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Use Case

Modern Cloud Data Loss Prevention That Detects and Responds to Insider Risk

Insider threats demand more than surface-level visibility.

In modern cloud and SaaS environments, insider-driven data loss requires real-time detection and automated control:

Alert noise (i.e., false positives) makes it hard to identify true threats

Unrestricted sharing in tools like Google Drive and Microsoft 365 exposes sensitive data

Manual access control for sensitive data delays least privilege enforcement

Lack of automatic response to security posture changes allows exposure to persist

Sentra gives enterprises the visibility they need to stop insider threats before data is exposed

Identify and mitigate threats to sensitive data in near real-time

Block risky sharing or encrypt sensitive content in SaaS tools and data shares

Automatically restrict access to sensitive data and enforce least-privilege access

Govern access to cloud and SaaS data with AI-powered precision

Sentra finds critical data hidden within unstructured data before hackers do

Gain visibility into everywhere sensitive data exists, within unstructured assets and files — and insight into its business purpose.

Cloud-native DLP, built for scale

Legacy endpoint DLP tools struggle in dynamic cloud environments. Sentra delivers real-time visibility and control purpose-built for multi-cloud and SaaS—eliminating gaps left by on-prem solutions.

Smarter detection through deep context

Sentra's DDR engine uses identity, location, behavior, and sensitivity to detect and prioritize insider threats. It monitors log and configuration changes, prioritizes risk by severity, and catches threats traditional posture tools miss—like data exfiltration or abnormal privilege use.

Automated protection for unstructured data

Sentra enforces least-privilege policies and automatically restricts access when security posture changes. It prevents leaks by remediating risky actions—like unauthorized sharing or movement across environments—in Google Drive and Microsoft 365.

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