How to Identify and Triage False Positives in SAST Scans

Introduction

False positives in Static Application Security Testing (SAST) create noise, slow down development, and weaken trust in security tools. AppSec teams are often buried under hundreds of alerts—many of which are inaccurate or non-exploitable. As developers become desensitized to the flood of issues, real vulnerabilities risk going unnoticed.

In this guide, we’ll walk through how to reduce false positives in SAST: what causes them, how to build an effective triage process, and where automation (including AI) can eliminate noise and improve signal.

What Is a False Positive in SAST?

A false positive occurs when a SAST tool flags a vulnerability that isn’t truly exploitable due to mitigating code, environment context, or flawed detection logic. These findings create alert fatigue and slow remediation—but not every low-risk issue is a false positive, and not every false positive is harmless.

Train Your Triage Team to Differentiate Between:

• True Positives — confirmed, exploitable vulnerabilities
• False Positives — inaccurate or irrelevant detections
• Acceptable Risks — valid issues with business-justified exceptions

To go deeper into the root causes of inaccurate alerts, in this article.

Establish a Triage Workflow

To manage SAST findings at scale, your team needs a structured, repeatable triage process. Here's a proven approach:

• Automated Pre-Triage: Use filters to ignore known-safe patterns, trusted libraries, or low-impact categories.
• Contextual Review: Evaluate the flagged code in light of data flows, existing controls, and architectural defenses.
• Developer Review: Assign issues to developers who are most familiar with the flagged code.
• Security Validation: Before marking anything as a false positive, the AppSec team should review and validate the justification.

A full step-by-step framework is available here.

Use Issue Tags and Risk Scoring

To surface real risks faster, every finding should be enriched with metadata that reflects:

• CWE category
• Severity score
• Sensitivity of affected assets
• Reachability (can the code be executed?)

This helps dashboards highlight risky true positives while suppressing false noise. You’ll also find that tagging improves cross-team communication and SLA tracking.

Document False Positives Transparently

Never just close a false positive and move on. Instead, track them in your vulnerability management system with details such as:

• Justification (e.g., unreachable code, sanitization earlier in the chain)
• Reviewer and timestamp
• Reference to a secure coding standard or in-place architectural control

This creates an auditable record that improves compliance and trust across teams.

Why False Positives Are a Serious Problem

When false positives go unchecked, they drain security teams and damage developer trust. The result? Slower releases, backlog pile-ups, and unpatched real vulnerabilities.

To learn how this problem erodes DevSecOps maturity here.

Manual vs. Automated False Positive Handling

Triage doesn’t have to be fully manual. Use scripting or automation to:

• Flag unreachable or non-exploitable code
• Highlight issues in low-priority modules
• Enforce review policies for certain code owners or high-value systems

Explore the strengths and trade-offs in this blog here.

How AI Can Help Reduce False Positives

Tools like Mobb use AI to analyze patterns, suppress noise, and recommend validated remediations. They reduce engineering time spent on low-priority issues and increase developer adoption of secure practices.

Learn more in this article here.

How to Prioritize Real Vulnerabilities

Reducing false positives is only one part of the problem. Prioritizing real, exploitable threats requires contextual signals from code structure, input sources, usage paths, and asset sensitivity.

See how to operationalize prioritization here.

Tools That Help Reduce False Positives

From tuned SAST engines to AI-powered triage platforms, the right tools can cut your alert volume in half without compromising visibility.

Browse our curated toolkit here.

Comparing SAST Tools by Accuracy

Some SAST tools are more precise than others. If your current scanner produces constant noise, it may be time to evaluate alternatives with lower false positive rates.

See our feature-by-feature comparison here.

Final Thoughts

Reducing false positives in SAST isn’t just about tool tuning—it’s about improving how security and engineering teams collaborate. With structured triage, transparent documentation, and intelligent automation, you can dramatically reduce noise and help your developers focus on what really matters: fixing real vulnerabilities.