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. To unlock the benefits of AI without increasing risk, organizations should implement smart, developer-friendly checks known as guardrails, which are not rules or restrictions, but rather checks that let developers scale AI safely. One way to start is with pull request (PR) checks, which integrate directly into development workflows, scanning new code for vulnerabilities before it's merged into the main branch, and can be reinforced with Snyk CLI integration in the build pipeline. To truly support secure AI adoption, organizations must shift security left by catching vulnerabilities at the source, as code is being written, rather than just after it's committed. This can be done using local scanning capabilities and IDE plugins that deliver automatic fixes to developers. Incentivizing adoption rather than enforcing it is also crucial, with tactics such as making access to AI coding assistants contingent on local security setup or providing targeted training to raise awareness about AI-related risks. Centralized control and conditional access can also be used to embed security directly into access workflows using existing tooling. By pre-configuring environments with AI coding tools and security plugins side by side, security just happens, and teams that align productivity and security from the start will unlock the real promise of AI-assisted development.