How are you addressing technical debt risk resulting from AI coding tools? Have you established specific processes to manage vulnerabilities in AI-generated code, and are you collaborating with software engineering or other teams using these tools?

AI-assisted coding tools aren’t technical debt creators, they’re technical debt accelerants. If an organization already struggles with code backlog, quality, or maintainability, AI doesn’t introduce new debt; it simply amplifies existing weaknesses by producing more code faster than governance and review processes can handle. The real risk lies not in the code itself, but in the unsupportable conditions that emerge when AI-generated output bypasses established development, testing, and secure coding standards.

Addressing this risk requires cross-functional governance between Security Architecture, Engineering, and DevOps, ensuring that AI-generated code is subject to the same vulnerability management, peer review, and lifecycle controls as human-written code. In short, AI accelerates both opportunity and risk, organizations that mature their secure development and change management processes in parallel will stay ahead of that curve instead of being buried by it.

I really wish we’d use the terms alongside their definitions, but it seems “technical debt” is now being used just as a euphemism for certain developers (in-house or outsourced) incompetence. Bad coding doesn't equal technical debt. A bad developer with an AI alongside him/her, will still be just as bad a developer.

I have noticed that until recently bad developers typically struggled leading to their slower performance compared to good developers.
Now, with AI, they can generate substandard and insecure code faster and have little incentive or opportunity to improve.
The best scenario is when experienced developers use LLMs to write some simple scripts, generate some basic configurations, header comments, and learn some new basic stuff about the programming languages they may not have typically used before. Identify best developers in your teams whose codes have least findings on bug bounty programs. Retain them if you have that decision making capability and arm them with Ai tools to reduce your technical debt on secure codings.