We’re building Appcan, a security testing platform. While running scans ourselves, we kept hitting the same problem: the reports were accurate, but hard to act on.

Most scan outputs are long lists of findings with severity labels, but very little context. You still have to decide what actually matters, what to fix first, and what can wait.

So we added an AI interpretation layer on top of existing scan results.

Instead of just showing raw findings, the AI reads the report as a whole and produces a short, structured summary explaining what the real risks are, why they matter, and where to focus first. It doesn’t re-scan anything or invent data; it only interprets what’s already there.

The goal isn’t to replace security engineers. It’s to reduce the cognitive load for teams who need to understand risk quickly and move forward.

We’re still early and learning. I’d love to hear from people who deal with security reports: does this kind of AI-generated insight actually help, or does it create new problems?

Happy to answer questions and get feedback.