Nearly all your developers are using AI-assisted coding tools to write code at scale, but nearly 50% of the code generated by our AI-assisted coding tools is vulnerable. Developers expose secrets and so on and so forth on all these things. Your developers are not building secure apps or securely designed applications. How do you solve it?

Developers are told to "be careful with AI security" with no concrete definition of what that means in their own pull requests. Prompt injection, unsafe output handling, and RAG data leakage don't show up in a standard SAST scan, so vulnerable AI features regularly ship clean through existing pipelines. Security teams end up reviewing LLM-integrated code manually, one PR at a time, because there's no baseline of secure coding skill to lean on.This certification gives engineering leaders a way to verify, not assume, that developers can write secure code around prompts, retrieval, and model output before those features reach production.

Autonomous and tool-using agents are being built and deployed faster than teams can reason about what happens when one is manipulated. Memory poisoning, unsafe tool invocation, and unclear trust boundaries between agents aren't covered by traditional AppSec or even standard LLM security training — most engineers building agents today have never been shown how one actually gets exploited.

An agent that can take autonomous action is a new class of production risk: it doesn't just return an answer, it does things. Enterprises adopting agentic AI need engineers who understand agent architecture well enough to secure it — least-privilege tool access, sandboxing, safe multi-agent orchestration — before an agent is given real permissions in a real system.