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Strac Sensitive Data Discovery

Discovers, classifies, and redacts sensitive data across SaaS, cloud, and endpoints

Data Protection
Commercial
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Strac Sensitive Data Discovery Description

Strac Sensitive Data Discovery is a Data Security Posture Management (DSPM) solution that identifies, categorizes, and protects sensitive data across SaaS applications, cloud databases, and endpoint devices. The platform uses machine learning algorithms and OCR models to scan text, attachments, and audio/video recordings to detect PII, PHI, and PCI data. The solution provides agentless data discovery and classification capabilities across Windows, macOS, and Linux endpoints, as well as cloud services like AWS and SaaS applications like Office 365. It scans both unstructured documents (PDF, JPEG, PNG, Word, Excel, ZIP files) and structured data in database tables. Strac offers real-time scanning and classification to detect sensitive data exposure as it occurs. The platform includes automated remediation actions such as redaction, masking, labeling, blocking, and secure deletion to enforce security policies without manual intervention. The solution provides a customizable classification framework that allows organizations to define sensitive data based on their regulatory and business requirements. It supports compliance with GDPR, HIPAA, PCI-DSS, SOC 2, CPRA, ISO 27001, and ISO 27701. Strac includes reporting and analytics capabilities for monitoring compliance with data protection regulations and conducting audits. The platform tracks access permissions and monitors who can view and share sensitive files across the organization's digital assets.

Strac Sensitive Data Discovery FAQ

Common questions about Strac Sensitive Data Discovery including features, pricing, alternatives, and user reviews.

Strac Sensitive Data Discovery is Discovers, classifies, and redacts sensitive data across SaaS, cloud, and endpoints developed by Strac. It is a Data Protection solution designed to help security teams with Cloud Security, Compliance, DLP.

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