If you’ve spent any time in a modern dev shop lately, you’ve probably noticed how much AI has crept into our daily workflow. From auto-generating boilerplate code to assisting with documentation and even suggesting test cases, AI has become the quiet extra team member on every project.
But here’s the thing most people aren’t talking about: your security and compliance team probably has no idea how or where AI is being used in your development lifecycle.
I saw this firsthand while working on a system modernization project for a large enterprise last year. Half of the developers were quietly pasting snippets into ChatGPT for help with edge cases, while the other half were experimenting with GitHub Copilot in VS Code. And yet, when I asked the security team about AI policies, they just shrugged—they weren’t tracking any of it.
Why This Matters
In 2025, companies are moving fast to integrate AI, but compliance hasn’t caught up. Recent surveys confirm what I’ve seen:
- 98% of security leaders admit they lack visibility into how developers are using AI in the pipeline.
- 82% say they worry about unapproved AI introducing vulnerabilities or IP exposure.
This is more than just a “policy problem”—it’s a risk multiplier:
- Unvetted AI-generated code can introduce subtle security flaws.
- Regulated industries (finance, healthcare, government) can’t perform proper audits without knowing which AI was involved.
- Licensing & data compliance can be violated if training data or outputs aren’t handled correctly.
Imagine pushing code to production, only to discover later that a piece of functionality was 80% written by a model that was trained on questionable open-source code. Suddenly, your legal and security teams are in firefighting mode.
Bridging the Visibility Gap
The solution isn’t to ban AI or slow down developers—it’s to build visibility into the workflow. Here are a few strategies I’ve seen work:
- Create an AI usage inventory:
Track which teams are using Copilot, ChatGPT, or other AI tools and for what purposes. A simple internal survey or Slack check-in can kickstart this. - Tag AI-generated commits:
Some forward-thinking teams now include metadata in commit messages to indicate AI-assisted contributions. This provides traceability without shaming developers. - Integrate compliance into CI/CD:
Tools like SonarQube and Snyk can help flag security risks in AI-generated code, but custom scripts that check for compliance tags are even better. - Educate devs about IP & data risk:
The biggest risk comes from developers unknowingly pasting sensitive code into public AI tools. A quick lunch-and-learn session can prevent massive headaches.
A Personal Take
I love AI-assisted development. It’s made my life easier as a consultant, and I’ve seen teams ship faster than ever before. But speed without visibility is a ticking time bomb.
I recommend that every engineering leader treat AI visibility as a critical first step toward long-term compliance. The earlier you know what’s happening in your pipelines, the easier it is to implement policies without slowing down innovation.
If your team is experimenting with AI today, start documenting it now. Six months from now, your compliance officer (or your future self) will thank you.
