CyberNeuro-RT
Most network defense works from a list of things somebody has already seen. That leaves a gap: the attack nobody has catalogued yet looks like ordinary traffic. CyberNeuro-RT closes that gap by learning your network instead of memorising someone else's.
Segment B · host 10.4.22.8
A single host began reading and rewriting files across three shares at a rate no user on this network has ever produced. No signature was involved. The host was isolated before any file was written back in an encrypted state.
It finds what it was never told to look for
No signature required.
Signature tools can only recognise an attack that has already been described to them. CyberNeuro-RT builds a picture of what normal looks like on your network and reports the traffic that does not fit, whether or not anyone has named it yet. The picture keeps updating, so it does not go stale as your network changes.
Segment B · host 10.4.22.8
Outbound volume and timing on this host stopped matching the profile the sensor learned for it. No signature was involved. Nothing about this pattern had been seen on this network before.
It runs where the traffic actually is
One engine, three placements.
The same engine deploys in a cloud core, in a datacentre, or on a low-power chip attached to a sensor at a remote site. An edge deployment draws a small fraction of the power a conventional setup needs, which is what makes it practical somewhere with no rack and no cooling. One product to operate, not a separate edge line.
It respects how much time your analysts have
A queue short enough to finish.
A queue nobody can finish is a queue that hides the real thing. Findings are ranked by how likely and how serious they are, tuned hard against false alarms, and presented so an analyst can see why something was flagged rather than just that it was. It sits behind the tools you already run and is built to reduce the chance an intrusion succeeds, not to promise none ever will.
Outbound volume and timing stopped matching the profile learned for this host
First connection to a destination never seen from this segment
Session length outside the envelope learned for this sensor
Built at Quantum Ventura.
Built at Quantum Ventura. It learns the network it is deployed on rather than arriving with a model trained on somebody else's traffic, and it runs as a second layer alongside the perimeter tooling already in place.
| Position | Second layer, alongside existing perimeter tooling |
|---|---|
| Detection | Learned on your own network, not shipped pre-trained |
| Deployment | Cloud core, datacentre, or low-power hardware at the edge |
| Interface | Multilingual, English and Japanese in place today |
Tell us the program
and the problem.
We reply from San Jose, usually within two working days.