive Filtering?
18 of the 20 tools tracked in this directory support AI Triage / False-Positive Filtering, 13 fully and 5 partially, including Codacy, Aikido, ChatGPT / Codex, Checkmarx One, Claude Code. Support is not the same as parity, since some implementations are narrower in scope, gated to a higher plan tier, or maintained only for existing customers. The note under each tool is what its own documentation describes.
What should you look for in AI Triage / False-Positive Filtering?
Whether suppressed findings remain visible and auditable. Explanations for each triage decision rather than a bare confidence score. Measured false-negative rate, not just noise reduction claims. Whether triage decisions can be overridden and learned from.