What Is Shadow Data? Examples, Risks and How to Detect It

For organizations invested in Microsoft 365, Purview and Copilot now sit at the center of both data protection and productivity. Purview offers rich DLP capabilities, along with sensitivity labels that drive encryption, retention, and policy. Copilot promises to unlock new value from content in SharePoint, OneDrive, Teams, and other services.

But there is a catch. Both Purview DLP and Copilot depend heavily on labels and correct classification. If labels are missing, wrong, or inconsistent, then:

• DLP rules fire in the wrong places (creating false positives) or miss critical data (worse!).
• Copilot accesses content you never intended it to see and can inadvertently surface it in responses.

In many environments, that’s exactly what’s happening. Labels are applied manually. Legacy content, exports from non‑Microsoft systems, and AI‑ready datasets live side by side with little or no consistent tagging. Purview has powerful controls, it just doesn’t always have the accurate inputs it needs.

The fastest way to boost performance of Purview DLP and make Copilot safe is to fix labels at the source using a DSPM platform, then let Microsoft’s native controls do the work they’re already good at.

The limits of M365‑only classification

Purview’s built-in classifiers understand certain patterns and can infer sensitivity from content inside the Microsoft 365 estate. That can be useful, but it doesn’t solve two big problems.

First, PHI, PCI, PII, and IP often originate in systems outside of M365; core banking platforms, claims systems, Snowflake, Databricks, and third‑party SaaS applications. When that data is exported or synced into SharePoint, OneDrive, or Teams, it often arrives without accurate labels.

Second, even within M365, there are years of accumulated documents, emails, and chat history that have never been systematically classified. Applying labels retroactively is time‑consuming and error‑prone if you rely on manual tagging or narrow content rules. And once there, without contextual analysis and deeper understanding of the unstructured files in which the data lives, it becomes extremely difficult to apply precise sensitivity labels.When you add Copilot (or any AI agent/assistant) into the mix, any mislabeling or blind spots in classification can quickly turn into AI‑driven data exposure. The stakes are higher, and so is the need for a more robust foundation.

Using DSPM to fix labels at the source

A DSPM platform like Sentra plugs into your environment at a different layer. It connects not just to Microsoft 365, but also to cloud providers, data warehouses, SaaS applications, collaboration tools, and AI platforms. It then builds a cross‑environment view of where sensitive data lives and what it contains, based on multi‑signal, AI‑assisted classification that’s tuned to your business context.

Once it has that view, Sentra can automatically apply or correct Microsoft Purview Information Protection (MPIP) labels across M365 content and, where appropriate, back into other systems. Instead of relying on spotty manual tagging and local heuristics, you get labels that reflect a consistent, enterprise‑wide understanding of sensitivity.

Those labels become the language that Purview DLP, encryption, retention, and Copilot controls understand. You are effectively giving Microsoft’s native tools a richer, more accurate map of your data, enabling them to confidently apply appropriate controls and streamline remediations.

Making Purview DLP work smarter

When labels are trustworthy, Purview DLP policies become easier to design and maintain. Rather than creating sprawling rule sets that combine patterns, locations, and exceptions, you can express policies in simple, label‑centric terms:

• “Encrypt and allow PHI sent to approved partners; block PHI sent anywhere else.”
• “Block Highly Confidential documents shared with external accounts; prompt for justification when Internal documents leave the tenant.”

DSPM’s role is to ensure that content carrying PHI or other regulated data is actually labeled as such, whether it started life in M365 or came from elsewhere. Purview then enforces DLP based on those labels, with far fewer false positives and far fewer edge cases.

During rollout, you can run new label‑driven policies in audit mode to observe how they would behave, work with business stakeholders to adjust where necessary, and then move the most critical rules into full enforcement.

Keeping Copilot inside the guardrails

Copilot adds another dimension to this story. By design, it reads and reasons over large swaths of your content, then generates responses or summaries based on that content. If you don’t control what Copilot can see, it may surface PHI in a chat about scheduling, or include sensitive IP in a generic project update.

Here again, labels should be the control plane. Once DSPM has ensured that sensitive content is labeled accurately and consistently, you can use those labels to govern Copilot:

• Limit Copilot’s access to certain labels or sites, especially those holding PHI, PCI, or trade secrets.
• Restrict certain operations (such as summarization or sharing) when output would be based on Highly Confidential content.
• Exclude specific labeled datasets from Copilot’s index entirely.

Because DSPM also tracks where labeled data moves, it can alert you when sensitive content is copied into a location with different Copilot rules. That gives you an opportunity to remediate before an incident, rather than discovering the issue only after a problematic AI response.

A practical path for Microsoft‑centric organizations

For organizations that have standardized on Microsoft 365, the message is not “replace Purview” or “turn off Copilot.” It’s to recognize that Purview and Copilot need a stronger foundation of data intelligence to act safely and predictably.

That foundation comes from pairing DSPM and auto‑labeling with Purview’s native capabilities, which combined enable you to:

1. Discover and classify sensitive data across your full estate, including non‑Microsoft sources.
2. Auto‑apply MPIP labels so that M365 content is tagged accurately and consistently.
3. Simplify DLP and Copilot policies to be label‑driven rather than pattern‑driven.
4. Iterate in audit mode before expanding enforcement.

Once labels are fixed at the source, you can lean on Purview DLP and Copilot with much more confidence. You’ll spend less time chasing noisy alerts and unexpected AI behavior, and more time using the Microsoft ecosystem the way it was intended: as a powerful, integrated platform for secure productivity.Ready to supercharge Purview DLP and make M365 Copilot safe by fixing labels at the source? Schedule a Sentra demo

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Modern cloud environments contain vast amounts of data stored in object storage services such as Amazon S3, Google Cloud Storage, and Azure Blob Storage. In large organizations, a single data store can contain billions (or even tens of billions) of objects. In this reality, traditional approaches that rely on scanning every file to detect sensitive data quickly become impractical.

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Full object-level inspection is expensive, slow, and difficult to sustain over time. It increases cloud costs, extends onboarding timelines, and often fails to keep pace with continuously changing data. As a result, modern data security platforms must adopt more intelligent techniques to build accurate data inventories and sensitivity models without scanning every object.

Why Object-Level Scanning Fails at Scale

Object storage systems expose data as individual objects, but treating each object as an independent unit of analysis does not reflect how data is actually created, stored, or used.

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In large environments, scanning every object introduces several challenges:

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• Cost amplification from repeated content inspection at massive scale
  
• Long time to actionable insights during the first scan
  
• Operational bottlenecks that prevent continuous scanning
  
• Diminishing returns, as many objects contain redundant or structurally identical data
  
  

The goal of data discovery is not exhaustive inspection, but rather accurate understanding of where sensitive data exists and how it is organized.

The Dataset as the Correct Unit of Analysis

Although cloud storage presents data as individual objects, most data is logically organized into datasets. These datasets often follow consistent structural patterns such as:

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• Time-based partitions
  
• Application or service-specific logs
  
• Data lake tables and exports
  
• Periodic reports or snapshots
  
  

For example, the following objects are separate files but collectively represent a single dataset:

logs/2026/01/01/app_events_001.json

logs/2026/01/02/app_events_002.json

logs/2026/01/03/app_events_003.json

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While these objects differ by date, their structure, schema, and sensitivity characteristics are typically consistent. Treating them as a single dataset enables more accurate and scalable analysis.

Analyzing Storage Structure Without Reading Every File

Modern data discovery platforms begin by analyzing storage metadata and object structure, rather than file contents.

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This includes examining:

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• Object paths and prefixes
  
• Naming conventions and partition keys
  
• Repeating directory patterns
  
• Object counts and distribution
  
  

By identifying recurring patterns and natural boundaries in storage layouts, platforms can infer how objects relate to one another and where dataset boundaries exist. This analysis does not require reading object contents and can be performed efficiently at cloud scale.

Configurable by Design

Sampling can be disabled for specific data sources, and the dataset grouping algorithm can be adjusted by the user. This allows teams to tailor the discovery process to their environment and needs.

Automatic Grouping into Dataset-Level Assets

Using structural analysis, objects are automatically grouped into dataset-level assets. Clustering algorithms identify related objects based on path similarity, partitioning schemes, and organizational patterns. This process requires no manual configuration and adapts as new objects are added. Once grouped, these datasets become the primary unit for further analysis, replacing object-by-object inspection with a more meaningful abstraction.

Representative Sampling for Sensitivity Inference

After grouping, sensitivity analysis is performed using representative sampling. Instead of inspecting every object, the platform selects a small, statistically meaningful subset of files from each dataset.

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Sampling strategies account for factors such as:

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• Partition structure
  
• File size and format
  
• Schema variation within the dataset
  
  

By analyzing these samples, the platform can accurately infer the presence of sensitive data across the entire dataset. This approach preserves accuracy while dramatically reducing the amount of data that must be scanned.

Handling Non-Standard Storage Layouts

In some environments, storage layouts may follow unconventional or highly customized naming schemes that automated grouping cannot fully interpret. In these cases, manual grouping provides additional precision. Security analysts can define logical dataset boundaries, often supported by LLM-assisted analysis to better understand complex or ambiguous structures. Once defined, the same sampling and inference mechanisms are applied, ensuring consistent sensitivity assessment even in edge cases.

Scalability, Cost, and Operational Impact

By combining structural analysis, grouping, and representative sampling, this approach enables:

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• Scalable data discovery across millions or billions of objects
  
• Predictable and significantly reduced cloud scanning costs
  
• Faster onboarding and continuous visibility as data changes
  
• High confidence sensitivity models without exhaustive inspection
  
  

This model aligns with the realities of modern cloud environments, where data volume and velocity continue to increase.

From Discovery to Classification and Continuous Risk Management

Dataset-level asset discovery forms the foundation for scalable classification, access governance, and risk detection. Once assets are defined at the dataset level, classification becomes more accurate and easier to maintain over time. This enables downstream use cases such as identifying over-permissioned access, detecting risky data exposure, and managing AI-driven data access patterns.

Applying These Principles in Practice

Platforms like Sentra apply these principles to help organizations discover, classify, and govern sensitive data at cloud scale - without relying on full object-level scans. By focusing on dataset-level discovery and intelligent sampling, Sentra enables continuous visibility into sensitive data while keeping costs and operational overhead under control.

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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.

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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.

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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.

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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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