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Description

In today's landscape, every organization functions in a mobile capacity, encompassing remote employees, independent contractors, and executives and sales teams constantly on the go. As collaboration on sensitive materials increases, so do the risks associated with security errors and insider threats. Conventional perimeter-based security measures fall short in delivering the necessary visibility and business continuity sought by security and IT departments. Safeguarding intellectual property, as well as customer and employee data, demands more than just preventative strategies. Relying heavily on prevention leads to numerous blind spots, even after dedicating extensive time to data discovery, classification, and policy development. Consequently, responding to data breaches in real-time becomes unfeasible, often requiring days or weeks to connect the dots between DLP, application, and forensic logs. In this evolving threat landscape, users themselves have become the primary security perimeter, making it crucial for security teams to extract meaningful context from various logs regarding suspicious user and data activities, a task that is often labor-intensive and frequently unmanageable. Organizations must adapt their security strategies to effectively address this new reality.

Description

Reveal Security ITDR detects identity threats - post authentication - in and across SaaS applications and cloud services. Powered by unsupervised machine learning, it continuously monitors and validates the behavior of trusted human users, APIs and other entities, accurately detecting anomalies that signal an in-progress identity threat.