What a healthy OT network looks like
There’s a quiet advantage in OT security that IT security doesn’t have: industrial networks are boring, in the best possible way.
OT has a heartbeat
A well-run control network is deterministic. Controllers poll on fixed intervals. Devices talk to a small, stable set of peers. The traffic mix barely changes from hour to hour. Compared with the churn of an enterprise IT network, OT has a heartbeat — periodic, predictable, and engineered.
That predictability is a gift for detection. When normal is this stable, abnormal stands out.
Two kinds of intelligence
The best OT detection uses two lenses at once:
- What’s normal for this network — learned automatically from the traffic, with no rules to write.
- What a healthy OT network should look like — grounded in engineering first principles and standards like IEC 62443: proper segmentation, sane polling rates, expected protocol behaviour.
The first lens adapts to your environment. The second means you get value from day one: before a site-specific baseline has fully formed, it’s already flagging the misconfigurations and unhealthy patterns that shouldn’t be there.
Why it matters
A tool that only learns “your normal” will happily learn a broken normal. Knowing what good looks like is what lets detection say not just “this changed” but “this shouldn’t be happening at all.”
Maigadi pairs both. It doesn’t just learn your normal — it knows what good looks like.