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Quantum Ventura Applied AI · Cyber · Sensing Request a demo
Network defense

CyberNeuro-RT

Most network defense works from a list of threats someone has already seen. CyberNeuro-RT learns what ordinary traffic looks like on your network, then tells you when something does not fit, including things nobody has catalogued yet.

Real timescored as traffic arrives
Edge or coreone engine, either place
Second layerruns alongside your perimeter
CNRT Console · Network Monitor
LIVE
Packet rate
1.42 M/s
GPU accelerated
Threat score
0.002
Within normal band
Edge draw
4.2 W
Neuromorphic node
// console output
13:42:01.002SNN core 4 · scanning TCP streamCLEARED
13:42:01.018Anomaly signature matched, session isolatedFLAGGED
13:42:01.030Rule pushed to perimeter firewallAPPLIED

Interface preview. Layout is representative of the product console; values shown are illustrative, not measured performance.

How it works

What it actually does.

Four things a security team gets out of it, in the order they matter.

01

It notices what it has not been told to look for

  • Learns the shape of ordinary traffic on your network rather than matching a list of known attacks
  • Surfaces behaviour that does not fit, including patterns that have never been catalogued
  • Keeps learning as the network changes, so the baseline does not go stale
02

It runs where the traffic is

  • The same engine deploys in a datacentre, in a cloud core, or on a low-power chip on a remote sensor
  • Edge deployments draw a small fraction of the power a conventional setup needs
  • No separate product line for edge and core, which means one thing to operate
03

It keeps the alert queue short enough to work

  • Findings are ranked by how likely and how serious they are, not dumped in a list
  • Tuned against false alarms, because a queue nobody can finish is a queue that hides the real thing
  • Built so an analyst can see why something was flagged, not just that it was
04

It sits alongside what you already run

  • A second layer behind the perimeter tools already in place, not a replacement for them
  • Built to reduce the likelihood that an intrusion succeeds, and to surface the ones that do sooner
  • No security product can make an intrusion impossible, and we do not claim otherwise
Where it came from

The record behind it.

Developed with Lockheed Martin's MFC Division and Pennsylvania State University, under partial funding from the U.S. Department of Energy.

PartnersLockheed Martin Co. MFC Division · Pennsylvania State University
FundingPartial funding from the U.S. Department of Energy
DeploymentCloud core, datacentre, or low-power hardware at the edge
PositionSecond layer, alongside existing perimeter tooling
Start a conversation

Tell us the program.

Whether you are a program office with a problem, a prime looking for a subcontractor, or an enterprise evaluating CyberNeuro-RT, the fastest route is to describe what you are trying to solve.

Program officesBring the problem, not a specification. Most of our work starts before anyone knows what the answer looks like.
Primes and integratorsWe hold the consortium memberships and the audit history that subcontracting needs.
EnterprisesProduct evaluations for CyberNeuro-RT, Vanguard and CodeLens start with a briefing and a scoping call.
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