Varonis

Automatically scans and identifies sensitive data (PII, PHI, financial records, credentials) across on-premises file servers, cloud storage, and databases without requiring manual tagging or pre-classification. Uses AI/ML to recognize patterns and classify data types across heterogeneous environments.

Establishes behavioral baselines for user access patterns using machine learning and flags deviations in real-time that indicate insider threats, compromised accounts, or breach activity. Monitors access to sensitive data and alerts on suspicious behavior.

Monitors privileged user accounts and service accounts for suspicious activity, unauthorized privilege escalation, and credential abuse. Tracks all actions taken by high-risk accounts.

Analyzes data sharing patterns and identifies risky sharing scenarios such as sensitive data shared externally, shared with overly broad groups, or shared with inactive users. Flags collaboration risks.

Provides forensic data and investigation tools to support incident response teams during breach investigations. Enables rapid scope assessment, impact analysis, and evidence collection.

Generates compliance-ready audit reports for regulatory frameworks (GDPR, HIPAA, SOX, PCI-DSS) automatically by mapping discovered data to regulatory requirements and documenting access controls. Eliminates manual audit preparation work.

Provides detailed visibility into who accessed what data, when, from where, and what actions they performed through the DatAdvantage module. Enables forensic investigation of data breaches and insider incidents with complete audit trails.

Creates a unified inventory and map of all data assets across heterogeneous environments including on-premises file servers, cloud storage (AWS, Azure, Google Cloud), and databases. Provides single pane of glass visibility into data landscape.

Analyzes and visualizes user permissions, group memberships, and access control lists across systems to identify over-privileged users, orphaned accounts, and excessive permissions. Highlights access control risks and compliance violations.

Applies machine learning to establish behavioral profiles for users and service accounts, then detects when behavior deviates from established patterns. Identifies compromised accounts, insider threats, and lateral movement attempts.

Identifies data that should be deleted or archived based on retention policies and age, then enforces deletion or archival. Helps organizations comply with data minimization requirements and reduce storage costs.

Detects ransomware attacks and data exfiltration attempts by identifying mass file encryption, unusual file modifications, and bulk data access/transfer patterns that deviate from normal behavior.

Automatically classifies data based on content analysis, metadata, and patterns without requiring manual tagging. Applies consistent classification labels across heterogeneous data sources.