Behavioral AI Core
Unsupervised models build a real-time baseline for every entity in your environment — users, processes, network flows, cloud APIs. Anomalies score by deviation, not by signature.
Ethereon learns behavior — not signatures. By modeling every user, process, and packet in real time, it surfaces novel attack patterns 48–72 hours before any CVE is published — and contains them before damage spreads.
Legacy EDR/SIEM tools fire on patterns that already exist in databases. Ethereon fires on behavior — which means it sees the attacks no one has named yet.
Unsupervised models build a real-time baseline for every entity in your environment — users, processes, network flows, cloud APIs. Anomalies score by deviation, not by signature.
Heap sprays, ROP chains, novel C2 channels — Ethereon recognises exploit shape, not its CVE. Average lead time over public disclosure: 48–72 hours.
Detect, isolate, remediate, report — without waiting for a human to wake up. Sub-3-minute MTTR with built-in playbook framework.
Models train across the global Ethereon fleet without leaking customer data — every new attack we see makes every customer safer.
One-click audit exports for ISO 27001, GDPR, PCI DSS, HIPAA, NIS2, and SOC 2. Evidence is collected continuously, not at audit time.
Edge inference modules run on endpoints with <3% CPU. Protection works even when the cloud connection is severed.
Simulated feed from a single mid-market deployment. The actual platform processes billions of events per day across the Ethereon cloud.
From raw telemetry to autonomous remediation, here's how Ethereon turns noise into outcomes.
Endpoint, network, cloud, identity, and SIEM data is normalized and streamed into the inference plane in real time. Native collectors for Splunk, QRadar, Elastic, Sentinel, Wazuh.
Per-entity ML models build continuously-updated baselines: what's normal for this user, this process, this network flow, this cloud role. The baseline is the truth — anything else is noise to be scored.
An ensemble of Random Forest, LSTM, Isolation Forest, and a transformer-based context model produces a 0.0–1.0 anomaly score with confidence and MITRE ATT&CK tactic mapping.
{
"entity": "user:k.tanaka",
"event": "credential_access",
"anomaly_score": 0.94,
"confidence": "HIGH",
"tactic": "T1003 - Credential Dumping",
"recommended_action": "isolate_endpoint",
"ts": "2026-04-25T11:24:08Z"
}
A proprietary pattern library identifies exploit shapes (heap sprays, ROP chains, shellcode signatures, novel C2 cadence) without ever needing a CVE. Unsupervised clustering surfaces never-before-seen TTPs.
Confirmed threats trigger response playbooks: network isolation, process termination, evidence preservation, MFA invalidation, alerting. All within seconds, all auditable.
Evidence is collected continuously and exported on demand into ISO 27001 / GDPR / PCI DSS / HIPAA / NIS2 / SOC 2 audit packs. Auditors get one URL, not a year of email threads.
Real-time fraud-pattern detection, transaction-graph anomaly modeling, and regulator-ready evidence packs.
Learn moreNation-state APT detection, air-gapped deployment, federated learning across agency silos.
Learn moreCritical-infrastructure protection, signaling-plane monitoring, lawful-intercept alignment.
Learn moreHIPAA-aligned audit pipelines, medical-device behavioral fingerprinting, ransomware-resilience.
Learn moreSOC-in-a-box for teams without a 24/7 analyst rotation. Mid-market pricing, enterprise capability.
Learn moreOT/IT convergence, ICS/SCADA behavioral profiling, NIS2 alignment for EU operators.
Learn moreLong-form research and weekly threat briefings live on the CybernytronX blog — Ethereon's parent organization.
We're working with a small group of design-partner customers right now. Drop your email and we'll send you the launch invite the moment we're public.