User Behaviour Analytics

As an add-on module, our User Behaviour Analytics (UBA) forms a supplementary layer to your security, to provide complete visibility, detect account compromises, and mitigate and detect malicious or anomalous insider activity.

ML Learns by itself, based on patterns of user behaviour.

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.

Insider threats account for over 30% of account compromises

Overview

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.

The Challenge

Many threats and risks cannot be detected by SIEM, IPS, and other security tools.

The Solution

SecurityHQ’s SOC teams deliver real-time advanced anomaly detection, to identify malicious and anomalous activity arising from zero-day exploits, as well as malware and insider activity.