The security audit email arrived on a Tuesday morning. "We've identified three instances where API keys were exposed in your codebase. Two appear to have been generated by AI coding tools. We need to discuss your AI governance practices."
For the VP of Engineering at "PayFlow" (not their real name), this was the moment abstract concerns about AI coding tools became an urgent business problem.
"I knew we had shadow AI issues," she told us. "Developers were using whatever tools they wanted. Copilot, Cursor, ChatGPT, Claude—I couldn't even tell you what percentage of our code was AI-generated. That audit email made it real."
The Starting Point
PayFlow Before the Wake-Up Call
PayFlow is a Series B fintech startup processing payments for small businesses. They handle sensitive financial data. Their customers include healthcare providers and legal firms—industries with strict compliance requirements.
The company had enthusiastically adopted AI coding tools. "We saw the productivity gains," their CTO explained. "Developers were shipping faster. Code reviews seemed fine. We didn't think deeply about governance until the problems started."
The Three Incidents
Hardcoded API Key in Production
A junior developer asked Copilot for help with a third-party API integration. The generated code included a placeholder API key format that the developer replaced with a real key—then committed to their public GitHub repository. The key was scraped by automated bots within hours.
Impact: Compromised API access, $4,200 in fraudulent API charges, 8 hours of incident response.
SQL Injection Vulnerability
AI-generated code for a search feature didn't properly parameterize database queries. The vulnerability was discovered during a routine penetration test—before any exploitation—but could have exposed customer data.
Impact: Emergency patching, delayed product launch, mandatory security review of 6 months of commits.
Dependency Confusion Attack
An AI tool recommended a package with a name similar to an internal PayFlow library. The suggested package was a typosquatted version that could have exfiltrated environment variables. A senior developer caught it during code review—by luck.
Impact: Near-miss. Led to the comprehensive audit that prompted this case study.
"The third incident was the one that scared me most. It wasn't exploited, but it easily could have been. We got lucky. That's when I knew we couldn't keep operating this way."
The 90-Day Fix
PayFlow's leadership made AI governance a priority. They didn't have a massive budget or months to implement. Here's what they did in 90 days.
Phase 1: Discovery (Weeks 1-2)
Before fixing the problem, they needed to understand its scope.
What They Discovered
- 6 AI tools in active use: GitHub Copilot (official), Cursor (some teams), ChatGPT (widespread), Claude (growing), Tabnine (legacy), and a smaller open-source tool
- ~35% of recent code was AI-assisted: Based on a sampling analysis of commit patterns and developer surveys
- No correlation between AI use and seniority: Senior developers used AI tools as much as juniors—just for different tasks
- Zero standardized review process: Code reviews focused on logic, not AI-specific risks
"The discovery phase was humbling," the CTO admitted. "I thought maybe 15-20% of code was AI-generated. It was closer to 35%. And that's just what we could measure."
Phase 2: Policy (Weeks 3-4)
With data in hand, they built a governance framework.
The Policy Framework
Approved tools: GitHub Copilot (enterprise tier only) and Claude (via API with logging). Everything else required explicit approval.
Mandatory practices: All AI-generated code must be reviewed by a human familiar with the feature area. No AI-generated code in security-critical paths without security team review.
Forbidden patterns: No pasting internal code into public AI tools. No using AI for credential management, cryptography, or authentication flows.
Transparency requirement: Developers must flag AI-generated code in commit messages.
The hardest part wasn't writing the policy—it was getting buy-in.
"Developers pushed back. They felt like we were treating them like children. We had to explain: this isn't about trust in you, it's about trust in the tools. AI makes mistakes. We need systematic ways to catch them."
Phase 3: Tooling (Weeks 5-8)
Policies mean nothing without enforcement. PayFlow implemented technical controls.
Pre-commit hooks: Automated scanning for hardcoded secrets, SQL injection patterns, and known vulnerable dependencies. Blocks commits that fail checks.
CI/CD integration: SAST (Static Application Security Testing) runs on every pull request. AI-specific vulnerability patterns added to the scanner ruleset.
Audit logging: All interactions with approved AI tools are logged. Not to punish developers—to identify patterns when issues occur.
Visibility dashboard: A simple dashboard showing AI tool usage by team, flagged issues by category, and compliance metrics.
Phase 4: Training (Weeks 9-12)
Technical controls catch problems. Training prevents them.
Training Modules Developed
- "AI Code Review" (2 hours): What to look for when reviewing AI-generated code. Common vulnerability patterns. When to request security review.
- "Secure AI Prompting" (1 hour): How to structure prompts to avoid common issues. What not to include in prompts.
- "Incident Response" (1 hour): What to do when you suspect AI-generated code caused a problem. Escalation paths.
"Training was where we saw the biggest attitude shift," the VP of Engineering noted. "Once developers understood why we were doing this—and saw real examples of what can go wrong—resistance dropped. Most people want to do the right thing. They just need to know what the right thing is."
The Results
Before and After: Key Metrics
The 847 pre-commit catches are particularly telling. "Those are 847 potential problems that would have made it into our codebase without governance," the CTO pointed out. "Some were minor—code style issues, missing tests. But 23 were genuine security vulnerabilities. That's 23 incidents we didn't have."
What Didn't Work
Not everything went smoothly. Here's what PayFlow would do differently.
The Friction Problem
Initial pre-commit hooks were too aggressive. False positive rate was ~15%. Developers started finding workarounds to skip checks. After two weeks, they tuned the rules—false positives dropped to ~3%, and compliance went back up.
The Logging Backlash
Some developers felt the audit logging was surveillance. Leadership had to explicitly clarify: logs are for incident investigation, not performance review. They also made logs accessible to developers themselves, not just management.
The Productivity Dip
First month saw a ~10% productivity decrease as developers adjusted to new processes. By month three, productivity had recovered and slightly exceeded baseline. The team attributes this to catching bugs earlier, reducing rework.
What They'd Tell Other Teams
We asked PayFlow's leadership what advice they'd give to other companies facing similar challenges.
"Don't wait for an incident. Start with visibility—just knowing what AI tools people are using and how much code is AI-generated. That data changes the conversation."
"Make it about the code, not the developers. Frame governance as 'AI makes mistakes, we need to catch them' rather than 'we don't trust you.' That framing matters."
"Budget for training. The technical controls are necessary but not sufficient. People need to understand why this matters and how to work safely with AI tools."
The Bottom Line
PayFlow spent approximately $45,000 on their 90-day governance implementation (tooling, training development, productivity dip). They estimate their three pre-governance security incidents cost approximately $35,000 in direct response costs—not counting reputational risk, delayed launches, or the near-miss that didn't get exploited.
"The ROI math is clear in hindsight," the CTO concluded. "But the real value isn't cost savings. It's peace of mind. I can tell our board and our customers that we have visibility and control over AI in our codebase. Six months ago, I couldn't say that honestly."
A Note on This Case Study
PayFlow's experience is one company's journey. Your situation may be different. The solutions that worked for them might not fit your context. We've presented this as a concrete example, not a template.
If you're facing similar challenges, we'd recommend starting with the discovery phase—understanding your current state—before implementing solutions.