I've spent years working in IT and cybersecurity without a formal degree behind it, which meant learning by actually running things, not just studying them. The pattern I kept running into: small and mid-sized teams running Linux servers with nobody dedicated to watching them. Not because they didn't care, but because a full-time security hire doesn't make sense at that size, and most tools are built assuming you have one. What actually pushed me to build this rather than just complain about it: the tools that did exist for smaller teams were either too basic to catch anything real, or noisy enough that people stopped reading the alerts within a week. Neither of those is actually security. It's just the feeling of it. So I started building an agent that does the boring, unglamorous work "watching a server" actually means, catching webshells, flagging config drift, blocking brute-force sources, quietly enough that people are still paying attention to it six months in, not just week one. The approach changed a lot along the way. The AI audit piece started as a single model reviewing findings, and it just wasn't good enough: too many false positives, not enough judgment calls a human would trust. That became a three-role pipeline instead: one model finds and scores issues, a second validates whether the finding is actually real before it ever reaches a human, and a third does a quality pass. That one change did more for trust in the tool than any single detection feature I added. The other thing that changed everything: before I sold a single subscription, I put SecAI on my own production servers. Not a demo environment, the actual infrastructure running this. That forces a different kind of honesty into the build. If something wasn't good enough to trust with my own uptime, it wasn't good enough to ship to anyone else.

SecAI

AI-powered cybersecurity platform for Linux servers

AI-powered cybersecurity platform for Linux servers