Behavioral Personas 

TOTAL uses LLMs and Reinforcement Learning to build behavioral personas for every employee. Each persona reflects an employee’s role, responsibilities, rhythm of work, and patterns of interaction across systems. These personas evolve over time with continued interaction, enabling the system to distinguish harmless variability from genuine threat indicators. 

Contextual Storytelling 

TOTAL transforms fragmented alerts into a unified storyline that explains not only what happened, but why it happened. By linking motive, opportunity, and probable cause, security teams gain visibility into the full context of user actions. 

Learning Up and Down the Stack 

Modern adversaries rarely rely on one tactic. During their attacks, they conduct reconnaissance, coordinate collusive campaigns, and probe for weaknesses across the enterprise. Surfacing these hidden patterns requires a sustained understanding of each user’s behavior and how it evolves. Without visibility into long-term behavioral patterns, it’s impossible to know whether a low-level anomaly is meaningful or just noise. TOTAL solves this by transforming isolated endpoint and network events into a continuous behavioral story. 

By correlating endpoint, network, and behavioral signals, TOTAL turns raw activity into identity context. Its native low-level signals corroborate or invalidate higher-order behavioral patterns, filtering noise and strengthening detections. This layered approach exposes account drift, collusion, and insider threats that rule-based tools miss.