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

2025 was a pivotal year for Chief Information Security Officers (CISOs). As cyber threats surged and digital acceleration transformed business, CISOs gained more influence in boardrooms but also took on greater accountability. The old model of perimeter-based defense has ended. Security strategies now focus on resilience and real-time visibility with sensitive data protection at the core.

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As 2026 approaches, CISOs are turning this year’s lessons into a proactive, AI-smart, and business-aligned strategy. This article highlights the top CISO priorities for 2026, the industry’s shift from prevention to resilience, and how Sentra supports security leaders in this new phase.

Lessons from 2025: Transparency, AI Risk, and Platform Resilience

Over the past year, CISOs encountered high-profile breaches and shifting demands. According to the Splunk 2025 CISO Report an impressive 82% reported direct interactions with CEOs, and 83% regularly attended board meetings. Still, only 29% of board members had cybersecurity experience, leading to frequent misalignment around budgets, innovation, and staffing.

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The data is clear: 76% of CISOs expected a significant cyberattack, but 58% felt unprepared, as reported in the Proofpoint 2025 Voice of the CISO Report. Many CISOs struggled with overwhelming tool sprawl and alert fatigue, 76% named these as major challenges. The rapid growth in cloud, SaaS, and GenAI environments left major visibility gaps, especially for unstructured and shadow data. Most of all, CISOs concluded that resilience - quick detection, rapid response, and keeping the business running, matters more than just preventing attacks. This shift is changing the way security budgets will be spent in 2026.

The Evolution of DSPM: From Inventory to Intelligent, AI-Aware Defense

First generation data security posture management (DSPM) tools focused on identifying assets and manually classifying data. Now, CISOs must automatically map, classify, and assign risk scores to data - structured, unstructured, or AI-generated - across cloud, on-prem and SaaS environments, instantly. If organizations lack this capability, critical data remains at risk (Data as the Core Focus in the Cloud Security Ecosystem).

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AI brings both opportunity and risk. CISOs are working to introduce GenAI security policies while facing challenges like data leakage, unsanctioned AI projects, and compliance issues. DSPM solutions that use machine learning and real-time policy enforcement have become essential.

The Top Five CISO Priorities in 2026

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1. Secure and Responsible AI: As AI accelerates across the business, CISOs must ensure it does not introduce unmanaged data risk. The focus will be on maintaining visibility and control over sensitive data used by AI systems, preventing unintended exposure, and establishing governance that allows the company to innovate with AI while protecting trust, compliance, and brand reputation.

2. Modern Data Governance: As sensitive data sprawls across on-prem, cloud, SaaS, and data lakes, CISOs face mounting compliance pressure without clear visibility into where that data resides. The priority will be establishing accurate classification and governance of sensitive, unstructured, and shadow data - not only to meet regulatory obligations, but to proactively reduce enterprise risk, limit blast radius, and strengthen overall security posture.
   
   
3. Tool Consolidation: As cloud and application environments grow more complex, CISOs are under pressure to reduce data sprawl without increasing risk. The priority is consolidating fragmented cloud and application security tools into unified platforms that embed protection earlier in the development lifecycle, improve risk visibility across environments, and lower operational overhead. For boards, this shift represents both stronger security outcomes and a clearer return on security investment through reduced complexity, cost, and exposure.

4. Offensive Security/Continuous Testing: One-time security assessments can no longer keep pace with AI-driven and rapidly evolving threats. CISOs are making continuous offensive security a core risk-management practice, regularly testing environments across hardware, cloud, and SaaS to expose real-world vulnerabilities. For the board, this provides ongoing validation of security effectiveness and reduces the likelihood of unpleasant surprises from unknown exposures. Some exciting new AI red team solutions are appearing on the scene such as 7ai, Mend.io, Method Security, and Veria Labs.

5. Zero Trust Identity Governance: Identity has become the primary attack surface, making advanced governance essential rather than optional. CISOs are prioritizing data-centric, Zero Trust identity controls to limit excessive access, reduce insider risk, and counter AI-enabled attacks. At the board level, this shift is critical to protecting sensitive assets and maintaining resilience against emerging threats.

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These areas show a greater need for automation, better context, and clearer reporting for boards.

Sentra Enables Secure and Responsible AI with Modern Data Governance

As AI becomes central to business strategy, CISOs are being held accountable for ensuring innovation does not outpace security, governance, or trust. Secure and Responsible AI is no longer about policy alone, it requires continuous visibility into the sensitive data flowing into AI systems, control over shadow and AI-generated data, and the ability to prevent unintended exposure before it becomes a business risk.

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At the same time, Modern Data Governance has emerged as a foundational requirement. Exploding data volumes across cloud, SaaS, data lakes, and on-prem environments have made traditional governance models ineffective. CISOs need accurate classification, unified visibility, and enforceable controls that go beyond regulatory checkboxes to actively reduce enterprise risk.

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Sentra brings these priorities together by giving security leaders a clear, real-time understanding of where sensitive data lives, how it is being used - including by AI - and where risk is accumulating across the organization. By unifying DSPM and Data Detection & Response (DDR), Sentra enables CISOs to move from reactive security to proactive governance, supporting AI adoption while maintaining compliance, resilience, and board-level confidence.

Looking ahead to 2026, the CISOs who lead will be those who can see, govern, and secure their data everywhere it exists and ensure it is used responsibly to power the next phase of growth. Sentra provides the foundation to make that possible.

Conclusion

The CISO’s role in 2025 shifted from putting out fires to driving change alongside business leadership. Expectations will keep rising in 2026; balancing board expectations, the opportunities and threats of AI, and constant new risks takes a smart platform and real-time clarity.

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Sentra delivers the foundation and intelligence CISOs need to build resilience, stay compliant, and fuel data-powered AI growth with secure data. Those who can see, secure, and respond wherever their data lives will lead. Sentra is your partner to move forward with confidence in 2026.

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Data Subject Access Requests (DSARs) are one of the most demanding requirements under privacy regulations such as GDPR and CPRA. As personal data spreads across cloud, SaaS, and legacy systems, responding to DSARs manually becomes slow, costly, and error-prone. This article explores why DSARs are so difficult to scale, the key challenges organizations face, and how DSAR automation enables faster, more reliable compliance.

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Privacy regulations are no longer just legal checkboxes, they are a foundation of customer trust. In today’s data-driven world, individuals expect transparency into how their personal information is collected, used, and protected. Organizations that take privacy seriously demonstrate respect for their users, strengthening trust, loyalty, and long-term engagement.

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Among these requirements, DSARs are often the most complex to support. They give individuals the right to request access to their personal data, typically with a strict response deadline of 30 days. For large enterprises with data scattered across cloud, SaaS, and on-prem environments, even a single request can trigger a frantic search across multiple systems, manual reviews, and legal oversight - quickly turning DSAR compliance into a race against the clock, with reputation and regulatory risk on the line.

What Is a Data Subject Access Request (DSAR)?

A Data Subject Access Request (DSAR) is a legal right granted under privacy regulations such as GDPR and CPRA that allows individuals to request access to the personal data an organization holds about them. In many cases, individuals can also request information about how that data is used, shared, or deleted.

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Organizations are typically required to respond to DSARs within a strict timeframe, often 30 days, and must provide a complete and accurate view of the individual’s personal data. This includes data stored in databases, files, logs, SaaS platforms, and other systems across the organization.

Why DSAR Requests Are Difficult to Manage at Scale

DSARs are relatively manageable for small organizations with limited systems. At enterprise scale, however, they become significantly more complex. Personal data is no longer centralized. It is distributed across cloud platforms, SaaS applications, data lakes, file systems, and legacy infrastructure. Privacy teams must coordinate with IT, security, legal, and data owners to locate, review, and validate data before responding. As DSAR volumes increase, manual processes quickly break down, increasing the risk of delays, incomplete responses, and regulatory exposure.

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Key Challenges in Responding to DSARs

Data Discovery & Inventory

‍For large organizations, pinpointing where personal data resides across a diverse ecosystem of information systems, including databases, SaaS applications, data lakes, and legacy environments, is a complex challenge. The presence of fragmented IT infrastructure and third-party platforms often leads to limited visibility, which not only slows down the DSAR response process but also increases the likelihood of missing or overlooking critical personal data.

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Linking Identities Across Systems‍

A single individual may appear in multiple systems under different identifiers, especially if systems have been acquired or integrated over time. Accurately correlating these identities to compile a complete DSAR response requires sophisticated identity resolution and often manual effort.

Unstructured Data Handling

‍Unlike structured databases, where data is organized into labeled fields and can be efficiently queried, unstructured data (like PDFs, documents, and logs) is free-form and lacks consistent formatting. This makes it much harder to search, classify, or extract relevant personal information.
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Response Timeliness

‍Regulatory deadlines force organizations to respond quickly, even when data must be gathered from multiple sources and reviewed by legal teams. Manual processes can lead to delays, risking non-compliance and fines.

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Volume & Scalability

‍While most organizations can handle an occasional DSAR manually, spikes in request volume - driven by events like regulatory campaigns or publicized incidents - can overwhelm privacy and legal teams. Without scalable automation, organizations face mounting operational costs, missed deadlines, and an increased risk of inconsistent or incomplete responses.

The Role of Data Security Platforms in DSAR Automation

Sentra is a modern data security platform dedicated to helping organizations gain complete visibility and control over their sensitive data. By continuously scanning and classifying data across all environments (including cloud, SaaS, and on-premises systems) Sentra maintains an always up-to-date data map, giving organizations a clear understanding of where sensitive data resides, how it flows, and who has access to it. This data map forms the foundation for efficient DSAR automation, enabling Sentra’s DSAR module to search for user identifiers only in locations where relevant data actually exists - ensuring high accuracy, completeness, and fast response times.

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Another key factor in managing DSAR requests is ensuring that sensitive customer PII doesn’t end up in unauthorized or unintended environments. When data is copied between systems or environments, it’s essential to apply tokenization or masking to prevent unintentional sprawl of PII. Sentra helps identify misplaced or duplicated sensitive data and alerts when it isn’t properly protected. This allows organizations to focus DSAR processing within authorized operational environments, significantly reducing both risk and response time.

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Smart Search of Individual Data

To initiate the generation of a Data Subject Access Request (DSAR) report, users can submit one or more unique identifiers—such as email addresses, Social Security numbers, usernames, or other personal identifiers—corresponding to the individual in question. Sentra then performs a targeted scan across the organization’s data ecosystem, focusing on data stores known to contain personally identifiable information (PII). This includes production databases, data lakes, cloud storage services, file servers, and both structured and unstructured data sources.

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Leveraging its advanced classification and correlation capabilities, Sentra identifies all relevant records associated with the provided identifiers. Once the scan is complete, it compiles a comprehensive DSAR report that consolidates all discovered personal data linked to the data subject that can be downloaded as a PDF for manual review or securely retrieved via Sentra’s API.

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Establishing a DSAR Processing Pipeline
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Large organizations that receive a high volume of DSAR (Data Subject Access Request) submissions typically implement a robust, end-to-end DSAR processing pipeline. This pipeline is often initiated through a self-service privacy portal, allowing individuals to easily submit requests for access or deletion of their personal data. Once a request is received, an automated or semi-automated workflow is triggered to handle the request efficiently and in compliance with regulatory timelines.

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1. Requester Identity Verification: Confirm the identity of the data subject to prevent unauthorized access (e.g., via email confirmation or secure login).
   
   
2. Mapping Identifiers: Collect and map all known identifiers for the individual across systems (e.g., email, user ID, customer number).
   
   
3. Environment-Wide Data Discovery (via Sentra): Use Sentra to search all relevant environments — cloud, SaaS, on-prem — for personal data tied to the individual. By using Sentra’s automated discovery and classification, Sentra can automatically identify where to search for.
   
   
4. DSAR Report Generation (via Sentra): Compile a detailed report listing all personal data found and where it resides.
   
   
5. Data Deletion & Verification: Remove or anonymize personal data as required, then rerun a search to verify deletion is complete.
   
   
6. Final Response to Requester: Send a confirmation to the requester, outlining the actions taken and closing the request.

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Sentra plays a key role in the DSAR pipeline by exposing a powerful API that enables automated, organization-wide searches for personal data. The search results can be programmatically used to trigger downstream actions like data deletion. After removal, the API can initiate a follow-up scan to verify that all data has been successfully deleted.

Benefits of DSAR Automation 

With privacy regulations constantly growing, and DSAR volumes continuing to rise, building an automated, scalable pipeline is no longer a luxury - it’s a necessity.

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• Automated and Cost-Efficient: Replaces costly, error-prone manual processes with a streamlined, automated approach.
  
• High-Speed, High-Accuracy: Sentra leverages its knowledge of where PII resides to perform targeted searches across all environments and data types, delivering comprehensive reports in hours—not days.
  
• Seamless Integration: A powerful API allows integration with workflow systems, enabling a fully automated, end-to-end DSAR experience for end users.

By using Sentra to intelligently locate PII across all environments, organizations can eliminate manual bottlenecks and accelerate response times. Sentra’s powerful API and deep data awareness make it possible to automate every step of the DSAR journey - from discovery to deletion - enabling privacy teams to operate at scale, reduce costs, and maintain compliance with confidence. 

Turning DSAR Compliance into a Scalable Advantage with Automation

As privacy expectations grow and regulatory pressure intensifies, DSARs are no longer just a compliance checkbox, they are a reflection of how seriously an organization treats user trust. Manual, reactive processes simply cannot keep up with the scale and complexity of modern data environments, especially as personal data continues to spread across cloud, SaaS, and on-prem systems.

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By automating DSAR workflows with a data-centric security platform like Sentra, organizations can respond faster, reduce compliance risk, and lower operational costs - all while freeing privacy and legal teams to focus on higher-value initiatives. In this way, DSAR compliance becomes not just a regulatory obligation, but a measure of operational maturity and a scalable advantage in building long-term trust.

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Learn how to build automated data security policies that reduce data exposure, meet GDPR, PCI DSS, and HIPAA requirements, and scale data governance across cloud, SaaS, and AI-driven environments as organizations move into 2026.

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As 2025 comes to a close, one reality is clear: automated data security and governance programs are a must-have to truly leverage data and AI. Sensitive data now moves faster than human review can keep up with. It flows across multi-cloud storage, SaaS platforms, collaboration tools, logging pipelines, backups, and increasingly, AI and analytics workflows that continuously replicate data into new locations. For security and compliance teams heading into 2026, periodic audits and static policies are no longer sufficient. Regulators, customers, and boards now expect continuous visibility and enforcement.

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This is why automated data security policies have become a foundational control, not a “nice to have.”

In this blog, we focus on how data security policies are actually used at the end of 2025, and how to design them so they remain effective in 2026.

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You’ll learn:

• The most important compliance and risk-driven policy use cases
• How organizations operationalize data security policies at scale
• Practical examples aligned with GDPR, PCI DSS, HIPAA, and internal governance
  

Why Automated Data Security Policies Matter Heading into 2026

The direction of regulatory enforcement and threat activity is consistent:

• Continuous compliance is now expected, not implied
• Overexposed data is increasingly used for extortion, not just theft
• Organizations must prove they know where sensitive data lives and who can access it
  

Recent enforcement actions have shown that organizations can face penalties even without a breach, simply for storing regulated data in unapproved locations or failing to enforce access controls consistently.

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Automated data security policies address this gap by continuously evaluating:

• Data sensitivity
• Access scope
• Storage location and residency
• surfacing violations in near real time.

Three Data Security Policy Use Cases That Deliver Immediate Value

As organizations prepare for 2026, most start with policies that reduce data  exposure quickly.

1. Limiting Data Exposure and Ransomware Impact

Misconfigured access and excessive sharing remain the most common causes of data exposure. In cloud and SaaS environments, these issues often emerge gradually, and go unnoticed without automation.

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High-impact policies include:

• Sensitive data shared with external users: Detect files containing credentials, PII, or financial data that are accessible to outside collaborators.
  
• Overly broad internal access to sensitive data: Identify data shared with “Anyone in the organization,” significantly increasing exposure during account compromise.
  

These policies reduce blast radius and help prevent data from becoming leverage in extortion-based attacks.

2. Enforcing Secure Data Storage and Handling (PCI DSS, HIPAA, SOC 2)

Compliance violations in 2025 rarely result from intentional misuse. They happen because sensitive data quietly appears in the wrong systems.

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Common policy findings include:

• Payment card data in application logs or monitoring tools: A persistent PCI DSS issue, especially in modern microservice environments.
  
• Employee or patient records stored in collaboration platforms: PII and PHI often end up in user-managed drives without appropriate safeguards.
  

Automated policies continuously detect these conditions and support fast remediation, reducing audit findings and operational risk.

3. Maintaining Data Residency and Sovereignty Compliance

As global data protection enforcement intensifies, data residency violations remain one of the most common and costly compliance failures.

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Automated policies help identify:

• EU personal data stored outside approved EU regions: A direct GDPR violation that is common in multi-cloud and SaaS environments.
  
• Cross-region replicas and backups containing regulated data: Secondary storage locations frequently fall outside compliance controls.
  
  

These policies enable organizations to demonstrate ongoing compliance, not just point-in-time alignment.

What Modern Data Security Policies Must Do (2026-Ready)

As teams move into 2026, effective data security policies share three traits:

1. They are data-aware: Policies are based on data sensitivity - not just resource labels or storage locations.
   
2. They operate continuously: Policies evaluate changes as data is created, moved, shared, or copied into new systems.
   
3. They drive action: Every violation maps to a remediation path: restrict access, move data, or delete it.
   

This is what allows security teams to scale governance without slowing the business.

Conclusion: From Static Rules to Continuous Data Governance

Heading into 2026, automated data security policies are no longer just compliance tooling, they are a core layer of modern security architecture.

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They allow organizations to:

• Reduce exposure and ransomware risk
• Enforce regulatory requirements continuously
• Govern sensitive data across cloud, SaaS, and AI workflows

Most importantly, they replace reactive audits with real-time data governance.

Organizations that invest in automated, data-aware security policies today will enter 2026 better prepared for regulatory scrutiny, evolving threats, and the continued growth of their data footprint.

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