DTEX Insider Risk Management vs Security Vision UEBA

DTEX Insider Risk Management helps enterprise security teams examine user behavior and activity around data and AI to identify potential insider-driven breaches. It offers preconfigured and customizable behavior indicators, user baselines, anomaly detection, and risk scoring. Investigations can draw on MITRE ATT&CK-aligned profiling and endpoint telemetry such as event logs, registry changes, and credential usage to examine signs including lateral movement and privilege escalation. Preconfigured DLP patterns flag risky behavior, while data lineage tracks file interactions and changes. A threat-hunting engine supports searches across insider data using an open query language and customizable visualizations. The platform covers users, endpoints, servers, applications, data, and AI activity across Linux, macOS, Windows, and web. DTEX describes pseudonymization that masks personal identifiers and can be reversed for escalated investigations. It reports metadata collection of about 5 MB per user per day, with deployment materials giving a 3–5 MB range. Pricing is available on request.

Security Vision UEBA builds behavioral models for users, accounts, devices, hosts and processes, then detects deviations in activity and traffic. Its analysis combines correlation rules, statistical methods and machine learning to identify suspicious behavior and incidents. It can analyze raw data from data lakes, SIEM, next-generation firewalls, proxy servers, and Windows or Linux devices. Configured connectors support SIEM systems, Cisco, CheckPoint, Palo Alto and Juniper network devices, Squid and Blue Coat proxies, Kafka, Elasticsearch, and Windows and Linux logs. Users can add or adapt Sigma and correlation rules with a correlation rule editor. UEBA can enrich incident data and send events or incidents to SOAR or SIEM. Response actions can include adding entries to NGFW blocklists or SIEM active lists. Its API provides alarms, suspicious events and object alerts. Deployment is on premises; the maker says its machine-learning models run on customer infrastructure without sending data to the cloud. It is paid with individual pricing, and license type and support level affect cost.