Use cases · Anomaly detection · Security operations
One alert that
means something.
Security event streams are behavioural sequences — which process chains follow which, which sign-in patterns are that admin’s normal. Senua AI learns your environment’s own behaviour and raises few, explained, confidence-ranked alerts — deployable inside an air gap, where cloud detection products cannot go.
The scenario
11,000 alerts a day, and the real incident was found weeks late.
A critical-infrastructure operator runs a corporate network and an air-gapped OT enclave. The SIEM produces eleven thousand alerts a day; triage burns the team out, and the incident that mattered was buried in the noise for weeks. The AI-detection vendor they trialled could not deploy inside the air gap (cloud-only), could not explain a single alert, and priced per event.
Meanwhile the regulator now requires documented reasoning for incident-response decisions — a score from a black box does not satisfy an auditor.
How Senua AI handles it, end to end
Event logs are already symbol streams — the engine’s native food. No feature engineering, no signature updates, no phoning home.
1 · Learn this environment
Senua AI induces the normal behavioural states of your auth sequences, process chains and flows from your own logs — not a generic model of someone else’s network.
2 · Watch inside the air gap
A single native binary plus local model state — no internet, no GPU, no telemetry leaving the enclave. It runs where policy says cloud AI cannot.
3 · Raise signals, not noise
Behavioural departures surface as confidence-ranked alerts carrying the learned state, the event sequence that departed from it, and the confidence trajectory — a narrative an analyst can action and an auditor can read.
Straight answers
What this is — and what it isn’t.
It learns your normal, continuously.
New systems, new admins, new patterns are absorbed as they appear — no quarterly retraining cycle, no model release process.
Every alert is explainable.
The alert is the reasoning path: state, sequence, confidence. That is what turns an AI detection into evidence a response decision can cite.
It deploys where the data is.
Air-gapped OT enclaves, sovereign environments, classified networks — a CPU binary with no external dependency goes where SaaS detection is disqualified by policy, not preference.
An honest caveat.
An adversary actively tries to look normal. We validate against public red-team datasets with ground-truth event timing before any claim ships — and we publish the protocol so you can check it, not take it on faith.
The public proof — run it yourself
Validated on public red-team data, in the open.
We validate this use case on public labelled security corpora with known attack windows — the Los Alamos comprehensive authentication/process logs (real red-team events with ground-truth timing) and the CIC-IDS network flow benchmarks — with a pre-registered protocol: learn normal states from clean windows, replay the attack windows blind, measure detection latency and false alarms per normal hour against published baselines.
Your logs already hold the story.
Start with a retrospective audit: we replay your historical logs blind and show you which incidents surface as behavioural departures — inside your environment, nothing leaves.