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June 30, 2026

Trellix AI Data Risk Dashboard Demo

The Trellix AI Data Risk Dashboard enables organizations to monitor and control AI-related data risks by tracking user activity, detecting sensitive data exposures, and providing actionable insights across endpoints, networks, and cloud environments.

This video was created and contributed by, Frank DeRosa, Trellix.

The Trellix AI Data Risk Dashboard is a new feature available for free with Trellix EPL on Prem and DLP entitlements. The dashboard enables organizations to track and control AI risk by monitoring user attempts to upload sensitive information to AI platforms. The dashboard integrates with DLP endpoint, DLP network, and Skyhigh Security for cloud-to-cloud transactions, providing comprehensive coverage. 

The dashboard consists of six portals, each offering unique insights. It tracks over 400 AI-related URLs, showing which destinations users access most frequently. Administrators can monitor top classifications based on policy, such as PII, PCI, source code, and HIPAA, to identify where sensitive data is at risk. Actions taken, including block and no action (monitoring mode), are clearly displayed, helping organizations understand their current security posture. 

Exfiltration vectors are analyzed, with web protection, clipboard protection, and screen capture being the main methods users might attempt to use for data transfer. The dashboard leverages OCR technology to detect sensitive information even in images, ensuring robust protection. User groups and individual users with violations are identified, allowing for targeted investigation and remediation. Integration with Active Directory enhances visibility into user activity and group associations. 

Incident details are accessible through the dashboard, including evidence, classifications, rules triggered, and audit logs. Stakeholders can be assigned to incidents, and cases can be created for users with repeated violations, streamlining escalation to HR or legal if necessary. The dashboard's filtering and drill-down capabilities make it easy to analyze incidents and refine policies, supporting a proactive approach to AI data risk management. 

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