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Retail Data Breaches: How to Secure Customer Data With DSPM

May 28, 2024
3
Min Read
Data Security

In 2023, the average cost of a retail data breach reached $2.96 million, with the retail sector representing 6% of global data breaches, a rise from 5% in the prior year. 

Consequently, retail now ranks as the 8th most frequently targeted industry in cyber attacks, climbing from 10th place in 2022. According to the Sophos State of Ransomware in Retail report, ransomware affected 69% of retail enterprises in 2023. Nearly 75% of these ransomware incidents led to data encryption, marking an increase from 68% and 54% in the preceding two years. Yet, these breaches aren't merely a concern for retailers alone; they pose a severe threat to customer confidence at large. 


The need for retailers to focus on data security is crucial since the retail sector serves such a large community (and therefore is a huge target for fraud, account compromise, etc.).  Retailers, increasingly conducting business online, are subject to evolving privacy and credit card regulations, to protect consumers. One compromise or breach event can prove disastrous to the customer trust that retailers may have built over years.  

With the evolving cyber threats, the proliferation of cloud computing, and the persistent risk of human error, retailers confront a multifaceted security landscape. Retailers should take proactive measures, and gain a deeper understanding of the potential risks in order to properly harden their defenses.


The year 2024 had just begun when VF Corporation, a global apparel and footwear giant, experienced a significant breach. This incident served as a stark reminder of the far-reaching consequences of ransomware attacks in the retail industry. Approximately 35 million individuals, including employees, customers, and vendors, were affected. Personal information such as names, addresses, and Social Security numbers fell into the hands of malicious actors, emphasizing the urgent need for retailers to secure sensitive data.

How to Secure Customer Data

Automatically Discover, Classify and Secure All Customer Data

Automatically discovering, classifying, and securing all customer data is essential for businesses today. Sentra offers a comprehensive retail data security solution, uncovering sensitive customer data such as personally identifiable information (PII), cardholder data, payment account information, and order details across both known and unknown cloud data stores. 

With Sentra's Data Security Posture Management (DSPM) solution, no sensitive data is left undiscovered; the platform provides extensive coverage of data assets, custom data classes, and detailed cataloging of tables and objects. This not only ensures compliance but also supports data-driven decision-making through safe collaboration and data sharing. As a cloud-native solution, Sentra offers full coverage across major platforms like AWS, Azure, Snowflake, GCP, and Office 365, as well as on-premise file shares and databases. Your cloud data remains within your environment, ensuring you retain control of your sensitive data at all times.

Comply with Data Security and Privacy Regulations

Ensuring compliance with data security and privacy regulations is paramount in today's business landscape. With Sentra’s DSPM solution, you can streamline the process of preparing for security audits concerning customer and credit card/account data. Sentra’s platform efficiently identifies compliance discrepancies, enabling swift and proactive remediation measures.

You can also simplify the translation of requirements from various regulatory frameworks such as PCI-DSS, GDPR, CCPA, DPDPA, among others, using straightforward rules and policies. For instance, you'll receive notifications if regulated data is transferred between regions or to an insecure environment. 

Sentra Dashboard Issues showing top compliance frameworks

Furthermore, our system detects specific policy violations, such as uncovering PCI-DSS violations that indicate classified information, including credit cards and bank account numbers, being publicly accessible or located outside of a PCI compliant environment. Finally, we generate comprehensive compliance reports containing all necessary evidence, including sensitive data categories, regulatory measures, security posture, and the status of relevant regulatory standards.

Mitigate Supply Chain Risks and Emerging Threats

Addressing supply chain risks and emerging threats is critical for safeguarding your organization. Sentra leverages real-time threat monitoring, Data Detection and Response (DDR) to prevent fraud, data exfiltration, or breaches, thereby reducing downtime and ensuring the security of sensitive customer data.

Sentra dashboard example of sensitive data accessed from suspicious IP address

Sentra’s DSPM solution offers automated detection capabilities to alert you when third parties gain access to sensitive account and customer data, empowering you to take immediate action. By implementing least privilege access based on necessity, we help minimize supply chain risks, ensuring that only authorized individuals can access sensitive information. 

Additionally, Sentra’s DSPM enables you to enforce security posture and retention policies, thereby mitigating the risks associated with abandoned data. You'll receive instant alerts regarding suspicious data movements or accesses, such as those from unknown IP addresses, enabling you to promptly investigate and respond. In the event of a breach, our solution facilitates swift evaluation of its impact and enables you to initiate remedial actions promptly, thereby limiting potential damage to your organization.

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David Stuart is Senior Director of Product Marketing for Sentra, a leading cloud-native data security platform provider, where he is responsible for product and launch planning, content creation, and analyst relations. Dave is a 20+ year security industry veteran having held product and marketing management positions at industry luminary companies such as Symantec, Sourcefire, Cisco, Tenable, and ZeroFox. Dave holds a BSEE/CS from University of Illinois, and an MBA from Northwestern Kellogg Graduate School of Management.

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Elie Perelman
Elie Perelman
February 12, 2026
3
Min Read

Best Data Access Governance Tools

Best Data Access Governance Tools

Managing access to sensitive information is becoming one of the most critical challenges for organizations in 2026. As data sprawls across cloud platforms, SaaS applications, and on-premises systems, enterprises face compliance violations, security breaches, and operational inefficiencies. Data Access Governance Tools provide automated discovery, classification, and access control capabilities that ensure only authorized users interact with sensitive data. This article examines the leading platforms, essential features, and implementation strategies for effective data access governance.

Best Data Access Governance Tools

The market offers several categories of solutions, each addressing different aspects of data access governance. Enterprise platforms like Collibra, Informatica Cloud Data Governance, and Atlan deliver comprehensive metadata management, automated workflows, and detailed data lineage tracking across complex data estates.

Specialized Data Access Governance (DAG) platforms focus on permissions and entitlements. Varonis, Immuta, and Securiti provide continuous permission mapping, risk analytics, and automated access reviews. Varonis identifies toxic combinations by discovering and classifying sensitive data, then correlating classifications with access controls to flag scenarios where high-sensitivity files have overly broad permissions.

User Reviews and Feedback

Varonis

  • Detailed file access analysis and real-time protection capabilities
  • Excellent at identifying toxic permission combinations
  • Learning curve during initial implementation

BigID

  • AI-powered classification with over 95% accuracy
  • Handles both structured and unstructured data effectively
  • Strong privacy automation features
  • Technical support response times could be improved

OneTrust

  • User-friendly interface and comprehensive privacy management
  • Deep integration into compliance frameworks
  • Robust feature set requires organizational support to fully leverage

Sentra

  • Effective data discovery and automation capabilities (January 2026 reviews)
  • Significantly enhances security posture and streamlines audit processes
  • Reduces cloud storage costs by approximately 20%

Critical Capabilities for Modern Data Access Governance

Effective platforms must deliver several core capabilities to address today's challenges:

Unified Visibility

Tools need comprehensive visibility across IaaS, PaaS, SaaS, and on-premises environments without moving data from its original location. This "in-environment" architecture ensures data never leaves organizational control while enabling complete governance.

Dynamic Data Movement Tracking

Advanced platforms monitor when sensitive assets flow between regions, migrate from production to development, or enter AI pipelines. This goes beyond static location mapping to provide real-time visibility into data transformations and transfers.

Automated Classification

Modern tools leverage AI and machine learning to identify sensitive data with high accuracy, then apply appropriate tags that drive downstream policy enforcement. Deep integration with native cloud security tools, particularly Microsoft Purview, enables seamless policy enforcement.

Toxic Combination Detection

Platforms must correlate data sensitivity with access permissions to identify scenarios where highly sensitive information has broad or misconfigured controls. Once detected, systems should provide remediation guidance or trigger automated actions.

Infrastructure and Integration Considerations

Deployment architecture significantly impacts governance effectiveness. Agentless solutions connecting via cloud provider APIs offer zero impact on production latency and simplified deployment. Some platforms use hybrid approaches combining agentless scanning with lightweight collectors when additional visibility is required.

Integration Area Key Considerations Example Capabilities
Microsoft Ecosystem Native integration with Microsoft Purview, Microsoft 365, and Azure Varonis monitors Copilot AI prompts and enforces consistent policies
Data Platforms Direct remediation within platforms such as Snowflake BigID automatically enforces dynamic data masking and tagging
Cloud Providers API-based scanning without performance overhead Sentra’s agentless architecture scans environments without deploying agents

Open Source Data Governance Tools

Organizations seeking cost-effective or customizable solutions can leverage open source tools. Apache Atlas, originally designed for Hadoop environments, provides mature governance capabilities that, when integrated with Apache Ranger, support tag-based policy management for flexible access control.

DataHub, developed at LinkedIn, features AI-powered metadata ingestion and role-based access control. OpenMetadata offers a unified metadata platform consolidating information across data sources with data lineage tracking and customized workflows.

While open source tools provide foundational capabilities, metadata cataloging, data lineage tracking, and basic access controls, achieving enterprise-grade governance typically requires additional customization, integration work, and infrastructure investment. The software is free, but self-hosting means accounting for operational costs and expertise needed to maintain these platforms.

Understanding the Gartner Magic Quadrant for Data Governance Tools

Gartner's Magic Quadrant assesses vendors on ability to execute and completeness of vision. For data access governance, Gartner examines how effectively platforms define, automate, and enforce policies controlling user access to data.

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Yair Cohen
Yair Cohen
February 11, 2026
4
Min Read

DSPM vs DLP vs DDR: How to Architect a Data‑First Stack That Actually Stops Exfiltration

DSPM vs DLP vs DDR: How to Architect a Data‑First Stack That Actually Stops Exfiltration

Many security stacks look impressive at first glance. There is a DLP agent on every endpoint, a CASB or SSE proxy watching SaaS traffic, EDR and SIEM for hosts and logs, and perhaps a handful of identity and access governance tools. Yet when a serious incident is investigated, it often turns out that sensitive data moved through a path nobody was really watching, or that multiple tools saw fragments of the story but never connected them.

The common thread is that most stacks were built around infrastructure, not data. They understand networks, workloads, and log lines, but they don’t share a single, consistent understanding of:

  • What your sensitive data is
  • Where it actually lives
  • Who and what can access it
  • How it moves across cloud, SaaS, and AI systems

To move beyond that, security leaders are converging on a data‑first architecture that brings together four capabilities: DSPM (Data Security Posture Management), DLP (Data Loss Prevention), DAG (Data Access Governance), and DDR (Data Detection & Response) in a unified model.

Clarifying the Roles

At the heart of this architecture is DSPM. DSPM is your data‑at‑rest intelligence layer. It continuously discovers data across clouds, SaaS, on‑prem, and AI pipelines, classifies it, and maps its posture; configurations, locations, access paths, and regulatory obligations. Instead of a static inventory, you get a living view of where sensitive data resides and how risky it is.

DLP sits at the edges of the system. Its job is to enforce policy on data in motion and in use: emails leaving the organization, files uploaded to the web, documents synced to endpoints, content copied into SaaS apps, or responses generated by AI tools. DLP decides whether to block, encrypt, quarantine, or simply log based on policies and the context it receives.

DAG bridges the gap between “what” and “who.” It’s responsible for least‑privilege access; understanding which human and machine identities can access which datasets, whether they really need that access, and what toxic combinations exist when sensitive data is exposed to broad groups or powerful service accounts.

DDR closes the loop. It monitors access to and movement of sensitive data in real time, looking for unusual or risky behavior: anomalous downloads, mass exports, unusual cross‑region copies, suspicious AI usage. When something looks wrong, DDR triggers detections, enriches them with data context, and kicks off remediation workflows.

When these four functions work together, you get a stack that doesn’t just warn you about potential issues; it actively reduces your exposure and stops exfiltration in motion.

Why “DSPM vs DLP” Is the Wrong Framing

It’s tempting to think of DSPM and DLP as competing answers to the same problem. In reality, they address different parts of the lifecycle. DSPM shows you what’s at risk and where; DLP controls how that risk can materialize as data moves.

Trying to use DLP as a discovery and classification engine is what leads to the noise and blind spots described in the previous section. Conversely, running DSPM without any enforcement at the edges leaves you with excellent visibility but too little control over where data can go.

DSPM and DAG reduce your attack surface; DLP and DDR reduce your blast radius. DSPM and DAG shrink the pool of exposed data and over‑privileged identities. DLP and DDR watch the edges and intervene when data starts to move in risky ways.

A Unified, Data‑First Reference Architecture

In a data‑first architecture, DSPM sits at the center, connected API‑first into cloud accounts, SaaS platforms, data warehouses, on‑prem file systems, and AI infrastructure. It continuously updates an inventory of data assets, understands which are sensitive or regulated, and applies labels and context that other tools can use.

On top of that, DAG analyzes which users, groups, service principals, and AI agents can access each dataset. Over‑privileged access is identified and remediated, sometimes automatically: by tightening IAM roles, restricting sharing, or revoking legacy permissions. The result is a significant reduction in the number of places where a single identity can cause significant damage.

DLP then reads the labels and access context from DSPM and DAG instead of inferring everything from scratch. Email and endpoint DLP, cloud DLP via SSE/CASB, and even platform‑native solutions like Purview DLP all begin enforcing on the same sensitivity definitions and labels. Policies become more straightforward: “Block Highly Confidential outside the tenant,” “Encrypt PHI sent to external partners,” “Require justification for Customer‑Identifiable data leaving a certain region.”

DDR runs alongside this, monitoring how labeled data actually moves. It can see when a typically quiet user suddenly downloads thousands of PHI records, when a service account starts copying IP into a new data store, or when an AI tool begins interacting with a dataset marked off‑limits. Because DDR is fed by DSPM’s inventory and DAG’s access graph, detections are both higher fidelity and easier to interpret.

From there, integration points into SIEM, SOAR, IAM/CIEM, ITSM, and AI gateways allow you to orchestrate end‑to‑end responses: open tickets, notify owners, roll back risky changes, block certain actions, or update policies.

Where Sentra Fits

Sentra’s product vision aligns directly with this data‑first model. Rather than treating DSPM, DAG, DDR, and DLP intelligence as separate products, Sentra brings them together into a single, cloud‑native data security platform.

That means you get:

  • DSPM that discovers and classifies data across cloud, SaaS, on‑prem, and AI
  • DAG that maps and rationalizes access to that data
  • DDR that monitors sensitive data in motion and detects threats
  • Integrations that feed this intelligence into DLP, SSE/CASB, Purview, EDR, and other controls

In other words, Sentra is positioned as the brain of the data‑first stack, giving DLP and the rest of your security stack the insight they need to actually stop exfiltration, not just report on it afterward.

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Ward Balcerzak
Ward Balcerzak
February 11, 2026
3
Min Read

Best Data Classification Tools in 2026: Compare Leading Platforms for Cloud, SaaS, and AI

Best Data Classification Tools in 2026: Compare Leading Platforms for Cloud, SaaS, and AI

As organizations navigate the complexities of cloud environments and AI adoption, the need for robust data classification has never been more critical. With sensitive data sprawling across IaaS, PaaS, SaaS platforms, and on-premise systems, enterprises require tools that can discover, classify, and govern data at scale while maintaining compliance with evolving regulations. The best data classification tools not only identify where sensitive information resides but also provide context around data movement, access controls, and potential exposure risks. This guide examines the leading solutions available today, helping you understand which platforms deliver the accuracy, automation, and integration capabilities necessary to secure your data estate.

Key Consideration What to Look For
Classification Accuracy AI-powered classification engines that distinguish real sensitive data from mock or test data to minimize false positives
Platform Coverage Unified visibility across cloud, SaaS, and on-premises environments without moving or copying data
Data Movement Tracking Ability to monitor how sensitive assets move between regions, environments, and AI pipelines
Integration Depth Native integrations with major platforms such as Microsoft Purview, Snowflake, and Azure to enable automated remediation

What Are Data Classification Tools?

Data classification tools are specialized platforms designed to automatically discover, categorize, and label sensitive information across an organization's entire data landscape. These solutions scan structured and unstructured data, from databases and file shares to cloud storage and SaaS applications, to identify content such as personally identifiable information (PII), financial records, intellectual property, and regulated data subject to compliance frameworks like GDPR, HIPAA, or CCPA.

Effective data classification tools leverage machine learning algorithms, pattern matching, metadata analysis, and contextual awareness to tag data accurately. Beyond simple discovery, these platforms correlate classification results with access controls, data lineage, and risk indicators, enabling security teams to identify "toxic combinations" where highly sensitive data sits behind overly permissive access settings. This contextual intelligence transforms raw classification data into actionable security insights, helping organizations prevent data breaches, meet compliance obligations, and establish the governance guardrails necessary for secure AI adoption.

Top Data Classification Tools

Sentra

Sentra is a cloud-native data security platform specifically designed for AI-ready data governance. Unlike legacy classification tools built for static environments, Sentra discovers and governs sensitive data at petabyte scale inside your own environment, ensuring data never leaves your control.

What Users Like:

  • Classification accuracy and contextual risk insights consistently praised in January 2026 reviews
  • Speed and precision of classification engine described as unmatched
  • DataTreks capability creates interactive maps tracking data movement, duplication, and transformation
  • Distinguishes between real sensitive data and mock data to prevent false positives

Key Capabilities:

  • Unified visibility across IaaS, PaaS, SaaS, and on-premise file shares without moving data
  • Deep Microsoft integration leveraging Purview Information Protection with 95%+ accuracy
  • Identifies toxic combinations by correlating data sensitivity with access controls
  • Tracks data movement to detect when sensitive assets flow into AI pipelines
  • Eliminates shadow and ROT data, typically reducing cloud storage costs by ~20%

BigID

BigID uses AI-powered discovery to automatically identify sensitive or regulated information, continuously monitoring data risks with a strong focus on privacy compliance and mapping personal data across organizations.

What Users Like:

  • Exceptional data classification capabilities highlighted in January 2026 reviews
  • Comprehensive data-discovery features for privacy, protection, and governance
  • Broad source connectivity across diverse data environments

Varonis

Varonis specializes in unstructured data classification across file servers, email, and cloud content, providing strong access monitoring and insider threat detection.

What Users Like:

  • Detailed file access analysis and real-time protection
  • Actionable insights and automated risk visualization

Considerations:

  • Learning curve when dealing with comprehensive capabilities

Microsoft Purview

Microsoft Purview delivers exceptional integration for organizations invested in the Microsoft ecosystem, automatically classifying and labeling data across SharePoint, OneDrive, and Microsoft 365 with customizable sensitivity labels and comprehensive compliance reporting.

Nightfall AI

Nightfall AI stands out for real-time detection capabilities across modern SaaS and generative AI applications, using advanced machine learning to prevent data exfiltration and secret sprawl in dynamic environments.

Other Notable Solutions

Forcepoint takes a behavior-based approach, combining context and user intent analysis to classify and protect data across cloud, network, and endpoints, though its comprehensive feature set requires substantial tuning and comes with a steeper learning curve.

Google Cloud DLP excels for teams pursuing cloud-first strategies within Google's environment, offering machine-learning content inspection that scales seamlessly but may be less comprehensive across broader SaaS portfolios.

Atlan functions as a collaborative data workspace emphasizing metadata management, automated tagging, and lineage analysis, seamlessly connecting with modern data stacks like Snowflake, BigQuery, and dbt.

Collibra Data Intelligence Cloud employs self-learning algorithms to uncover, tag, and govern both structured and unstructured data across multi-cloud environments, offering detailed reporting suited to enterprises requiring holistic data discovery with strict compliance oversight.

Informatica leverages AI to profile and classify data while providing end-to-end lineage visualization and analytics, ideal for large, distributed ecosystems demanding scalable data quality and governance.

Evaluation Criteria for Data Classification Tools

Selecting the right data classification tool requires careful assessment across several critical dimensions:

Classification Accuracy

The engine must reliably distinguish between genuine sensitive data and mock or test data to prevent false positives that create alert fatigue and waste security resources. Advanced solutions employ multiple techniques including pattern matching, proximity analysis, validation algorithms, and exact data matching to improve precision.

Platform Coverage

The best solutions scan IaaS, PaaS, SaaS, and on-premise file shares without moving data from its original location, using metadata collection and in-environment scanning to maintain data sovereignty while delivering centralized governance. This architectural approach proves especially critical for organizations subject to strict data residency requirements.

Automation and Integration

Look for tools that automatically tag and label data based on classification results, integrate with native platform controls (such as Microsoft Purview labels or Snowflake masking policies), and trigger remediation workflows without manual intervention. The depth of integration with your existing technology stack determines how seamlessly classification insights translate into enforceable security policies.

Data Movement Tracking

Modern tools must monitor how sensitive assets flow between regions, migrate across environments (production to development), and feed into AI systems. This dynamic visibility enables security teams to detect risky data transfers before they result in compliance violations or unauthorized exposure.

Scalability and Performance

Evaluate whether the solution can handle your data volume without degrading scan performance or requiring excessive infrastructure resources. Consider the platform's ability to identify toxic combinations, correlating high-sensitivity data with overly permissive access controls to surface the most critical risks requiring immediate remediation.

Best Free Data Classification Tools

For organizations seeking to implement data classification without immediate budget allocation, two notable free options merit consideration:

Imperva Classifier: Data Classification Tool is available as a free download (requiring only email submission for installation access) and supports multiple operating systems including Windows, Mac, and Linux. It features over 250 built-in search rules for enterprise databases such as Oracle, Microsoft SQL, SAP Sybase, IBM DB2, and MySQL, making it a practical choice for quickly identifying sensitive data at risk across common database platforms.

Apache Atlas represents a robust open-source alternative originally developed for the Hadoop ecosystem. This enterprise-grade solution offers comprehensive metadata management with dedicated data classification capabilities, allowing organizations to tag and categorize data assets while supporting governance, compliance, and data lineage tracking needs.

While free tools offer genuine value, they typically require more in-house expertise for customization and maintenance, may lack advanced AI-powered classification engines, and often provide limited support for modern cloud and SaaS environments. For enterprises with complex, distributed data estates or strict compliance requirements, investing in a commercial solution often proves more cost-effective when factoring in total cost of ownership.

Making the Right Choice for Your Organization

Selecting among the best data classification tools requires aligning platform capabilities with your specific organizational context, data architecture, and security objectives. User reviews from January 2026 provide valuable insights into real-world performance across leading platforms.

When evaluating solutions, prioritize running proof-of-concept deployments against representative samples of your actual data estate. This hands-on testing reveals how well each platform handles your specific data types, integration requirements, and performance expectations. Develop a scoring framework that weights evaluation criteria according to your priorities, whether that's classification accuracy, automation capabilities, platform coverage, or integration depth with existing systems.

Consider your organization's trajectory alongside current needs. If AI adoption is accelerating, ensure your chosen platform can discover AI copilots, map their knowledge base access, and enforce granular behavioral guardrails on sensitive data. For organizations with complex multi-cloud environments, unified visibility without data movement becomes non-negotiable. Enterprises subject to strict compliance regimes should prioritize platforms with proven regulatory alignment and automated policy enforcement.

The data classification landscape in 2026 offers diverse solutions, from free and open-source options suitable for organizations with strong technical teams to comprehensive commercial platforms designed for petabyte-scale, AI-driven environments. By carefully evaluating your requirements against the strengths of leading platforms, you can select a solution that not only secures your current data estate but also enables confident adoption of AI technologies that drive competitive advantage.

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