Real-time detection of anomalous privileged user activity. Machine learning baselines, automated risk scoring, and zero-trust response — before bad actors can cause damage.
Continuous monitoring across all privileged identities. Every action scored, every anomaly surfaced, every threat classified — instantly.
Four stages transform raw activity logs into actionable threat intelligence, with automated response that stops threats before escalation.
Collect audit logs from PAM vaults, Active Directory, cloud platforms, and endpoint agents. Normalize events into a unified timeline schema regardless of source format.
ML models establish a unique behavioral fingerprint per identity — typical login hours, common access patterns, session duration, and credential usage frequency.
Dynamic deviation scoring compares live activity to baselines in real-time. Composite risk scores factor behavioral signals plus external threat intelligence context.
Policies trigger MFA challenges, session termination, or alerting based on risk thresholds — stopping privilege escalation and lateral movement without human delay.
Session recordings, forensic audit trails, and timeline visualization give security teams full context for every incident — supporting compliance and post-incident review.
Continuous reporting on risk trends, access patterns, and policy violations enables data-driven security hardening and executive-ready posture summaries.
Every node represents a privileged identity. Edge intensity reflects interaction frequency. Pulsing nodes are active anomalies. Click a node to simulate investigation.
Click each category to explore the specific threat indicators PBA detects.
Adjust the behavioral parameters below to simulate how PBA calculates composite risk scores for a privileged user identity.