I’ve spent time talking to engineering teams about their AI coding assistant adoption, and I keep hearing the same story: developers love the productivity boost, but security teams are drowning in a new category of issues they are not equipped to handle.
Here is what changed. When GitHub Copilot and similar AI coding tools started generating new code in many organizations, we did not just get faster development. We shifted where security decisions happen. That code suggestion appearing in your Integrated Development Environment (IDE) is not simply completing a line. It is making architectural choices, pulling in dependencies, shaping patterns, and influencing long-term application security posture.
Most organizations still treat this as a developer productivity story when it is actually a software supply chain and secure coding story.
The most underestimated risk is not the AI-generated code itself, but the dependencies these tools introduce. When a developer accepts an AI suggestion that imports a convenient library, they are rarely thinking about the transitive dependency chain that comes with it.
I recently saw a single accepted AI-generated code suggestion bring in 47 dependencies. Two had known critical vulnerabilities and one had not been maintained in three years.
Traditional Software Composition Analysis (SCA) runs too late in the process to prevent this. By the time your SCA tool flags issues in CI and CD, that library choice has already influenced other parts of the codebase. Other teams have adopted it. The refactoring cost has gone from 30 minutes to three days.
This is why forward-thinking DevSecOps teams are shifting security left by moving checks from the pipeline into the IDE itself at the moment a suggestion is accepted.
The solution is not blocking AI assistants or interrupting developers every few seconds with warnings. That approach always leads to tool abandonment.
The most effective strategy treats the AI coding assistant as part of your security architecture rather than as a separate productivity tool.
This means configuring your development environment so that before any code suggestion is accepted, it is automatically validated against your security and quality policies.
These checks must feel instant. Developers will only keep them if they run in milliseconds and do not break the flow state.
Run your existing codebase through attribution analysis to understand how much AI-generated code you have and what dependency patterns it is introducing.
Do not wait for a perfect solution. Start with basic dependency policies and secure coding checks directly in the IDE at the moment of code acceptance.
Your attack surface now includes the training data, suggestion logic, and dependency recommendations of your AI coding assistants. Your threat model should reflect that reality.
There is much more to explore. Expect future conversations about confounder and collider bias in AI-generated code and how LLM drift affects secure development practices.
The teams succeeding here are not choosing between velocity and security. They are redesigning their security architecture so it can operate at the speed of AI-assisted development.
If you want tighter control over dependency risks and secure coding patterns at the moment code is written, Kiuwan can help. Kiuwan integrates with the IDE to analyze vulnerabilities, enforce policies, and support true shift-left security.
Start a free Kiuwan trial and explore what IDE-first security looks like in practice.
JD Burke is the Director of Security Products at Sembi with more than 20 years of experience in product management and application security. He has held senior technical roles at Snyk, CyberRes/Fortify, and Kiuwan, with expertise across SAST, SCA, and DevOps integration. JD combines hands-on security knowledge with product leadership, guiding cross-functional teams through planning, feature development, and market positioning while maintaining deep expertise in vulnerability assessment and compliance frameworks.
