A cloud-native SIEM centralizes security data — logs, identity, network, and application telemetry — from across your cloud workloads, then uses correlation, machine learning, and behavioral analytics to detect, prioritize, and respond to threats in real time. It’s built on scalable data-lake architecture for the telemetry volumes modern cloud generates.

Traditional SIEMs lean on static, signature-based rules and batch processing, which miss novel attacks and bury analysts in false positives. A cloud-native, AI-driven SIEM adds behavioral analytics (UEBA), real-time correlation into attack sequences, and risk-based triage — catching what signatures miss and surfacing the few incidents that matter.

AI correlation groups related alerts into higher-level incidents, and risk scoring weighs asset criticality, user privilege, threat-intel confidence, and behavioral context — so analysts see the handful of real incidents instead of thousands of flat-severity alerts.

User and Entity Behavior Analytics builds a behavioral baseline for each user, device, and application, then flags deviations — off-hours access, unusual data volumes, role-inconsistent activity. It’s essential for catching insider threats, compromised credentials, and lateral movement, since those use legitimate access and never trip signature rules.

Yes. Detections are mapped to MITRE ATT&CK techniques, so analysts can see which tactic and attack stage they’re facing and what to expect next — and a coverage heatmap shows exactly where detection gaps remain.

AWS, Azure, GCP, and OCI are supported as first-class citizens, alongside on-prem, under one normalized detection model — giving you unified visibility rather than separate exports reconciled after the fact.