AI-generated code is everywhere. One in five organizations has already had a serious security incident tied to AI-generated code. CISOs need to know who’s using what, where AI code enters the pipeline, and what risks come with it.
The old audit playbook doesn’t cut it anymore. You need visibility into the agentic development lifecycle — the ADLC, as some are calling it.
Start by recording every AI tool your developers use. Sanctioned or not, map them directly to code outputs. You can’t govern what you can’t see.
Then benchmark those tools. Test them against known vulnerability patterns. Standardize on the ones that produce secure code. Track model context protocol (MCP) integrations to make sure AI agents only connect to approved tools and data sources.
The best LLMs perform comparably with skilled developers on some security tasks — flagging code smells and anti-patterns, for instance. But they struggle with DoS protection, insufficient logging, and misconfigured permissions. Top security-proficient developers still outperform LLMs. Average developers don’t.
Upskilling matters. Build a risk score for your team, similar to a credit score, that considers each developer’s skills, practices, and oversight capabilities. Know who can spot AI-introduced vulnerabilities and who needs help.
Finally, link AI tool deployment to business outcomes. Connect the audit data to productivity, code quality, and security metrics. That’s how you decide which tools are worth the investment and where you need to pull back.
None of this is about slowing down development. It’s about making sure the speed AI brings doesn’t run straight into a wall.
