Solution
AI-Assisted Analysis
A reasoning layer over raw events.
Conventional tools emit events. Blueural adds interpretation: models that score anomalies, models that recognize attack patterns in metadata, and summarization that explains what a cluster of events represents.
What it covers
- Anomaly scoring over flow characteristics, DNS volume and packet sizing
- Pattern models compiled for accelerated inference on the appliance
- Unsupervised baselining that learns what is normal for your network
- Generated summaries describing correlated events in plain language
- Suggested response steps for recognizable incident classes
What you get
Running this locally is the point. GPU acceleration on the appliance makes on-premise analysis practical without a server room, and keeps traffic metadata inside the building.
The analysis stack
- Anomaly detection — statistical and tree-based models over flow features
- Pattern recognition — compiled models for throughput on edge hardware
- Behavioural baselining — models trained on your traffic, not a generic corpus
- Narrative generation — summarization of multi-event incidents
Why inference at the edge
Local inference removes the cloud round trip, so detection is not gated on upload bandwidth. It also means traffic metadata never has to leave the premises to be useful, which is the privacy argument for the whole architecture.
Does this match a problem you have?
Tell us about your environment and we will show you how this would apply.
Get in Touch