I tested a GenAI agent on real cybersecurity scenarios and it surprised me

I recently came across a platform that lets you spin up custom GenAI agents without writing much code. Out of curiosity, I tried building one for a use case I rarely see discussed outside enterprise tools, a quick threat triage for small teams.

The idea was simple. Feed the agent basic logs or threat descriptions and ask it to suggest next steps. Not alerts or dashboards but contextual follow ups. Like asking, “This looks like a port scan, what should I check next” or “This file was flagged, how can I validate it manually”

I used Lyzr to test it in a controlled setup with mock data. What stood out was not the accuracy but the way it handled uncertainty. When the input was vague, it asked for clarification. When it lacked context, it acknowledged that instead of guessing. That felt new.

It was not perfect. It struggled with very technical payloads and made conservative assumptions. But for exploratory questions or narrowing down false positives, it was surprisingly helpful.

Has anyone else here experimented with GenAI in this way? Not to replace analysts but to make the loop tighter between suspicion and action I feel like there is a lot of unexplored ground in using agents for reasoning rather than reporting.