Good security should not
require a security team.
Blueural Network LLP builds network threat detection for organizations that will never staff a security operations centre — which is most of them.
Why we started
The tooling that detects network intrusions properly has largely been built for organizations with analysts to operate it. Everyone else gets a firewall, antivirus and hope — then finds out about a breach weeks later, usually from someone else.
That gap is not really about budget. It is about the assumption that somebody is watching. A mid-sized manufacturer, a clinic or a fifteen-person engineering team has the same exposure and none of the staffing.
So the product is shaped around that constraint. An appliance that installs in an afternoon, analyses traffic where it already sits, and produces a short list of things worth attention rather than a dashboard nobody opens.
Where we are
- Founded
- Blueural Network LLP
- Based in
- Coimbatore, Tamil Nadu, India
- Focus
- On-premise detection for small and mid-sized networks
- Hardware platform
- NVIDIA Jetson class modules
How we make decisions
Four positions that settle most arguments about what to build next.
Privacy by architecture
Analysis happens on the appliance. This is a structural decision, not a policy one — the data cannot leak from a cloud we never send it to.
Proven tools over novelty
Suricata, TensorRT, DeepStream. We add intelligence on top of components that already work rather than reinventing the base layer.
Measure before claiming
Detection numbers are easy to quote and hard to reproduce. We publish method alongside result, or we stay quiet until we can.
Written for generalists
Alerts should read like "this laptop contacted four malicious addresses in five minutes" — not a raw event that needs an analyst to decode.
The work
A small team spanning three disciplines, because the product needs all three to be credible.
Embedded and edge engineering
Getting models to run reliably on constrained hardware, in a fanless box, indefinitely.
Detection and data science
Building models that hold a low false-positive rate on networks we have never seen.
Security research
Turning current attacker behaviour into detections that survive contact with reality.
Think we are wrong about something?
We would genuinely rather hear it now. If you run a network that does not fit the picture above, that is useful to us.