Build Fast, Stay Secure: Guardrails for AI Coding Assistants

AI code tools are here — but so are the risks

AI coding assistants like GitHub Copilot and Google Gemini Code Assist are changing how developers work — accelerating delivery, removing repetition, and giving teams back time to build.

But speed isn’t free. Studies show that around 27% of AI-generated code contains vulnerabilities, not because the tools are broken, but because they generate code faster than most teams can review it. The result? A growing wave of insecure code is making it into production.

So, how do you unlock the benefits of AI without increasing your risk? Policy, forming a crucial part of AI governance is a starting-point, but where should you begin to give effect to policy?

The answer is simple: guardrails.

Not rules. No restrictions. But smart, developer-friendly checks that let you scale AI safely.

Dive into the four key types of AI security guardrails and how to make them easy for developers to adopt. When you’re ready to go deeper, check out our step-by-step implementation guide.

Start with PR checks — your first line of defense

One of the simplest and most effective ways to reduce security risk from AI-generated code is to start with pull request (PR) checks. Snyk’s PR checks integrate directly into your existing development workflows, scanning new code for vulnerabilities before it’s merged into the main branch. They’re easy to configure, centrally managed, and provide immediate feedback, making them a practical first step for teams rolling out AI code tools.

Moreover, Snyk Agent Fix equips developers to auto-remediate in their PRs in seconds, with autonomously generated and pre-validated auto-fixes that will not introduce new vulnerabilities into your code, and context-aware fix explanations that guide developers to make an informed choice on their preferred auto-fix.

Snyk’s PR checks also display all the context needed to remediate security issues without ever leaving the PR. This means that these PR checks are not a point of friction, because developers have the tools to immediately and rapidly act on the highlighted vulnerabilities and move on, without breaking their rhythm.

For organizations with more complex CI/CD environments, PR checks can be reinforced with Snyk CLI integration in the build pipeline. This creates a second layer of defense, ensuring that risky code doesn’t slip through before deployment.

But while these protections are valuable, they are ultimately reactive. By the time the issue is flagged, the developer has already written, tested, and possibly built functionality around vulnerable code. Rewriting or refactoring at this stage introduces delays, disrupts flow, and adds frustration.

Security gates like PR checks are essential, but only part of the story. To truly support secure AI adoption, organizations must match the pace of AI and move even earlier in the lifecycle, stopping insecure code before it’s committed, not just before it’s merged. That’s where shifting left comes in.

Shift left, save time: Secure AI code at the source

While PR checks are a great safety net, they come into play after the code is committed, often too late to avoid costly rewrites. The smarter move? Shift security left and catch vulnerabilities at the source, as code is being written, before bringing in PR checks and auto-remediation in PRs as an additional layer of security to sweep up any remaining security issues.

Catching vulnerabilities in source code is where Snyk’s local scanning capabilities come in. Whether developers write code in their IDE or generate it through agentic tools like Cursor or GitHub Copilot, Snyk can be there, scanning for security issues as code is created.

And since detecting issues is only half the story, Snyk Agent Fix is also present in IDEs, delivering 80%-accurate automatic fixes to developers, with all the Snyk Agent Fix features mentioned above. With Snyk abstracting away the work of researching, writing, testing, and implementing the fix, all the developer has to do is select their preferred fix and apply it with a click.

The Snyk IDE plugin supports real-time, in-editor scanning, flagging vulnerabilities before the code is even committed. For more flexible workflows, teams can also deploy Snyk’s local MCP server, which integrates with agents and local environments to ensure AI-generated code is tested from the moment it’s written.

The result? Fewer security surprises downstream, faster delivery, more automation, and less rework. 

Importantly, this approach meets developers where they already are. Aligning security with existing workflows removes friction rather than adding it. Installing the plugin doesn’t solve every problem, but it’s a critical step, a foot in the door that sets the foundation for deeper adoption.

Incentivize, don’t enforce: Carrot-first adoption tactics

Mandates and strict policies may succeed in the short term, but often meet resistance and reduce long-term engagement. When rolling out security tools alongside AI code assistants, the better approach is incentivizing adoption, not enforcing it. Developers are far more likely to embrace guardrails when they see the value and can adopt them on their own terms.

One effective tactic is to make access to AI coding assistants contingent on the local security setup. For example, organizations can ask developers to submit a screenshot showing the Snyk IDE plugin installed and configured before granting a license to GitHub Copilot or Google Gemini Code Assist. It’s a lightweight ask that sets a clear expectation: if you’re using powerful code-generation tools, you’re also responsible for validating that output locally. This “carrot-first” model is lightweight, easy to adopt, and builds the right habits.

From there, a “trust but verify” model helps reinforce adoption. By combining data from the Snyk Developer IDE usage report and your AI coding assistant’s admin logs, you can easily spot gaps where developers use AI tooling without local security checks and address them through coaching rather than confrontation.

To make this even smoother, bake security into the onboarding experience. Standardize development environments with tools like GitHub Codespaces or IDEs that interact with containers, including the Snyk plugin by default.

Pair this with targeted training, such as Snyk Learn’s OWASP Top Ten for LLM and GenAI, to raise awareness about AI-related risks. Snyk’s platform brings this education into developers’ daily workflows as well, linking vulnerability findings in the IDE and in PRs to appropriate, bite-sized Snyk Learn lessons to help developers understand the issues raised, before they auto-remediate with Snyk Agent Fix or create fixes with fix guidance and real-life examples.

Together, these tactics drive adoption and make security feel like an enabler, not a hurdle. And that’s what makes it stick.

Central control, conditional access

For organizations with centralized IT management, there’s an opportunity to go beyond encouragement and embed security directly into access workflows using existing tooling. If your team already uses endpoint management tools like Microsoft Intune, Jamf, or Citrix, you can conditionally grant access to AI tools, but only once Snyk’s security tooling is verified on the machine. This creates a seamless compliance checkpoint that recognizes secure behavior with access to innovation.

Similarly, firewalls and allow-lists can be configured to block API calls to tools like GitHub Copilot or Google Gemini Code Assist unless the required security guardrails, like the Snyk IDE plugin or a local MCP server, are detected. These network-level controls act as a safety net, ensuring AI assistance doesn’t become a security liability.

Importantly, these aren’t draconian measures but pragmatic controls reinforcing trust. Instead of saying “no” to AI tools, this approach says “yes, when it’s safe.” It flips the narrative: secure workflows aren’t the cost of doing business, they’re the gateway to scalable, responsible innovation. 

By pre-configuring environments with AI coding tools and security plugins side by side, security just happens. There is no friction, no forgetting, just secure-by-default setups.

Secure innovation starts with integration

Security guardrails aren’t about slowing teams down or saying “no” to AI, they’re about creating safer, more scalable “yeses.” Whether your organization leans toward open tooling or enforces strict IT controls, Snyk provides flexible, battle-tested ways to embed security into your development workflows.

The goal isn’t to monitor developers, it’s to support them, empowering teams to work faster without compromising safety. From PR checks to IDE plugins and conditional access, each integration point is a chance to shift security left and reduce risk before it becomes a problem.

Teams that align productivity and security from the start will unlock the real promise of AI-assisted development, not just faster code, but smarter, more secure software at scale.

Ready to go deeper? Follow prescriptive steps to secure your AI-generated code with the full implementation guide: “AI Code Guardrails: A Practical Guide for Secure Rollout,” built for engineering leaders and security champions. Uncover real-world examples, rollout tactics, and tips for making security scalable, without slowing your team down.