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BLUEURAL
Detectingransomware

Intelligence at the edge.
Security by design.

Blueural analyses your network with neural models running on-site, not in someone else's cloud. Threats surface in seconds, and your traffic never leaves the building.

detection log
edge-01

Example detection: Blueural Edge correlates network events on-device and returns a plain language verdict, such as command-and-control beaconing, DNS tunneling used for exfiltration, or an active intrusion caught by a decoy.

analyzed on-device · 0 bytes uploadedHIGH

Most networks are already telling you something is wrong

The signal is in the flow records, the DNS queries and the login attempts. The problem is volume — nobody has time to read it. Blueural runs neural models against that stream on-site and hands you the few things that matter.

Neural detection on-device

GPU-accelerated models score network, DNS and device behaviour as it happens.

Nothing leaves your premises

Traffic and logs stay inside your network. Only alerts and metadata travel.

Detection without the round trip

Local inference means findings are not gated on your upload bandwidth.

One view across sites

Flows, servers, IoT and decoys land in a single prioritized incident list.

Built for generalists

Alerts read as sentences, not raw events. No security specialist required.

Out-of-band by design

The appliance never sits in the forwarding path, so it cannot break your network.

How it works

From network tap to a sentence you can act on.

01

Deploy

Mirror traffic from your switch or install a passive TAP. The appliance stays out of the forwarding path.

02

Analyze

Neural models process flows, DNS queries and logs continuously, on the device itself.

03

Detect

Anomaly and pattern models identify malware, lateral movement and brute-force attempts.

04

Act

Prioritized incidents arrive in Sentinel with the affected device and recommended next step.

The hardware

Built on NVIDIA Jetson

Running neural models against live traffic needs real compute in the building. Jetson modules deliver GPU inference measured in hundreds of INT8 TOPS inside a fanless power envelope, which is what makes local analysis viable without a server room.

  • CUDA and TensorRT for compiled model throughput
  • DeepStream pipelines when camera feeds are in scope
  • Multiple models in parallel — flow anomalies and vision at once
  • Tens of watts, not hundreds, so it lives in a comms cabinet
How we use it →
carrier board · compute modulefanless enclosure

Compact

~40 INT8 TOPS · 8 GB

Single-site, light traffic

Standard

~100–157 INT8 TOPS · 16 GB

Most deployments

Extended

~200–275 INT8 TOPS · 32–64 GB

Heavy traffic, more cameras

Let's talk about your network

Tell us how your environment is laid out and we will walk through what deployment would look like.

Get in Touch