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

As cloud environments grow more complex in 2026, knowing how to discover sensitive data in the cloud has become one of the most pressing challenges for security and compliance teams. Data sprawls across IaaS, PaaS, SaaS platforms, and on-premise file shares, often duplicating, moving between environments, and landing in places no one intended. Without a systematic approach to discovery, organizations risk regulatory exposure, unauthorized AI access, and costly breaches. This article breaks down the key methods, tools, and architectural considerations that make cloud sensitive data discovery both effective and scalable.

Why Sensitive Data Discovery in the Cloud Is So Difficult

The core problem is visibility. Sensitive data, PII, financial records, health information, intellectual property, doesn't stay in one place. It gets copied from production to development environments, ingested into AI pipelines, backed up across regions, and shared through SaaS applications. Each transition creates a new exposure surface.

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• Toxic combinations: High-sensitivity data behind overly permissive access configurations creates dangerous scenarios that require continuous, context-aware monitoring, not just point-in-time scans.
• Shadow and ROT data: Redundant, obsolete, or trivial data inflates cloud storage costs and expands the attack surface without adding business value.
• Multi-environment sprawl: Data moves across cloud providers, regions, and service tiers, making a single unified view extremely difficult to maintain.

What Are Cloud DLP Solutions and How Do They Work?

Cloud Data Loss Prevention (DLP) solutions discover, classify, and protect sensitive information across cloud storage, applications, and databases. They operate through several interconnected mechanisms:

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• Scan and classify: Pattern matching, machine learning, and custom detectors identify sensitive content and assign classification labels (e.g., public, confidential, restricted).
• Enforce automated policies: Context-aware rules trigger encryption, masking, or access restrictions based on classification results.
• Monitor data movement: Continuous tracking of transfers and user behaviors detects anomalies like unusual download patterns or overly broad sharing.
• Integrate with broader controls: Many DLP tools work alongside CASBs and Zero Trust frameworks for end-to-end protection.

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The result is enhanced visibility into where sensitive data lives and a proactive enforcement layer that reduces breach risk while supporting regulatory compliance.

What Is Google Cloud Sensitive Data Protection?

Google Cloud Sensitive Data Protection is a cloud-native service that automatically discovers, classifies, and protects sensitive information across Cloud Storage buckets, BigQuery tables, and other Google Cloud data assets.

Core Capabilities

• Automated discovery and profiling: Scans projects, folders, or entire organizations to generate data profiles summarizing sensitivity levels and risk indicators, enabling continuous monitoring at scale.
• Detailed data inspection: Performs granular analysis using hundreds of built-in detectors alongside custom infoTypes defined through dictionaries, regular expressions, or contextual rules.
• De-identification techniques: Supports redaction, masking, and tokenization, making it a strong foundation for data governance within the Google Cloud ecosystem.

How Sensitive Data Protection’s Data Profiler Finds Sensitive Information

Sensitive Data Protection’s data profiler automates scanning across BigQuery, Cloud SQL, Cloud Storage, Vertex AI datasets, and even external sources like Amazon S3 or Azure Blob Storage (for eligible Security Command Center customers). The process starts with a scan configuration defining scope and an inspection template specifying which sensitive data types to detect.

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These profiles give security and governance teams an always-current view of where sensitive data resides and how risky each asset is.

Understanding Sensitive Data Protection Pricing

Sensitive Data Protection primarily uses per-GB profiling charges, billed based on the amount of input data scanned, with minimums and caps per dataset or table. Certain tiers of Security Command Center include organization-level discovery as part of the subscription, but for most workloads several factors directly influence total cost:

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For organizations operating at petabyte scale, these factors make it essential to design discovery workflows carefully rather than running broad, undifferentiated scans.

Tracking Data Movement Beyond Static Location

Static discovery, knowing where sensitive data sits right now, is necessary but insufficient. The real risk often emerges when data moves: from production to development, across regions, into AI training pipelines, or through ETL processes.

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• Data lineage tracking: Captures transitions in real time, not just periodic snapshots.
• Boundary crossing detection: Flags when sensitive assets cross environment boundaries or land in unexpected locations.
• Practical example: Detecting when PII flows from a production database into a dev environment is a critical control, and requires active movement monitoring.

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This is where platforms differ significantly. Some tools focus on cataloging data at rest, while more advanced solutions continuously monitor flows and surface risks as they emerge.

How Sentra Approaches Sensitive Data Discovery at Scale

Sentra is built specifically for the challenges described throughout this article. Its agentless architecture connects directly to cloud provider APIs without inline components on your data path and operates entirely in-environment, so sensitive data never leaves your control for processing. This design is critical for organizations with strict data residency requirements or preparing for regulatory audits.

Key Capabilities

• Unified multi-environment coverage: Spans IaaS, PaaS, SaaS, and on-premise file shares with AI-powered classification that distinguishes real sensitive data from mock or test data.
• DataTreks™ mapping: Creates an interactive map of the entire data estate, tracking active data movement including ETL processes, migrations, backups, and AI pipeline flows.
• Toxic combination detection: Surfaces sensitive data behind overly broad access controls with remediation guidance.
• Microsoft Purview integration: Supports automated sensitivity labeling across environments, feeding high-accuracy labels into Purview DLP and broader Microsoft 365 controls.

What Users Say (Early 2026)

Strengths:

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• Classification accuracy: Reviewers note it is “fast and most accurate” compared to alternatives.
• Shadow data discovery: “Brought visibility to unstructured data like chat messages, images, and call transcripts” that other tools missed.
• Compliance facilitation: Teams report audit preparation has become significantly more manageable.

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Considerations:

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• Initial learning curve with the dashboard configuration.
• On-premises capabilities are less mature than cloud coverage, relevant for organizations with significant legacy infrastructure.

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Beyond security, Sentra's elimination of shadow and ROT data typically reduces cloud storage costs by approximately 20%, extending the business case well beyond compliance.

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For teams looking to understand how to discover sensitive data in the cloud at enterprise scale, Sentra's Data Discovery and Classification offers a comprehensive starting point, and its in-environment architecture ensures the discovery process itself doesn't introduce new risk.

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AI assistants, SaaS, and hybrid work have made data easier than ever to discover, share, and reuse. Tools like Gemini for Google Workspace and Microsoft 365 Copilot can search across drives, mailboxes, chats, and documents in seconds - surfacing information that used to be buried in obscure folders and old snapshots.

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That’s great for productivity, but dangerous for data security.

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Traditional, policy‑based DLP wasn’t designed to handle this level of complexity. At the same time, many organizations now use DSPM tools to understand where their sensitive data lives, but still lack real‑time control over how that data moves on endpoints, in browsers, and across SaaS.

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Together, Sentra and Orion close this gap: Sentra brings next‑gen, context-driven DSPM; Orion brings next‑gen, behavior‑driven DLP. The result is end‑to‑end, AI‑ready data protection from data store to last‑mile usage, creating a learning, self‑improving posture rather than a static set of controls.

Why DSPM or DLP Alone Isn’t Enough

Modern data environments require two distinct capabilities: deep data intelligence and real-time enforcement based on contextual business context.

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DSPM solutions provide a data-centric view of risk. They continuously discover and classify sensitive data across cloud, SaaS, and on-prem environments. They map exposure, detect shadow data, and surface over-permissioned access. This gives security teams a clear understanding of what sensitive data exists, where it resides, who can access it, and how exposed it is.

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DLP solutions operate where data moves - on endpoints, in browsers, across SaaS, and in email. They enforce policies and prevent exfiltration as it happens. 

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Without rich data context like accurate sensitivity classification, exposure mapping, and identity-to-data relationships, DLP solutions often rely on predefined rules or limited signals to decide what to block, allow, or escalate.

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DLP can be enforced, but its precision depends on the quality of the data intelligence behind it.

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In AI-enabled, multi-cloud environments, visibility without enforcement is insufficient - and enforcement without deep data understanding lacks precision. To protect sensitive data from discovery by AI assistants, misuse across SaaS, or exfiltration from endpoints, organizations need accurate, continuously updated data intelligence, real-time, context-aware enforcement, and feedback between the two layers. 

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That is where Sentra and Orion complement each other.

Sentra: Data‑Centric Intelligence for AI and SaaS

Sentra provides the data foundation: a continuous, accurate understanding of what you’re protecting and how exposed it is.

Deep Discovery and Classification

Sentra continuously discovers and classifies sensitive data across cloud‑native platforms, SaaS, and on‑prem data stores, including Google Workspace, Microsoft 365, databases, and object storage. Under the hood, Sentra uses AI/ML, OCR, and transcription to analyze both structured and unstructured data, and leverages rich data class libraries to identify PII, PHI, PCI, IP, credentials, HR data, legal content, and more, with configurable sensitivity levels.

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This creates a live, contextual map of sensitive data: what it is, where it resides, and how important it is.

Reducing Shadow Data and Exposure

Sentra helps teams clean up the environment before AI and users can misuse it. 

It uncovers shadow data and obsolete assets that still carry sensitive content, highlights redundant or orphaned data that increases exposure (without adding business value), and supports collaborative workflows for remediation for security, data, and app owners.

Access Governance and Labeling for AI and DLP

Sentra turns visibility into governance signals. It maps which identities have access to which sensitive data classes and data stores, exposing overpermissioning and risky external access, and driving least‑privilege by aligning access rights with sensitivity and business needs.

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To achieve this, Sentra automatically applies and enforces:

Google Labels across Google Drive, powering Gemini controls and DLP for Drive, and Microsoft Purview Information Protection (MPIP) labels across Microsoft 365, powering Copilot and DLP policies.

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These labels become the policy fabric downstream AI and DLP engines use to decide what can be searched, summarized, or shared.

Orion: Behavior‑Driven DLP That Thinks Beyond Policies

Orion replaces policy reliance with a set of intelligent, context-aware proprietary AI agents

AI Agents That Understand Context

Orion’s agents collect rich context about data, identity, environment, and business relationships. 

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This includes mapping data lineage and movement patterns from source to destination, a contextual understanding of identities (role, department, tenure, and more), environmental context (geography, network zone, working hours), external business relationships (vendor/customer status), Sentra’s data classification, and more. 

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Based on this rich, business-aware context, Orion’s agents detect indicators of data loss and stop potential exfiltrations before they become incidents. That means a full alignment between DLP and how your business actually operates, rather than how it was imagined in static policies.

Unified Coverage Where Data Moves

Orion is designed as a unified DLP solution, covering: 

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• Endpoints
• SaaS applications
• Web and cloud
• Email
• On‑prem and storage, including channels like print

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From initial deployment, Orion quickly provides meaningful detections grounded in real behavior, not just pattern hits. Security teams then get trusted, high‑quality alerts.

Better Together: End‑to‑End, AI‑Ready Protection

Individually, Sentra and Orion address critical yet distinct challenges. Together, they create a closed loop:

Sentra → Orion: Smarter Detections

Sentra gives Orion high‑quality context:

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• Which assets are truly sensitive, and at what level.
• Where they live, how widely they’re exposed, and which identities can reach them.
• Which documents and stores carry labels or policies that demand stricter treatment.

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Orion uses this information to prioritize and enrich detections, focusing on events involving genuinely high‑risk data. It can then adapt behavior models to each user and data class, improving precision over time.

Orion → Sentra: Real‑World Feedback

Orion’s view into actual data movement feeds back into Sentra, exposing data stores that repeatedly appear in risky behaviors and serve as prime candidates for cleanup or stricter access governance. It also highlights identities whose actions don’t align with their expected access profile, feeding Sentra’s least‑privilege workflows. This turns data protection into a self‑improving system instead of a set of static controls.

What this means for Security and Risk Teams

With Sentra and Orion together, organizations can:

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• Securely adopt AI assistants like Gemini and Copilot, with Sentra controlling what they can see and Orion controlling how data is actually used on endpoints and SaaS.
• Eliminate shadow data as an exfil path by first mapping and reducing it with Sentra, then guarding remaining high‑risk assets with Orion until they’re remediated.
• Make least‑privilege real, with Sentra defining who should have access to what and Orion enforcing that principle in everyday behavior.
• Provide auditors and boards with evidence that sensitive data is discovered, governed, and protected from exfiltration across both data platforms and endpoints.

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Instead of choosing between “see everything but act slowly” (DSPM‑only) and “act without deep context” (DLP‑only), Sentra and Orion let you do both well - with one data‑centric brain and one behavior‑aware nervous system.

Ready to See Sentra + Orion in Action?

If you’re looking to secure AI adoption, reduce data loss risk, and retire legacy DLP noise, the combination of Sentra DSPM and Orion DLP offers a practical, modern path forward.

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See how a unified, AI‑ready data protection architecture can look in your environment by mapping your most critical data and exposures with Sentra, and letting Orion protect that data as it moves across endpoints, SaaS, and web in real time.

Request a joint demo to explore how Sentra and Orion together can help you think beyond policies and build a data protection program designed for the AI era.

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Enterprises managing sprawling multi-cloud environments struggle to keep ROPA (Records of Processing Activities) reporting accurate and up to date for GDPR compliance. As manual, spreadsheet-based workflows hit their limits, automation has become essential - not just to save time, but to build confidence in what data is actually being processed across the organization.

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Recently, during a strategy session, a leading GDPR-regulated customer shared how they are using Sentra to move beyond manual ROPA processes. By relying on Sentra’s automated data discovery, AI-driven classification, and environment-aware reporting, the organization has operationalized a high-confidence ROPA across ~100 cloud accounts. Their experience highlights a critical shift: ROPA as a trusted source of truth rather than a checkbox exercise.

Why ROPA Often Comes Up Short in Practice

For many organizations, maintaining a ROPA is a regulatory requirement, but not a reliable one.

As the customer explained:

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“What I’ve often seen is the ROPA or the records of processing activity being something that is a very checkbox thing to do. And that’s because it’s really hard to understand what data you actually have unless you literally go and interrogate every database.”

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Without direct visibility into cloud data stores, ROPA documentation often relies on assumptions, interviews, and outdated spreadsheets. This approach doesn’t scale and creates risk during audits, due diligence, and regulatory inquiries, especially for companies operating across multiple clouds or growing through acquisition.

From Guesswork to a High-Confidence ROPA

The same customer described how Sentra fundamentally changed their approach:

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“What Sentra allowed us to do is really have what I’ll describe as a high confidence ROPA. Our ROPA wasn’t guesswork, it was based on actual information that Sentra had gone out, touched our databases, looked inside them, identified the specific types of data records, and then gave us that inventory of what we had.”

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By directly scanning databases and cloud data stores, Sentra replaces assumptions with facts. ROPA reports are generated from live discovery results, giving compliance teams confidence that they can accurately attest to:

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• What personal data they hold
• Where it resides
• How it is processed
• And how it is governed

This transforms ROPA from a static document into a defensible, audit-ready asset.

The Need for Automated ROPA Reporting at Scale

Manual ROPA reporting becomes unmanageable as cloud environments expand. Organizations with dozens or hundreds of cloud accounts quickly face gaps, inconsistencies, and outdated records. Industry research shows that privacy automation can reduce manual ROPA effort by up to 80% and overall compliance workload by 60%. But effective automation requires focus. Reporting must concentrate on production environments, where real customer data lives, rather than drowning teams in noise from test or development systems.

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As a privacy champion on this project, explains:

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“What I’m interested in is building a data inventory that gives me insight from a privacy point of view on what kind of customer data we are holding.”

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This shift toward privacy-focused inventories ensures ROPA reporting stays meaningful, actionable, and aligned with regulatory intent.

How Sentra Enables Template-Driven, Environment-Aware ROPA Reporting

Sentra’s reporting framework allows organizations to create custom ROPA templates tailored to their regulatory, operational, and business needs. These templates automatically pull from continuously updated discovery and classification results, ensuring reports stay accurate as environments evolve.

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A critical component of this approach is environment tagging. By clearly distinguishing production systems from non-production environments, Sentra ensures ROPA reports reflect only systems that actually process personal data. This reduces reporting noise, improves audit clarity, and aligns with modern GDPR automation best practices.

The result is ROPA reporting that is both scalable and precise - without requiring manual filtering or spreadsheet maintenance.

Solving the Data Classification Problem with Context-Aware AI

Accurate ROPA automation depends on intelligent data classification. Many tools rely on basic pattern matching, which often leads to false positives, such as mistaking airline or airport codes for regulated personal data in HR or internal systems.

Sentra addresses this challenge with AI-based, context-aware classification that understands how data is structured, where it appears, and how it is used. Rather than flagging data solely based on patterns, Sentra analyzes context to reliably distinguish between regulated personal data and non-regulated business data.

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This approach dramatically reduces false positives and gives privacy teams confidence that ROPA reports reflect real regulatory exposure - without manual cleanup, lookup tables, or ongoing tuning.

What Sets Sentra Apart for ROPA Automation

While many platforms claim to support ROPA automation, few can deliver accurate, production-ready reporting across complex cloud environments. Sentra stands out through:

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• Agentless data discovery
• Native multi-cloud support (AWS, Azure, GCP, and hybrid)
• Context-aware AI classification
• Data-centric inventory of all customer regulated data
• Flexible, customizable ROPA reporting templates
• Strong handling of inconsistent metadata and environment tagging
  
  

As the customer summarized:

“It’s no longer a checkbox exercise. It’s a very high confidence attestation of what we definitely have. That visibility allowed us to comply with GDPR in a much more comprehensive way.”

Conclusion

ROPA automation is not just about efficiency, it’s about trust. By grounding ROPA reporting in real data discovery, environment awareness, and AI-driven classification, Sentra enables organizations to replace guesswork with confidence.

The result is a scalable, defensible ROPA that reduces manual effort, lowers compliance risk, and supports long-term privacy maturity.

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Interested in seeing high-confidence ROPA automation in action? Book a demo with Sentra to learn how you can turn ROPA into a living source of truth for GDPR compliance.

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