Unstructured Data Is 80% of Your Risk: Why DSPM 1.0 Vendors, Like Varonis and Cyera, Fail to Protect It at Petabyte Scale

SOC 2 has become table stakes for cloud‑native and SaaS organizations. But for many security and GRC teams, each SOC 2 cycle still feels like starting from scratch; hunting for the latest access reviews, exporting encryption settings from multiple consoles, proving backups and logs exist - per data set, per environment. If your SOC 2 evidence process is still a patchwork of spreadsheets and screenshots, you’re not alone. The missing piece is a data‑centric view of your controls, especially around regulated data.

Why SOC 2 Evidence Is So Hard in Cloud and SaaS Environments

Under SOC 2, trust service criteria like Security, Availability, and Confidentiality translate into specific expectations around data:

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Is sensitive or regulated data discovered and classified consistently?

Are core controls (encryption, backup, access, logging) actually in place where that data lives?

Can you show continuous monitoring instead of point‑in‑time screenshots?

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In a typical multi‑cloud/SaaS environment:

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• Sensitive data is scattered across S3, databases, Snowflake, M365/Google Workspace, Salesforce, and more.
• Different teams own pieces of the puzzle (infra, security, data, app owners).
• Legacy tools are siloed by layer (CSPM for infra, DLP for traffic, privacy catalog for RoPA).

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So when SOC 2 comes around, you spend weeks assembling a story instead of being able to show a trusted, provable compliance posture at the data layer.

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The Data‑First Approach to SOC 2 Evidence

Instead of treating SOC 2 as a separate project, leading teams are aligning it with their data security posture management (DSPM) strategy:

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1. Start from the data, not from the infrastructure

• Build a unified inventory of sensitive and regulated data across IaaS, PaaS, SaaS, and on‑prem.
• Enrich each store with sensitivity, residency, and business context.

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2. Attach control posture to each data store

• For each regulated data store, track encryption status, backup configuration, access model, and logging/monitoring coverage as posture attributes.

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3. Generate SOC‑aligned evidence from the same system

• Use the regulated‑data inventory plus posture engine to produce SOC 2‑friendly reports and CSVs, rather than collecting evidence manually for each audit cycle.

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This is exactly the pattern that modern data security platforms like Sentra are implementing.

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How Sentra Helps Security and GRC Teams Automate SOC 2 Evidence

Sentra sits across your data estate and focuses on regulated data, with capabilities that map directly onto SOC 2 evidence needs:

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Comprehensive data‑store discovery and classification
Agentless discovery of data stores (managed and unmanaged) across multi‑cloud and on‑prem, combined with high‑accuracy classification for regulated and business‑critical data.

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Data‑centric security posture
For each store, Sentra tracks security properties—including encryption, backup, logging, and access configuration, and surfaces gaps where sensitive data is insufficiently protected.

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Framework‑aligned reporting
SOC 2 and other frameworks can be represented as report templates that pull directly from Sentra’s inventory and posture attributes, giving GRC teams “audit‑ready” exports without rebuilding evidence from scratch.

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The result is you can prove control over regulated data, for SOC 2 and beyond, with far less manual overhead.

Mapping SOC 2 Criteria to Data‑Level Evidence

Here’s how a data‑first posture shows up in SOC 2:

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CC6.x (Logical and Physical Access Controls)

Evidence: Identity‑to‑data mapping showing which users/roles can access which sensitive datasets across cloud and SaaS.

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CC7.x (Change Management / Monitoring)

Evidence: Data Detection & Response (DDR) signals and anomaly analytics around access to crown‑jewel data; logs that tie back to sensitive data stores.

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CC8.x (Risk Mitigation)

Evidence: Risk‑prioritized view of data stores based on sensitivity and missing controls, plus remediation workflows or automatic labeling/tagging to tighten upstream policies.

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When this data‑level view is in place, SOC 2 becomes evidence selection rather than evidence construction.

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A Repeatable SOC 2 Playbook for Security, GRC, and Privacy

To operationalize this approach, many teams follow a recurring pattern:

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1. Define a “regulated data perimeter” for SOC 2: Identify which clouds, SaaS platforms, and on‑prem stores contain in‑scope data (PII, PHI, PCI, financial records).

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2. Instrument with DSPM: Deploy a data security platform like Sentra to discover, classify, and map access to that data perimeter.

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3. Connect GRC to the same source of truth: Have GRC and privacy teams pull their SOC 2 evidence from the same inventory and posture views Security uses for day‑to‑day risk management.

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4. Continuously refine controls: Use posture and DDR insights to reduce exposure, close misconfigurations, and improve your next SOC 2 cycle before it starts.

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The more you lean on a shared, data‑centric foundation, the easier it becomes to maintain a trusted, provable compliance posture across frameworks, not just SOC 2.

Turning SOC 2 From a Project Into a Capability

Ultimately, the goal is to stop treating SOC 2 as a once-a-year project and start treating it as an ongoing capability embedded into how your organization operates. Security, GRC, and privacy teams should work from a single, unified view of regulated data and controls. Evidence should always be a few clicks away - not the result of a month-long scramble. And every audit should strengthen your data security posture, not distract from it. If you’re still managing compliance in spreadsheets, it’s worth asking what it would take to make your SOC 2 posture something you can prove on demand.

Ready to end the fire drills and move to continuous compliance? Book a Demo 

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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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