By using ML algorithms, we categorise patterns of user behaviour, to understand what constitutes normal behaviour, and to detect abnormal activity. If an unusual action is made on a device on a given network, such as an employee login late at night, inconsistent remote access, or an unusually high number of downloads, the action and user is given a risk score based on their activity, patterns and time.


Detect Compromised Accounts that have been spoofed, or unwittingly/knowingly had malware installed.
Risk Scoring provides a holistic overview.
View Access and Activity on protected data. See when data was accessed, why, when and how.
ML Learns by Itself, based on patters of user behaviour.
Less Time-Consuming and More Cost-Effective than delivering and monitoring systems in-house.