Using AI to Automatically Triage and Fix SAST Findings

Using AI to Automatically Triage and Fix SAST Findings

Static Application Security Testing (SAST) is essential—but the findings it produces often overwhelm teams. Between false positives, misprioritized vulnerabilities, and developer pushback, AppSec teams are spending more time sorting issues than fixing them.

That’s where AI comes in. Modern AppSec platforms now use AI to triage, prioritize, and even fix vulnerabilities automatically—right inside your existing workflows.

This article explains how AI improves accuracy, saves time, and restores trust in the security review process.

Still working through a high volume of false alerts? Start with How to Reduce False Positives in SAST.

Where AI Fits in the AppSec Workflow

AI doesn’t replace your SAST scanner—it enhances what happens after scanning:

1. Triage
   AI filters out false positives and classifies findings by reachability, severity, and exploitability.
2. Prioritization
   It assesses business logic, data flow, and input context to score real risk.
3. Remediation
   AI generates secure code fixes tailored to your codebase and recommends them as pull requests or IDE suggestions.

Learn how to build a triage model AI can support in How to Build an Effective SAST Triage Workflow.

Benefits of AI-Powered Remediation

• Eliminates manual triage bottlenecks
• Saves developer time by delivering fixes instead of just findings
• Improves consistency across teams and projects
• Reduces MTTR (Mean Time to Remediate)
• Builds developer trust by removing noise and providing context-aware fixes

To understand how prioritization plays a role in all of this, see How AppSec Teams Can Prioritize Real Vulnerabilities Faster.

What Makes AI Remediation Safe?

Tools like Mobb combine:

• Static analysis results with
• Contextual information from the codebase and
• Secure coding standards

This enables the AI to produce safe, tested, and mergeable fixes that integrate directly into developer workflows—GitHub, GitLab, Bitbucket, or IDEs.

Unlike generic AI suggestions from chat-based models, these remediations are specific to the vulnerability, scoped to your environment, and reviewable in seconds.

From Detection to Resolution—Automatically

Let’s say your scanner flags a potential SQL injection in a legacy module:

• AI confirms the input isn’t sanitized
• Confirms the function is reachable in production
• Generates a fix using parameterized queries
• Opens a PR tagged to the code owner

No context-switching, no backlogs, no drawn-out security reviews.

This kind of automation is what makes platforms like Mobb not just scanners—but remediation engines.

Want to see how Mobb stacks up against other tools?
Top SAST Tools Compared by False Positive Rate.

Final Thoughts

AI is changing the game in AppSec. From triaging SAST findings to delivering safe, contextual fixes, it helps teams do more with less—and move fast without compromising on security.

Manual triage still has its place, but for modern teams dealing with scale, AI isn’t a luxury—it’s a necessity.

To understand the tradeoffs, compare Manual vs. Automated False Positive Triage: Pros and Cons.