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

December 27, 2023
3
Min Read
Data Security

What is Shadow Data?

Shadow data refers to any organizational data that exists outside the centralized and secured data management framework. This includes data that has been copied, backed up, or stored in a manner not subject to the organization's preferred security structure. This elusive data may not adhere to access control limitations or be visible to monitoring tools, posing a significant challenge for organizations. Shadow data is the ultimate ‘known unknown’. You know it exists, but you don’t know where it is exactly. And, more importantly, because you don’t know how sensitive the data is you can’t protect it in the event of a breach. 

You can’t protect what you don’t know.

Where Does Shadow Data Come From?

Whether it’s created inadvertently or on purpose, data that becomes shadow data is simply data in the wrong place, at the wrong time. Let's delve deeper into some common examples of where shadow data comes from:

  • Persistence of Customer Data in Development Environments:

The classic example of customer data that was copied and forgotten. When customer data gets copied into a dev environment from production, to be used as test data… But the problem starts when this duplicated data gets forgotten and never is erased or is backed up to a less secure location. So, this data was secure in its organic location, and never intended to be copied – or at least not copied and forgotten.

Unfortunately, this type of human error is common.

If this data does not get appropriately erased or backed up to a more secure location, it transforms into shadow data, susceptible to unauthorized access.

  • Decommissioned Legacy Applications:

Another common example of shadow data involves decommissioned legacy applications. Consider what becomes of historical customer data or Personally Identifiable Information (PII) when migrating to a new application. Frequently, this data is left dormant in its original storage location, lingering there until a decision is made to delete it - or not.  It may persist for a very long time, and in doing so, become increasingly invisible and a vulnerability to the organization.

  • Business Intelligence and Analysis:

Your data scientists and business analysts will make copies of production data to mine it for trends and new revenue opportunities.  They may test historic data, often housed in backups or data warehouses, to validate new business concepts and develop target opportunities.  This shadow data may not be removed or properly secured once analysis has completed and become vulnerable to misuse or leakage.

  • Migration of Data to SaaS Applications:

The migration of data to Software as a Service (SaaS) applications has become a prevalent phenomenon. In today's rapidly evolving technological landscape, employees frequently adopt SaaS solutions without formal approval from their IT departments, leading to a decentralized and unmonitored deployment of applications. This poses both opportunities and risks, as users seek streamlined workflows and enhanced productivity. On one hand, SaaS applications offer flexibility and accessibility, enabling users to access data from anywhere, anytime. On the other hand, the unregulated adoption of these applications can result in data security risks, compliance issues, and potential integration challenges.

  • Use of Local Storage by Shadow IT Applications:

Last but not least, a breeding ground for shadow data is shadow IT applications, which can be created, licensed or used without official approval (think of a script or tool developed in house to speed workflow or increase productivity). The data produced by these applications is often stored locally, evading the organization's sanctioned data management framework. This not only poses a security risk but also introduces an uncontrolled element in the data ecosystem.

Shadow Data vs Shadow IT

You're probably familiar with the term "shadow IT," referring to technology, hardware, software, or projects operating beyond the governance of your corporate IT. Initially, this posed a significant security threat to organizational data, but as awareness grew, strategies and solutions emerged to manage and control it effectively. Technological advancements, particularly the widespread adoption of cloud services, ushered in an era of data democratization. This brought numerous benefits to organizations and consumers by increasing access to valuable data, fostering opportunities, and enhancing overall effectiveness.

However, employing the cloud also means data spreads to different places, making it harder to track. We no longer have fully self-contained systems on-site. With more access comes more risk. Now, the threat of unsecured shadow data has appeared. Unlike the relatively contained risks of shadow IT, shadow data stands out as the most significant menace to your data security. 

The common questions that arise:

1. Do you know the whereabouts of your sensitive data?
2. What is this data’s security posture and what controls are applicable? 

3. Do you possess the necessary tools and resources to manage it effectively?

 

Shadow data, a prevalent yet frequently underestimated challenge, demands attention. Fortunately, there are tools and resources you can use in order to secure your data without increasing the burden on your limited staff.

Data Breach Risks Associated with Shadow Data

The risks linked to shadow data are diverse and severe, ranging from potential data exposure to compliance violations. Uncontrolled shadow data poses a threat to data security, leading to data breaches, unauthorized access, and compromise of intellectual property.

The Business Impact of Data Security Threats

Shadow data represents not only a security concern but also a significant compliance and business issue. Attackers often target shadow data as an easily accessible source of sensitive information. Compliance risks arise, especially concerning personal, financial, and healthcare data, which demands meticulous identification and remediation. Moreover, unnecessary cloud storage incurs costs, emphasizing the financial impact of shadow data on the bottom line. Businesses can return investment and reduce their cloud cost by better controlling shadow data.

As more enterprises are moving to the cloud, the concern of shadow data is increasing. Since shadow data refers to data that administrators are not aware of, the risk to the business depends on the sensitivity of the data. Customer and employee data that is improperly secured can lead to compliance violations, particularly when health or financial data is at risk. There is also the risk that company secrets can be exposed. 

An example of this is when Sentra identified a large enterprise’s source code in an open S3 bucket. Part of working with this enterprise, Sentra was given 7 Petabytes in AWS environments to scan for sensitive data. Specifically, we were looking for IP - source code, documentation, and other proprietary data. As usual, we discovered many issues, however there were 7 that needed to be remediated immediately. These 7 were defined as ‘critical’.

The most severe data vulnerability was source code in an open S3 bucket with 7.5 TB worth of data. The file was hiding in a 600 MB .zip file in another .zip file. We also found recordings of client meetings and a 8.9 KB excel file with all of their existing current and potential customer data. Unfortunately, a scenario like this could have taken months, or even years to notice - if noticed at all. Luckily, we were able to discover this in time.

How You Can Detect and Minimize the Risk Associated with Shadow Data

Strategy 1: Conduct Regular Audits

Regular audits of IT infrastructure and data flows are essential for identifying and categorizing shadow data. Understanding where sensitive data resides is the foundational step toward effective mitigation. Automating the discovery process will offload this burden and allow the organization to remain agile as cloud data grows.

Strategy 2: Educate Employees on Security Best Practices

Creating a culture of security awareness among employees is pivotal. Training programs and regular communication about data handling practices can significantly reduce the likelihood of shadow data incidents.

Strategy 3: Embrace Cloud Data Security Solutions

Investing in cloud data security solutions is essential, given the prevalence of multi-cloud environments, cloud-driven CI/CD, and the adoption of microservices. These solutions offer visibility into cloud applications, monitor data transactions, and enforce security policies to mitigate the risks associated with shadow data.

How You Can Protect Your Sensitive Data with Sentra’s DSPM Solution

The trick with shadow data, as with any security risk, is not just in identifying it – but rather prioritizing the remediation of the largest risks. Sentra’s Data Security Posture Management follows sensitive data through the cloud, helping organizations identify and automatically remediate data vulnerabilities by:

  • Finding shadow data where it’s not supposed to be:

Sentra is able to find all of your cloud data - not just the data stores you know about.

  • Finding sensitive information with differing security postures:

Finding sensitive data that doesn’t seem to have an adequate security posture.

  • Finding duplicate data:

Sentra discovers when multiple copies of data exist, tracks and monitors them across environments, and understands which parts are both sensitive and unprotected.

  • Taking access into account:

Sometimes, legitimate data can be in the right place, but accessible to the wrong people. Sentra scrutinizes privileges across multiple copies of data, identifying and helping to enforce who can access the data.

Key Takeaways

Comprehending and addressing shadow data risks is integral to a robust data security strategy. By recognizing the risks, implementing proactive detection measures, and leveraging advanced security solutions like Sentra's DSPM, organizations can fortify their defenses against the evolving threat landscape. 

Stay informed, and take the necessary steps to protect your valuable data assets.

To learn more about how Sentra can help you eliminate the risks of shadow data, schedule a demo with us today.

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Discover Ron’s expertise, shaped by over 20 years of hands-on tech and leadership experience in cybersecurity, cloud, big data, and machine learning. As a serial entrepreneur and seed investor, Ron has contributed to the success of several startups, including Axonius, Firefly, Guardio, Talon Cyber Security, and Lightricks, after founding a company acquired by Oracle.

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Yair Cohen
Yair Cohen
January 9, 2026
3
Min Read
Data Security

How to Prevent Data Breaches in Healthcare and Protect PHI

How to Prevent Data Breaches in Healthcare and Protect PHI

Preventing data breaches in healthcare is no longer just about stopping cyberattacks. In 2026, the greater challenge is maintaining continuous visibility into where protected health information (PHI) lives, how it is accessed, and how it is reused across modern healthcare environments governed by HIPAA compliance requirements.

PHI no longer resides in a single system or under the control of one team. It moves constantly between cloud platforms, electronic health record (EHR) systems, business associates, analytics environments, and AI tools used throughout healthcare operations. While this data sharing enables better patient care and operational efficiency, it also introduces new healthcare cybersecurity risks that traditional, perimeter-based security controls were never designed to manage.

From Perimeter Security to Data-Centric PHI Protection

Many of the most damaging healthcare data breaches in recent years have shared a common root cause:

limited visibility into sensitive data and unclear ownership across shared environments.

Over-permissioned identities, long-lived third-party access, and AI systems interacting with regulated data without proper governance can silently expand exposure until an incident forces disruptive containment measures. Protecting PHI in 2026 requires a data-centric approach to healthcare data security. Instead of focusing only on where data is stored, organizations must continuously understand what sensitive data exists, who can access it, and how that access changes over time. This shift is foundational to effective HIPAA compliance, resilient incident response, and the safe adoption of AI in healthcare.

The Importance of Data Security in Healthcare

Healthcare organizations continue to face disproportionate risk from data breaches, with incidents carrying significant financial, operational, and reputational consequences. Recent industry analyses show that healthcare remains the costliest industry for data breaches, with the average breach costing approximately $7.4 million globally in 2025 and exceeding $10 million per incident in the U.S., driven by regulatory penalties and prolonged recovery efforts.

The scale and complexity of healthcare breaches have also increased. As of late 2025, hundreds of large healthcare data breaches affecting tens of millions of individuals had already been reported in the U.S. alone, including incidents tied to shared infrastructure and third-party service providers. These events highlight how a single exposure can rapidly expand across interconnected healthcare ecosystems.

Importantly, many recent breaches are no longer caused solely by external attacks. Instead, they stem from internal access issues such as over-permissioned identities, misdirected data sharing, and long-lived third-party access, risks now amplified by analytics platforms and AI tools interacting directly with regulated data. As healthcare organizations continue to adopt new technologies, protecting PHI increasingly depends on controlling how sensitive data is accessed, shared, and reused over time, not just where it is stored.

Healthcare Cybersecurity Regulations & Standards

For healthcare organizations, it is especially crucial to protect patient data and follow industry rules. Transitioning to the cloud shouldn't disrupt compliance efforts. But staying on top of strict data privacy regulations adds another layer of complexity to managing healthcare data.

Below are some of the top healthcare cybersecurity regulations relevant to the industry.


Health Insurance Portability and Accountability Act of 1996 (HIPAA)

HIPAA is pivotal in healthcare cybersecurity, mandating compliance for covered entities and business associates. It requires regular risk assessments and adherence to administrative, physical, and technical safeguards for electronic Protected Health Information (ePHI).

HIPAA, at its core, establishes national standards to protect sensitive patient health information from being disclosed without the patient's consent or knowledge. For leaders in healthcare data management, understanding the nuances of HIPAA's Titles and amendments is essential. Particularly relevant are Title II's (HIPAA Administrative Simplification), Privacy Rule, and Security Rule.

HHS 405(d)

HHS 405(d) regulations, under the Cybersecurity Act of 2015, establish voluntary guidelines for healthcare cybersecurity, embodied in the Healthcare Industry Cybersecurity Practices (HICP) framework. This framework covers email, endpoint protection, access management, and more.

Health Information Technology for Economic and Clinical Health (HITECH) Act

The HITECH Act, enacted in 2009, enhances HIPAA requirements, promoting the adoption of healthcare technology and imposing stricter penalties for HIPAA violations. It mandates annual cybersecurity audits and extends HIPAA regulations to business associates.

Payment Card Industry Data Security Standard (PCI DSS)

PCI DSS applies to healthcare organizations processing credit cards, ensuring the protection of cardholder data. Compliance is necessary for handling patient card information.

Quality System Regulation (QSR)

The Quality System Regulation (QSR), enforced by the FDA, focuses on securing medical devices, requiring measures like access prevention, risk management, and firmware updates. Proposed changes aim to align QSR with ISO 13485 standards.

Health Information Trust Alliance (HITRUST)

HITRUST, a global cybersecurity framework, aids healthcare organizations in aligning with HIPAA guidelines, offering guidance on various aspects including endpoint security, risk management, and physical security. Though not mandatory, HITRUST serves as a valuable resource for bolstering compliance efforts.

Preventing Data Breaches in Healthcare with Sentra

Sentra’s Data Security Posture Management (DSPM) automatically discovers and accurately classifies your sensitive patient data. By seamlessly building a well-organized data catalog, Sentra ensures all your patient data is secure, stored correctly and in compliance. The best part is, your data never leaves your environment.

Discover and Accurately Classify your High Risk Patient Data

Discover and accurately classify your high-risk patient data with ease using Sentra. Within minutes, Sentra empowers you to uncover and comprehend your Protected Health Information (PHI), spanning patient medical history, treatment plans, lab tests, radiology images, physician notes, and more. 

Seamlessly build a well-organized data catalog, ensuring that all your high-risk patient data is securely stored and compliant. As a cloud-native solution, Sentra enables you to scale security across your entire data estate. Your cloud data remains within your environment, putting you in complete control of your sensitive data at all times.

Sentra Reduces Data Risks by Controlling Posture and Access

Sentra is your solution for reducing data risks and preventing data breaches by efficiently controlling posture and access. With Sentra, you can enforce security policies for sensitive data, receiving alerts to violations promptly. It detects which users have access to sensitive Protected Health Information (PHI), ensuring transparency and accountability. Additionally, Sentra helps you manage third-party access risks by offering varying levels of access to different providers. Achieve least privilege access by leveraging Sentra's continuous monitoring and tracking capabilities, which keep tabs on access keys and user identities. This ensures that each user has precisely the right access permissions, minimizing the risk of unauthorized data exposure.

Stay on Top of Healthcare Data Regulations with Sentra

Sentra’s Data Security Posture Management (DSPM) solution streamlines and automates the management of your regulated patient data, preparing you for significant security audits. Gain a comprehensive view of all sensitive patient data, allowing our platform to automatically identify compliance gaps for proactive and swift resolution.

Sentra dashboard showing compliance frameworks
Sentra Dashboard shows the issues grouped by compliance frameworks, such as HIPAA and what the compliance posture is

Easily translate your compliance requirements for HIPAA, GDPR, and HITECH into actionable rules and policies, receiving notifications when data is copied or moved between regions. With Sentra, running compliance reports becomes a breeze, providing you with all the necessary evidence, including sensitive data types, regulatory controls, and compliance status for relevant regulatory frameworks.

Conclusion: From Perimeter Security to Continuous Data Governance

Healthcare organizations can no longer rely on perimeter-based controls or periodic audits to prevent data breaches. As PHI spreads across cloud platforms, business associates, and AI-driven workflows, the risk is no longer confined to a single system, it’s embedded in how data is accessed, shared, and reused.

Protecting PHI in 2026 requires continuous visibility into sensitive data and the ability to govern it throughout its lifecycle. This means understanding what regulated data exists, who has access to it, and how that access changes over time - across internal teams, third parties, and AI systems. Without this level of insight, compliance with HIPAA and other healthcare regulations becomes reactive, and incident response becomes disruptive by default.

A data-centric security model allows healthcare organizations to reduce their breach impact, limit regulatory exposure, and adopt AI safely without compromising patient trust. By shifting from static controls to continuous data governance, security and compliance teams can move from guessing where PHI lives to managing it with confidence.

To learn more about how you can enhance your data security posture, schedule a demo with one of our data security experts.

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Yair Cohen
Yair Cohen
December 28, 2025
3
Min Read

What CISOs Learned in 2025: The 5 Data Security Priorities Coming in 2026

What CISOs Learned in 2025: The 5 Data Security Priorities Coming in 2026

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.

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.

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).

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

  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.
  1. 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.

  2. 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.
  1. 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.
  1. 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.

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.

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.

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.

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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Meni Besso
Meni Besso
December 23, 2025
Min Read
Compliance

How to Scale DSAR Compliance (Without Breaking Your Team)

How to Scale DSAR Compliance (Without Breaking Your Team)

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.

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.

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.

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.

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.

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.

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.

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.

Data Security Platform example of US SSN finding

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.

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.

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.

DSAR Requests

Establishing a DSAR Processing Pipeline

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.

  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.

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.


  • 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.

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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