Real-World Results: Fixing AI-Generated Code at Scale

It’s one thing to talk about the promise of AI code fixing — it’s another to actually see it work in the wild. In this case study, we break down how a global software company used Mobb to fix thousands of vulnerabilities in AI-generated code — in just a few days. The result? A backlog cut in half, MTTR down by 80%, and developers who could finally stop chasing security tickets and get back to shipping code.

The Challenge: AI Coding at Scale, With No Safety Net

This customer had recently adopted AI coding assistants across their engineering org — including GitHub Copilot and Cursor — to accelerate development velocity.

But they quickly ran into problems:

• SAST tools flagged tens of thousands of issues in newly generated code
• Manual triage was slow, repetitive, and error-prone
• Developers were overwhelmed by false positives
• Security leaders were under pressure to show progress before the next compliance audit

Related: Secure by Default: How to Make AI Code Generation Safe in Production

The Solution: Mobb’s AI-Powered Code Remediation

The team integrated Mobb directly into their existing SAST + CI/CD workflow, using it to:

• Ingest scanner results (from Checkmarx and Fortify)
• Automatically triage findings, eliminating false positives
• Apply deterministic fixes directly in GitHub pull requests
• Track and report on remediation progress for compliance and risk metrics

The Results (seen after 7 Days)

📉 6,000+ issues fixed automatically
📈 80% reduction in mean time to remediation (MTTR)
✅ Zero developer context-switching — fixes were reviewed and accepted inline
🔁 Workflow scalability proven — adopted by 3 additional teams by week’s end
🔐 Compliance-ready reporting generated for PCI DSS and SOC 2

More context: How to Integrate AI Code Fixing into CI/CD Workflows

Developer Feedback

“I didn’t have to chase anyone. Mobb opened a PR, the fix was clean, and I just merged it. That’s the dream.”
— Senior Software Engineer, Backend Platform

“This is the first time we’ve had a tool that actually fixes security problems instead of just pointing fingers.”
— AppSec Lead

Why It Worked

This success wasn’t just about AI — it was about applying the right kind of automation:

• Deterministic, safe fixes that didn’t break builds
• No new tools to learn — it worked inside their GitHub PR flow
• Smart triage logic that respected developer time
• Clear ROI for security leadership

Want similar results? 5 Problems AI Code Fixing Solves for AppSec Teams

Conclusion: From Backlog to Breakthrough

AI-generated code isn’t slowing down. But with the right remediation approach, neither is security. This case study proves that fixing code at scale — safely, automatically, and continuously — is not only possible, it’s happening. With Mobb, AppSec teams can move from overwhelmed to optimized.

Try Mobb today and see how quickly you can clear your backlog. Get started here