Research & methodology
Detection grounded in research.
Maigadi's core didn't come from a marketing brief. It grew out of academic research into unsupervised, self-baselining anomaly detection for OT networks — with proven accuracy in evaluation.
Research-backed
The detection approach was developed and validated in academia, not retrofitted from IT tooling.
Novel & validated
Detection built on novel algorithms developed and validated in academic research — with every alert backed by evidence you can verify.
Built to be checked
Detection claims should be verifiable. Where we can, we evaluate on public ICS datasets so results can be reproduced, not just asserted.
The method, in brief
Learn your normal — and know what healthy looks like.
Maigadi pairs two lenses: it learns the unique rhythm of your network (unsupervised, self-baselining), and it applies what a healthy OT network should look like from engineering first principles and standards like IEC 62443. Every detection is explainable and mapped to MITRE ATT&CK for ICS, and the baseline is frozen during an anomaly so it can never be poisoned. For the full walk-through, see how it works.
Coming soon
The State of OT Detection.
We're preparing a flagship report on the state of OT detection — what's working, what isn't, and where the gaps are. Want a copy when it lands? Let us know.