Detecting Malicious Insiders and Imposters by Monitoring User Journeys in Enterprise Applications
- Detect abuse, misuse and malice in applications
- Ubiquitous for and across SaaS and custom-built applications
- Unprecedented signal-to-noise ratio based on User Journey Analytics
Detecting Attackers
and Malicious Insiders
in Business Applications with User Journey Analytics
- Detects fraud and security threats at the application layer
- Supports custom-built, cloud and SaaS applications
- Unprecedented signal-to-noise ratio based on User Journey Analytics
User Journey Analytics - Increased Accuracy, Reduced TCO
Application User Journey Analytics
User Journey Analytics is based on the analysis of activity sequences performed while using an application, in contrast with UEBA, which analyzes each activity on its own and then builds a timeline. The sequence provides enriched context for anomaly detection and enables better accuracy based on journeys, as opposed to outcomes. Higher quality alerts means security analysts reduce wasted time on false positives.
Multiple Journey Profiles per User
Leveraging RevealSecurity’s patented clustering engine - tailored for clustering user journeys - TrackerIQ automatically learns multiple journey profiles per user across applications. Detecting anomalous journeys by comparing users to themselves and their peers provides an extremely high signal-to-noise ratio.
Application Agnostic Detection
The detection model is applied to any SaaS or custom-built application, analyzing user journeys in and across applications to detect anomalies. This model eliminates the need to understand business application logic and no rules are required.
Application Layer Detection and Response
TrackerIQ complements XDR solutions at the business application layer to detect threats and fraud. Existing detection solutions are focused on the infrastructure layers and on access to business applications, resulting in very limited effectiveness against malicious insiders as well as external attackers who succeed to bypass authentication mechanisms.
Updates
Trusted by
