DSPM vs DLP

Hello all, 

Happy Friday! Welcome back to Strac’s weekly update. Today’s article explores the similarities and differences between DSPM and DLP. I hope you find it engaging and learn something new.

Additionally, if you need help discovering & classifying sensitive data on SaaS, Cloud, and Generative AI, and the ability to remediate (redact, mask, block, alert) PII, PHI, PCI, Sensitive Data or comply with PCI, HIPAA, SOC 2, GDPR, CCPA, please feel free to book a call with me.

Warmly,

Aatish

Strac’s Latest Views on DSPM vs DLP

 🚀 DSPM vs DLP. Understanding the Differences!

DSPM (Data Security Posture Management) focuses on managing data security holistically by identifying, monitoring, and protecting sensitive data across an organization’s ecosystem. Key components include sensitive data discovery, classification and access control management.

DLP (Data Loss Prevention) aims to prevent unauthorized disclosure of sensitive data through tools and processes that detect and prevent data breaches. Key components include sensitive data discovery, classification and remediation measures like redaction, masking, and blocking.

Both DSPM and DLP involve data discovery and classification, but DLP includes active remediation, while DSPM emphasizes managing access to sensitive data.

Strac: A platform that integrates DSPM and DLP, providing advanced data discovery, accurate classification, effective remediation, and comprehensive access management.

Key features:

• Advanced data discovery using machine learning and OCR.

• Accurate classification of sensitive data.

• Effective remediation options (redaction, masking, blocking, etc.).

• Deep integrations across SaaS, cloud, and GenAI platforms.

• Detailed access management insights.

• Ensures regulatory compliance (e.g., GDPR, HIPAA).

Strac stands out as a comprehensive data security solution by combining the strengths of both DSPM and DLP, offering unparalleled visibility, control, and security for sensitive data.

To learn more about how these two terms differ click here.