Data Classification Software in 2025

TL;DR:

• Data classification software is crucial for organizing and protecting sensitive information in today's data-driven world.
• Top 10 data classification solutions in 2025 include Strac, Varonis, Symantec, McAfee, Digital Guardian, Titus, Fortra, Netwrix, Spirion, and Forcepoint.
• Investing in data classification software helps with regulatory compliance, risk management, enhanced data security, operational efficiency, and cost savings.
• Automation in data classification enhances effectiveness by reducing manual effort and minimizing human error.
• Strac offers a comprehensive solution integrating cloud data discovery, DSPM, and DLP for end-to-end data protection and compliance.

In today’s data-driven world, information is an organization's most valuable asset—and simultaneously, one of its biggest liabilities. As companies collect vast amounts of data, the need to understand, manage, and protect that data becomes critical. Data classification software plays a pivotal role in this process, ensuring that sensitive information is identified, organized, and secured appropriately. In this guide, we’ll delve into what data classification solutions are, key features you should look for in a solution, review the top 10 tools in the market, and answer some common questions about their importance.

Introduction to Data Classification Solutions

Data classification is the process of organizing data into categories based on its level of sensitivity, compliance requirements, and business value. The primary goal of data classification is to ensure that sensitive information is handled according to organizational policies and regulatory mandates. Modern data classification solutions offer a range of automated capabilities, including:

• Discovery: Identifying data across on-premises and cloud environments.
• Scanning: Assessing data to determine its classification level.
• Classification: Tagging data based on sensitivity, regulatory obligations, and business impact.
• Remediation: Applying actions such as redaction, encryption, blocking, alerting, deletion, and more.

For modern enterprises, automated data classification is key to protecting sensitive data and ensuring compliance in an increasingly complex digital landscape.

Key Features for Data Classification Solutions

Data classification solutions are essential for securing sensitive information and ensuring compliance with regulations like GDPR, HIPAA, and PCI-DSS. The key features of a robust data classification solution include:

1. Data Discovery

• Automatically scans structured and unstructured data across on-premises, cloud, and endpoint environments.
• Detects sensitive data in databases (SQL, NoSQL, AWS RDS, Redshift, etc.), SaaS applications (Google Drive, SharePoint, Slack, etc.), and endpoints (Windows, macOS, Linux).
• Supports both real-time and historical scanning.

2. Data Classification and Labeling

• Uses predefined and custom classification rules to categorize data (e.g., PII, PHI, PCI, financial data).
• Supports metadata tagging and content-based classification.
• Contextual classification with ML/NLP-based models to reduce false positives.
• Allows integration with Microsoft Sensitivity Labels, Google Drive Labels, and other native labeling frameworks.

‎3. Granular Classification Policies

• Allows organizations to define classification based on:
  • Regulatory standards (GDPR, CCPA, HIPAA, PCI-DSS).
  • Industry-specific data models (e.g., healthcare, financial services).
  • Custom enterprise policies for internal compliance.

4. Pattern-Based and Contextual Classification

• Supports Regex-based detection for structured formats (e.g., SSNs, credit card numbers).
• Uses contextual keywords (e.g., "Invoice Number" near digits) for more precise classification.
• Incorporates AI/ML-based contextual analysis to reduce noise.

5. Automated Remediation and Protection

• Applies DLP (Data Loss Prevention) policies based on classification.
• Supports redaction, masking, encryption, tokenization, or access restriction.
• Can automatically restrict file-sharing permissions (e.g., converting public links to private in Google Drive, SharePoint).

‎6. Real-Time Alerts and Incident Response

• Sends real-time notifications when sensitive data is detected in unauthorized locations.
• Integrates with SIEM, SOAR, and Security Platforms (Splunk, Sumo Logic, Microsoft Sentinel).
• Provides incident response workflows for quick remediation.

‎‎7. Compliance Reporting and Audit Trails

• Generates audit logs and compliance reports to track data classification actions.
• Helps organizations demonstrate compliance during audits.
• Supports forensic analysis for data breach investigations.

8. Integration with Security Ecosystem

• Connects with CASB, DLP, DSPM, and IAM solutions for comprehensive security.
• Supports API-driven classification for cloud-native environments.
• Works with Zero Trust and Identity Access Management (IAM) frameworks.

9. Scalability and Performance

• Supports scalable classification across large datasets in cloud and on-prem.
• Offers agentless classification for SaaS and cloud platforms.
• Optimized for low-latency and real-time scanning in high-performance environments.

10. User Awareness and Training

• Provides classification prompts and user education to encourage compliance.
• Can enforce manual classification overrides in case of ambiguous data.

Top 10 Data Classification Solutions in 2025

The market for data classification software has evolved rapidly, with a mix of established players and innovative newcomers offering robust solutions. Here’s our curated list of the top 10 data classification tools to consider:

Strac

• Integrated Solution: Combines Cloud Data Discovery, DSPM, and DLP.
• Automated Remediation: Features actions such as redaction, encryption, blocking, alerting, and deletion.
• Scalability: Ideal for organizations looking for an end-to-end SaaS platform.
• User-Friendly Interface: Designed for seamless integration with existing IT infrastructure.

‎Get a demo of Strac now.

Varonis Data Security Platform

• Comprehensive Data Insight: Provides detailed monitoring of data access and usage.
• Advanced Analytics: Integrates a robust classification engine with in-depth analytics.
• Enterprise-Grade Security: Suitable for large organizations with complex data environments.

Symantec Data Loss Prevention (Broadcom)

• Deep Content Inspection: Offers advanced scanning and content analysis.
• Contextual Analysis: Ensures sensitive data is identified with high accuracy.
• Cross-Platform Security: Protects data across endpoints, networks, and cloud environments.

McAfee Total Protection for Data

• Comprehensive Coverage: Integrates data classification with encryption and threat detection.
• Endpoint Integration: Seamlessly connects with existing endpoint security solutions.
• Automated Protection: Facilitates real-time data scanning and remediation.

Digital Guardian

• Endpoint and Network Security: Provides protection across multiple environments.
• Customizable Classification: Allows organizations to define specific data handling policies.
• Scalable Solutions: Suitable for enterprises of all sizes.

Titus Classification Suite

• Flexible Labeling: Excels in data labeling and policy enforcement.
• Industry-Specific Solutions: Widely adopted in finance, healthcare, and other regulated industries.
• User-Friendly Design: Ensures ease of use for non-technical staff.

Fortra

• Intuitive Tagging: Provides an easy-to-use interface for data tagging.
• Automated Policy Enforcement: Streamlines the application of security policies.
• Enterprise Compatibility: Integrates with existing workflows in large organizations.

Netwrix Auditor

• Robust Auditing: Combines data classification with comprehensive auditing capabilities.
• Detailed Reporting: Offers in-depth reports on data access and compliance.
• Security and Compliance: Ideal for organizations with strict regulatory requirements.

Spirion Data Platform

• Precision Discovery: Excels at identifying and classifying sensitive data.
• User-Friendly Interface: Simplifies data management for mid-sized businesses.
• Automated Workflows: Reduces manual intervention with efficient automated processes.

Forcepoint

• Dynamic Approach: Utilizes behavioral analytics to improve data classification accuracy.
• Adaptive Security: Continuously evolves to meet emerging threats.
• Comprehensive Coverage: Protects data across multiple environments including cloud and on-premises.

Each of these solutions provides unique capabilities designed to meet the diverse needs of organizations. The right choice for your organization will depend on specific regulatory requirements, risk profiles, and existing IT infrastructure.

Frequently Asked Questions (FAQ)

FAQ 1: Do I really need a data classification tool, or is this just another vendor-driven hype?

DIY Approach: If your organization has a small dataset, tight access controls, and minimal compliance needs, you might manage with manual tagging and DLP rules. However, this is a huge operational burden, and mistakes can lead to data leaks.

Strac’s Take: Manual classification is a security accident waiting to happen. Strac automates data discovery, classification, and DLP across SaaS, Cloud, and Endpoints—ensuring sensitive data is protected without human error or operational overhead.

FAQ 2: Most vendors claim to use AI/ML for classification—how do I know if it’s actually good?

Red Flag: If a vendor says "we use AI" but can’t explain how they handle false positives, contextual sensitivity, or edge cases like financial data mixed with medical records, you’re being sold a black box that might misfire.

Strac’s Take: Strac’s classification engine combines ML + contextual keyword matching + pattern recognition to reduce false positives and adapt to real-world data complexities (e.g., PHI, PCI, or unstructured data spread across emails, tickets, and storage).

FAQ 3: Can't my existing DLP tool handle data classification? Why add another vendor?

Reality Check: Traditional DLP tools were not built for modern cloud/SaaS-first environments. Many require manual policies, lack context-aware classification, and struggle with unstructured data.

Strac’s Take: Strac automates classification at scale, integrates with SaaS, Cloud, and Endpoints, and allows for granular remediation (redact, alert, block, mask, delete)—something legacy DLP solutions just can't do well.

FAQ 4: How do I avoid vendor lock-in when choosing a data classification solution?

DIY Approach: Use open standards like DLP-compatible tags, keep classification metadata portable, and ensure the tool integrates via APIs with your existing security stack.

Strac’s Take: Strac provides open APIs, SIEM/SOAR integrations, and flexible policy enforcement, so your data remains yours, portable, and usable across your ecosystem—without locking you into a proprietary mess.

FAQ 5: What’s the biggest mistake companies make when implementing data classification?

Common Pitfall: Companies assume data classification is a one-time project, rather than an ongoing process that adapts as new sensitive data is created and shared.

Strac’s Take: Strac ensures continuous, real-time classification and remediation, scanning both historical and newly created data to keep pace with evolving security and compliance needs.

Conclusion

In the evolving landscape of data security, data classification software stands as a critical pillar for safeguarding sensitive information and ensuring compliance. The ability to automatically discover, scan, classify, and remediate data through actions like redaction, encryption, and deletion empowers organizations to manage risk more effectively and optimize operational efficiency.

With solutions like Strac leading the way by integrating cloud data discovery, DSPM, and DLP into a seamless SaaS offering, organizations can benefit from end-to-end data protection that not only meets regulatory requirements but also adapts to the dynamic nature of modern cyber threats.

As you consider investing in a data classification solution, weigh the specific needs of your organization—such as compliance requirements, risk profile, and integration with existing infrastructure—against the capabilities of these top-tier solutions. Whether you are a mid-sized business or a large enterprise, adopting a robust data classification strategy is not just a security imperative; it’s a strategic investment in the future of your organization.