Topic starter
02/08/2026 12:31 pm
TL;DR: Its private beta data showed Assistant marked 230 findings as likely false positives, with 95% of recorded responses agreeing and true positives 1.5x more likely to be fixed, while false positives were 2.2x more likely to be ignored, according to Semgrep. The real shift is not AI novelty but faster triage, tighter prompts, and better confidence handling for security workflows.
Editorial analysis by NHI Mgmt Group, based on content published by Semgrep: “AI & Cybersecurity: Learnings from three months of Semgrep Assistant”.
Key questions
Q: What should security teams do first when AI scanners produce more findings than analysts can triage?
A: Start by separating validated, exploitable findings from unverified output and route them into distinct queues.
Q: Why do AI-assisted security reviews still need human triage?
A: Because the model can misread context, inflate severity, or miss compound weaknesses that only become meaningful when combined.
Q: What are the signs that AI-assisted autofix is not ready for broad use?
A: Warning signs include fixes that look plausible but fail compile checks, require hidden changes in other files, or produce low-quality code that still needs heavy manual repair.
Practitioner guidance
- Define finding-specific prompt templates Separate instructions for security, performance, and code quality findings so the model uses the right triage criteria for each category.
- Add context that suppresses known-noise patterns Include narrowly scoped guidance for situations such as test-file findings where the security concern is lower or different from production code.
- Use a separate confidence gate for autofix Score proposed fixes for compile risk, cross-file dependencies, and likely implementation failure before they reach engineers.
Bottom line: AI-assisted triage is useful when it reduces security alert noise while preserving a clear human decision point for ambiguous findings.
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02/08/2026 1:28 pm
AI triage is now a control plane for review quality, not just a productivity feature. The important governance question is no longer whether AI can summarize findings, but whether it can reliably reduce analyst noise without masking real risk. That shifts attention toward prompt design, category routing, and measurable decision quality. Security teams should treat AI triage outputs as governed recommendations, not unquestioned conclusions.
A question worth separating out:
Q: How should security teams govern AI agents that triage code vulnerabilities?
A: Security teams should treat code-triaging AI agents as governed production services, not prompts with a UI. That means binding each decision to exact repository state, separating suggestion from approval, and limiting read and write access to the minimum scope needed for the task. Without those controls, the agent can be accurate in appearance while being unreliable in practice.
👉 Read our full editorial: AI-assisted code triage changes how security teams handle finding noise