Imagine a 24/7 digital security guard that has watched your company’s computers and network long enough to know exactly what “normal” looks like. The moment something behaves strangely — a laptop logging in from two countries at once, a server suddenly talking to an unknown system, or data moving at odd hours — it raises a flag, even if that specific attack method has never been seen before.
Organizations struggle to spot subtle, fast-moving cyber threats (insider threats, zero‑day attacks, account takeovers, data exfiltration) hidden in huge volumes of logs and network activity. Traditional rule-based security tools miss novel attacks and generate too many false positives for human teams to handle. AI anomaly detection automates pattern analysis to surface genuinely suspicious behavior in real time.
Combination of proprietary threat telemetry (endpoints, identities, cloud, network), continuously updated behavioral baselines, and integration into existing security operations workflows (EDR/XDR, SIEM, SOAR).
Classical-ML (Scikit/XGBoost)
Time-Series DB
High (Custom Models/Infra)
Real-time processing and storage of massive, high-velocity security telemetry (endpoints, network, identities, cloud) while keeping false positives manageable.
Early Majority
Emphasis on behavioral anomaly detection in cybersecurity contexts (user behavior analytics, endpoint and network anomalies, identity misuse) rather than generic anomaly detection for business metrics, with tight integration into threat intelligence and incident response workflows.