Anzenna vs DTEX Insider Risk Management
Anzenna
anzenna.aiDTEX Insider Risk Management
dtex.aiSide by side
| Figure | Anzenna | DTEX Insider Risk Management |
|---|---|---|
| Price from | — | — |
| Free plan | — | — |
| Free trial | — | — |
| Open source | — | — |
| Web | Yes | Yes |
| Windows | — | Yes |
| macOS | — | Yes |
| Linux | — | Yes |
| API | Yes | — |
| Plans published | 1 | 1 |
| Spec score · Insider Risk Management Software | 8.3 (#1) | 7.3 (#12) |
| Deployment | Anzenna says it connects through integrations without endpoint agents and lists a 15-minute install. | DTEX says lightweight forwarders deploy in minutes and collect 3–5 MB of data per user per day; the platform page also describes activity collection across endpoints and servers. |
| Integrations | Anzenna lists 130+ integrations, including Okta, Google Workspace, Microsoft 365, GitHub, Slack, and Snowflake. | The integration page describes connections to EDR, cloud security, data classification, SIEM/SOAR and case management, productivity apps including Google Workspace and Microsoft 365, HR systems, and data platforms. |
| Support | The contact page advertises a 24/7 emergency support line for existing customers. | DTEX offers i³ investigative services to help organizations identify, analyze, and respond to security incidents and insider threats. |
Plans
DTEX Insider Risk Management
- DTEX Insider Risk ManagementPriced on request
In their own words
DTEX Insider Risk Management
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.