Insider Threat Detection Built for Proactive Defense 

Legacy insider threat tools like DLP and UEBA focus on static file tracking and post-incident analysis. They help form a legal case after sensitive data has left your environment, but do little to prevent breaches proactively. Static rules for file tagging and access control are easily circumvented, and state-of-the-art systems still miss collusion, sabotage, and the exploitation of information for real-world theft. 

TOTAL Insider Threat was built differently, transforming detection from file tracking to diagnostic threat prevention. At its core is a privacy-preserving inference engine that learns each user’s unique patterns over time. Rather than a one-size-fits-all model, it builds clustered personas across the enterprise — enabling the system to separate harmless variability from true indicators of risk. 

By unifying signals from communications, HR systems, browsers, endpoints, and networks, TOTAL provides early warnings that static DLP cannot. Organizations can also inject their own contextual signals from case management or internal tools to sharpen accuracy and adapt detection to their unique environment. 

Key Differentiators 

Learning System: TOTAL applies LLMs and Reinforcement Learning to continuously learn each user’s normal patterns. The system adapts as roles and routines evolve, ensuring only meaningful deviations are elevated as risks. This dynamic learning reduces false positives and sharpens detection over time. 

Black Swan Detection: Surfaces rare but high-impact threats like sabotage, privilege abuse, or collusion that static rules consistently miss. 

Proactive by Design: Instead of flagging policy violations after the fact, TOTAL anticipates intent and trigger points, allowing action before insider threats materialize. 

Customer-Specific Signals: TOTAL incorporates offline activity and customer-specific inputs such as case management or proprietary tools. These signals surface threats unrelated to data exfiltration and allow detection to adapt to the unique workflows, policies, and risks of each organization. 

Privacy Controls: Granular options for data collection, processing, and residency ensure alignment with internal policy and enterprise risk posture. 

Solving Use Cases Beyond DLP 

TOTAL Insider Threat moves beyond postmortem file tracking, diagnosing intent through dynamic models and correlated signals. It delivers early warning of collusion, sabotage, and the broader spectrum of insider risks before breaches occur: