Move Fast, Stay Secure: How Engineering Teams Can Govern AI-Generated Code

OpsLevel

April 13, 2025

AI coding tools have gone from novelty to necessity in record time. GitHub Copilot, Cursor, Claude, ChatGPT, and other generative AI tools are now embedded in modern engineering workflows, helping developers write, refactor, and deploy code faster than ever. In fact, 96% of developers now use AI assistants to accelerate their work, and the latest DORA report notes that productivity gains are starting to take shape for teams that have adopted AI tools.

But here’s the paradox: while AI is making it easier to move fast, it’s also making it easier to introduce risk—invisible, systemic, and often overlooked.

As AI-generated code spreads across repos and services, engineering leaders are left asking: How do we maintain velocity without compromising security, standards, or reliability?

AI Code Is Everywhere—But That Doesn't Mean It's Safe

AI-assisted coding is here to stay. It’s not just automating boilerplate, it’s shaping architectural decisions, influencing design patterns, and even suggesting entire services. Teams that embrace it are seeing real productivity gains.

But speed comes with trade-offs. According to Snyk’s research:

Up to 40% of AI-generated code contains vulnerabilities (Copilot Security Study)
75.8% of developers believe AI-generated code is more secure than human-written code, a dangerous overconfidence gap
80% of developers admit to bypassing security measures to save time, trusting AI to generate "secure enough" code

This over-reliance introduces risk into production environments at a scale and pace we’ve never seen before. And with CVEs now being exploited as fast as 22 minutes after a PoC is released (Cloudflare AppSec Trends 2024), traditional security and governance workflows can’t keep up.

The Risk Isn't Just Breaking Things, It's What You Can't See

AI tools are getting pretty good at writing code. But they don’t:

Take ownership to the services they spawn
Write good documentation for what they build
Set up runbooks, alerts, or compliance checks
Worry about infrastructure, rate limits, or architectural concerns
Make sure to use up-to-date libraries

In other words, they don’t think about the system. That’s still your job.

Left unchecked, AI-generated code leads to fragmented ownership, inconsistent standards, and ultimately security blind spots. The pace is too fast for manual oversight, and too sprawling for spreadsheets or tribal knowledge to keep up.

Example:

Your team uses Copilot to scaffold a new service during a hackathon. It’s deployed quickly, gets traffic, and is relied on by two other teams within weeks. But it was built outside of your golden path, and never added to the service catalog. No one owns it. There’s no runbook, no alerts, it uses many outdated libraries and frameworks, and even pulled in some libraries with restrictive licensing.

Multiply this by 20, and you’ve got a governance nightmare.

How to Harness AI Safely

To move fast without creating excessive risk, engineering teams need automated guardrails that provide visibility, enforce standards, and detect issues in real time.

1. Automated Software Catalogs

You can’t effectively control what you can’t see, or don’t fully understand. A complete, continuously updated software catalog is foundational. It ensures that every component has a known owner, status, and set of attributes - from language and framework to criticality and compliance needs - providing you with the visibility and context needed

With AI-powered software catalogs, this isn’t a manual process. OpsLevel uses AI to auto-tag services, infer ownership, and surface gaps in metadata, so you’re not relying on developers to fill in the blanks.

2. Continuous Standards Enforcement

You don’t want to slow developers down with manual reviews and endless checklists. Instead, let your standards run in the background, automatically.

Scorecards in OpsLevel help you define what "good" looks like—whether that’s having a runbook, an on-call rotation, or passing security and quality checks. These run continuously, alerting teams when new issues are identified, or if your standards change.

3. Golden Paths for Engineers

Having a clearly defined set of service templates with up-to-date libraries and frameworks is crucial for risk management when it comes to AI code generation. If your team can leverage these templates in their IDE or authoring tool of choice, or have self-service actions to easily and quickly provision new services from the template, you’ll be setting the stage for more compliant code overall.

4. Real-Time Gap Detection and Campaigns

It’s not enough to know what’s missing. You need to fix it, fast. That’s where campaigns come in.

With OpsLevel, engineering leaders can launch org-wide initiatives (like "every service must have vulnerability scanning" or "upgrade all services to Python 3.12") and track progress in real-time. No more spreadsheet chaos or Slack reminders.

Prioritize by Risk, Not by Noise

Not every service requires the same level of rigor. Services that are public-facing, revenue-critical, or connected to sensitive data should have stricter standards. Use a tiering model to group services by risk and enforce accordingly.

What You Can Do Today

Here are four things every engineering org can do to start governing AI-generated code more effectively:

Audit your service catalog. Identify services that were created or modified with AI. Are they owned? Documented? Secure?
Define a maturity baseline. What does a "production-ready" service look like in your org? Make that explicit and automate it.
Automate visibility. Use a platform like OpsLevel to track gaps in real time across your software ecosystem.

Start small. Pick 5–10 services and run a focused campaign. Prove the value, then scale.

What You Can Do Today

Here are four things every engineering org can do to start governing AI-generated code more effectively:

Audit your service catalog. Identify services that were created or modified with AI. Are they owned? Documented? Secure?
Define a maturity baseline. What does a "production-ready" service look like in your org? Make that explicit and automate it.
Automate visibility. Use a platform like OpsLevel to track gaps in real time across your software ecosystem.
Start small. Pick 5–10 services and run a focused campaign. Prove the value, then scale.

Get the AI Development Readiness Tracker and start understanding your posture today.

AI Can Do More Than Write Code, It Can Help You Govern It

AI isn’t just for writing code. With the right platform, it can help you:

Detect incomplete or missing metadata
Recommend service owners based on commit history
Surface risks to service health and standards compliance

That’s why we’re investing heavily in AI—not just to help teams ship faster, but to help them stay in control. From AI-powered software cataloging to self-service capabilities and developer guardrails, our internal developer portal helps engineering teams successfully drive meaningful change, streamline workflows and connect critical data.

At OpsLevel, we’re helping organizations innovate with speed and confidence.

The Future Belongs to the Prepared

AI is changing how organizations operate, but its rapid advancement and adoption can be challenging to keep up with. While AI has the potential to drive progress, it also carries significant risks that may be difficult to anticipate.

The rise of AI in software development isn’t a trend; it’s a fundamental shift. The teams that establish systems and standards now will be the ones who scale safely later. In a world of accelerating velocity and complexity, governance isn’t optional—it’s essential.

The question is: Will you be ready before it matters most?

Want to see how OpsLevel helps teams manage AI-generated code at scale? Book a demo with our team or explore our library of on-demand demos in our resource center.

If you'd like to know how you stack up against other engineering teams in terms of AI usage and adoption, take our survey and get a copy of the analysis in return.

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