DTEX Platform

Models attacker-in-residence scenarios (pre-resignation data staging, after-hours privileged access, bulk download exceeding peer norms), with risk scores decaying appropriately for resolved anomalies.

Builds behavioral baselines per user account, device, and application, capturing access timing, resource usage patterns, and activity volumes specific to each entity rather than aggregate thresholds.

Combines multiple weak behavioral signals into a single risk score per user or entity, ranking which accounts warrant investigation so analysts focus on the highest-risk anomalies.

Maintains baselines over 30-90 day windows to normalize for seasonal variation, role transitions, and legitimate behavior changes, reducing false positives from periodic patterns.

Monitors and enforces data movement policies on endpoints, blocking or logging USB transfers, clipboard operations, print jobs, and screen captures of content matching classification policies.

Detects and controls sensitive data entered into generative AI tools, applying block, redact, or warn actions before data leaves the organization.

Correlates DLP policy violations with user behavioral context, distinguishing routine data movement from anomalous exfiltration patterns associated with insider threat or account compromise.

Applies sensitivity labels to data automatically based on content analysis and context without requiring users to manually classify documents before policy enforcement.

Applies preventative controls automatically such as blocking, encryption, alerting and user justification when sensitive data is detected.

Scores user risk dynamically based on role and behavior to prioritize data loss incidents.

Integrates with SIEM platforms for incident response.