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

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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Most security teams think of data classification as a single capability. A tool scans data, finds sensitive information, and labels it. Problem solved. In reality, modern data environments have made classification far more complex.

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As organizations scale across cloud platforms, SaaS apps, data lakes, collaboration tools, and AI systems, security teams must answer two fundamentally different questions:

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1. What sensitive data exists inside this asset?
2. What is this asset actually about?

These questions represent two distinct approaches:

• Entity-level data classification
• File-level (asset-level) data classification

A well-functioning Data Security Posture Management (DSPM) requires both.

What Is Entity-Level Data Classification?

Entity-level classification identifies specific sensitive data elements within structured and unstructured content. Instead of labeling an entire file as sensitive, it determines exactly which regulated entities are present and where they appear. These entities can include personal identifiers, financial account numbers, healthcare codes, credentials, digital identifiers, and other protected data types.

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This approach provides precision at the field or token level. By detecting and validating individual data elements, security teams gain measurable visibility into exposure - including how many sensitive values exist, where they are located, and how they are used. That visibility enables targeted controls such as masking, redaction, tokenization, and DLP enforcement. In cloud and AI-driven environments, where risk is often tied to specific identifiers rather than document categories, this level of granularity is essential.

Examples of Entity-Level Detection

Entity-level classifiers detect atomic data elements such as:

• Personal identifiers (names, emails, Social Security numbers)
• Financial data (credit card numbers, IBANs, bank accounts)
• Healthcare markers (diagnoses, ICD codes, treatment terms)
• Credentials (API keys, tokens, private keys, passwords)
• Digital identifiers (IP addresses, device IDs, user IDs)

This level of granularity enables precise policy enforcement and measurable risk assessment.

How Entity-Level Classification Works

High-quality entity detection is not just regex scanning. Effective systems combine multiple validation layers to reduce false positives and increase accuracy:

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• Deterministic patterns (regular expressions, format checks)
• Checksum validation (e.g., Luhn algorithm for credit cards)
• Keyword and proximity analysis
• Dictionaries and structured reference tables
• Natural Language Processing (NLP) with Named Entity Recognition
• Machine learning models to suppress noise

This multi-signal approach ensures detection works reliably across messy, real-world data.

When Entity-Level Classification Is Essential

Entity-level classification is essential when security controls depend on the presence of specific data elements rather than broad document categories. Many policies are triggered only when certain identifiers appear together ,such as a Social Security number paired with a name - or when regulated financial or healthcare data exceeds defined thresholds. In these cases, security teams must accurately locate, validate, and quantify sensitive fields to enforce controls effectively.

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This precision is also required for operational actions such as masking, redaction, tokenization, and DLP enforcement, where controls must be applied to exact values instead of entire files. In structured data environments like databases and warehouses, entity-level classification enables column- and table-level visibility, forming the basis for exposure measurement, risk scoring, and access governance decisions.

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However, entity-level detection does not explain the broader business context of the data. A credit card number may appear in an invoice, a support ticket, a legal filing, or a breach report. While the identifier is the same, the surrounding context changes the associated risk and the appropriate response.

This is where file-level classification becomes necessary.

What Is File-Level (Asset-Level) Data Classification?

File-level classification determines the semantic meaning and business context of an entire data asset.

Instead of asking what sensitive values exist, it asks:

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What kind of document or dataset is this? What is its business purpose?

Examples of File-Level Classification

File-level classifiers identify attributes such as:

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• Business domain (HR, Legal, Finance, Healthcare, IT)
  
• Document type (NDA, invoice, payroll record, resume, contract)
  
• Business purpose (compliance evidence, client matter, incident report)

This context is essential for appropriate governance, access control, and AI safety.

How File-Level Classification Works

File-level classification relies on semantic understanding, typically powered by:

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• Small and Large Language Models (SLMs/LLMs)
• Vector embeddings for topic similarity
• Confidence scoring and ensemble validation
• Trainable models for organization-specific document types

This allows systems to classify documents even when sensitive entities are sparse, masked, or absent.

For example, an employment contract may contain limited PII but still require strict access controls because of its business context.

When File-Level Classification Is Essential

File-level classification becomes essential when security decisions depend on business context rather than just the presence of sensitive strings. For example, enforcing domain-based access controls requires knowing whether a document belongs to HR, Legal, or Finance - not just whether it contains an email address or account number. The same applies to implementing least-privilege access models, where entire categories of documents may need tighter controls based on their purpose.

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File-level classification also plays a critical role in retention policies and audit workflows, where governance rules are applied to document types such as contracts, payroll records, or compliance evidence. And as organizations adopt generative AI tools, semantic understanding becomes even more important for implementing AI governance guardrails, ensuring copilots don’t ingest sensitive HR files or privileged legal documents.

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That said, file-level classification alone is not sufficient. While it can determine what a document is about, it does not precisely locate or quantify sensitive data within it. A document labeled “Finance” may or may not contain exposed credentials or an excessive concentration of regulated identifiers, risks that only entity-level detection can accurately measure.

Entity-Level vs. File-Level Classification: Key Differences

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Each approach solves a different security problem. Relying on only one creates blind spots or false positives. Together, they form a powerful combination.

Why Using Only One Approach Creates Security Gaps

Entity-Only Approaches

Tools focused exclusively on entity detection can:

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• Flag isolated sensitive values without context
  
• Generate high alert volumes
  
• Miss business intent
  
• Treat all instances of the same entity as equal risk
  

A payroll file and a legal complaint may both contain Social Security numbers — but they represent different governance needs.

File-Only Approaches

Tools focused only on semantic labeling can:

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• Identify that a document belongs to “Finance” or “HR”
  
• Apply domain-based policies
  
• Enable context-aware access
  
  

But they may miss:

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• Embedded credentials
  
• Excessive concentrations of regulated identifiers
  
• Toxic combinations of data types (e.g., PII + healthcare terms)
  
  

Without entity-level precision, risk scoring becomes guesswork.

How Effective DSPM Combines Both Layers

The real power of modern Data Security Posture Management (DSPM) emerges when entity-level and file-level classification operate together rather than in isolation. Each layer strengthens the other. Context can reinforce entity validation: for example, a dense concentration of financial identifiers helps confirm that a document truly belongs in the Finance domain or represents an invoice. At the same time, entity signals can refine context. If a file is semantically classified as an invoice, the system can apply tighter validation logic to account numbers, totals, and other financial fields, improving accuracy and reducing noise.

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This combination also enables more intelligent policy enforcement. Instead of relying on brittle, one-dimensional rules, security teams can detect high-risk combinations of data. Personal identifiers appearing within a healthcare context may elevate regulatory exposure. Credentials embedded inside operational documents may signal immediate security risk. An unusually high concentration of identifiers in an externally shared HR file may indicate overexposure. These are nuanced risk patterns that neither entity-level nor file-level classification can reliably identify alone.

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When both layers inform policy decisions, organizations can move toward true risk-based governance. Sensitivity is no longer determined solely by what specific data elements exist, nor solely by what category a document falls into, but by the intersection of the two. Risk is derived from both what is inside the data and what the data represents.

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This dual-layer approach reduces false positives, increases analyst trust, and enables more precise controls across cloud and SaaS environments. It also becomes essential for AI governance, where understanding both sensitive content and business context determines whether data is safe to expose to copilots or generative AI systems.

What to Look for in a DSPM Classification Engine

Not all DSPM platforms treat classification equally.

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When evaluating solutions, security leaders should ask:

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• Does the platform classify and validate sensitive entities beyond basic regex?
  
• Can it semantically identify document type and business domain?
  
• Are entity-level and file-level signals tightly integrated?
  
• Can policies reason across both layers simultaneously?
  
• Does risk scoring incorporate both precision and context?
  

The goal is not simply to “classify data,” but to generate actionable, risk-aligned data  intelligence.

The Bottom Line

Modern data estates are too complex for single-layer classification models. Entity-level classification provides precision, identifying exactly what sensitive data exists and where.

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File-level classification provides context - understanding what the data is and why it exists.

Together, they enable accurate risk detection, effective policy enforcement, least-privilege access, and AI-safe governance. In today’s cloud-first and AI-driven environments, data security posture management must go beyond isolated detections or broad labels. It must understand both the contents of data and its meaning - at the same time.

That’s the new standard for data classification.

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