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How to Scale DSAR Compliance (Without Breaking Your Team)

April 21, 2025
Min Read
Compliance

Privacy regulations such as GDPR (EU), CCPA/CPRA (California), and others are not just about legal checkboxes, they’re about building trust. In today’s data-driven world, customers expect organizations to be transparent about how their personal information is collected, used, and protected. When companies take privacy seriously, they demonstrate respect for their users, which in turn fosters loyalty and long-term engagement.

But among the many privacy requirements, Data Subject Access Requests (DSARs) can be the most complex to support. DSARs give individuals the right to request access to the personal data that an organization holds about them—often with a firm deadline of just 30 days to respond. For large enterprises with data scattered across multiple systems, both in the cloud and on-premises, even a single request can trigger a chaotic search across different platforms, manual reviews and legal oversight—it quickly becomes a race against the clock, with compliance, trust, and reputation on the line.

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

As privacy expectations grow and regulatory pressure intensifies, DSARs are no longer just a checkbox. They are a reflection of how seriously an organization takes user trust. Manual, reactive processes simply can’t keep up with the scale and complexity of modern data environments.

By automating DSAR workflows with tools like Sentra, organizations can achieve faster response times, lower operational costs, and sustained compliance - while freeing up teams to focus on higher-value privacy initiatives.

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Meni is an experienced product manager and the former founder of Pixibots (A mobile applications studio). In the past 15 years, he gained expertise in various industries such as: e-commerce, cloud management, dev-tools, mobile games, and more. He is passionate about delivering high quality technical products, that are intuitive and easy to use.

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Ward Balcerzak
Ward Balcerzak
October 20, 2025
3
Min Read
Data Security

2026 Cybersecurity Budget Planning: Make Data Visibility a Priority

2026 Cybersecurity Budget Planning: Make Data Visibility a Priority

Why Data Visibility Belongs in Your 2026 Cybersecurity Budget

As the fiscal year winds down and security leaders tackle cybersecurity budget planning for 2026, you need to decide how to use every remaining 2025 dollar wisely and how to plan smarter for next year. The question isn’t just what to cut or keep, it’s what creates measurable impact. Across programs, data visibility and DSPM deliver provable risk reduction, faster audits, and clearer ROI,making them priority line items whether you’re spending down this year or shaping next year’s plan. Some teams discover unspent funds after project delays, postponed renewals, or slower-than-expected hiring. Others are already deep in planning mode, mapping next year’s security priorities across people, tools, and processes. Either way, one question looms large: where can a limited security budget make the biggest impact - right now and next year?

Across the industry, one theme is clear: data visibility is no longer a “nice-to-have” line item, it’s a foundational control. Whether you’re allocating leftover funds before year-end or shaping your 2026 strategy, investing in Data Security Posture Management (DSPM) should be part of the plan.

As Bitsight notes, many organizations look for smart ways to use remaining funds that don’t roll over. The goal isn’t simply to spend, it’s to invest in initiatives that improve posture and provide measurable, lasting value. And according to Applied Tech, “using remaining IT funds strategically can strengthen your position for the next budget cycle.”

That same principle applies in cybersecurity. Whether you’re closing out this year or planning for 2026, the focus should be on spending that improves security maturity and tells a story leadership understands. Few areas achieve that more effectively than data-centric visibility.

(For additional background, see Sentra’s article on why DSPM should take a slice of your cybersecurity budget.)

Where to Allocate Remaining Year-End Funds (Without Hurting Next Year’s Budget)

It’s important to utilize all of your 2025 budget allocations because finance departments frequently view underspending as a sign of overfunding, leading to smaller allocations next year. Instead, strategic security teams look for ways to convert every remaining dollar into evidence of progress.

That means focusing on investments that:

  • Produce measurable results you can show to leadership.
  • Strengthen core program foundations: people, visibility, and process.
  • Avoid new recurring costs that stretch future budgets.

Top Investments That Pay Off

1. Invest in Your People

One of the strongest points echoed by security professionals across industry communities: the best investment is almost always your people. Security programs are built on human capability. Certifications, practical training, and professional growth not only expand your team’s skills but also build morale and retention, two things that can’t be bought with tooling alone.

High-impact options include:

  • Hands-on training platforms like Hack The Box, INE Skill Dive, or Security Blue Team, which develop real-world skills through simulated environments.
  • Professional certifications (SANS GIAC, OSCP, or cloud security credentials) that validate expertise and strengthen your team’s credibility.
  • Conference attendance for exposure to new threat perspectives and networking with peers.
  • Cross-functional training between SOC, GRC, and AppSec to create operational cohesion.

In practitioner discussions, one common sentiment stood out: training isn’t just an expense, it’s proof of leadership maturity.

As one manager put it, “If you want your analysts to go the extra mile during an incident, show you’ll go the extra mile for them when things are calm.”

2. Invest in Data Visibility (DSPM)

While team capability drives execution, data visibility drives confidence. In recent conversations among mid-market and enterprise security teams, Data Security Posture Management (DSPM) repeatedly surfaced as one of the most valuable investments made in the past year, especially for hybrid-cloud environments.

One security leader described it this way:

“After implementing DSPM, we finally had a clear picture of where sensitive data actually lived. It saved our team hours of manual chasing and made the audit season much easier.”

That feedback reflects a growing consensus: without visibility into where sensitive data resides, who can access it, and how it’s secured, every other layer of defense operates partly in the dark.

*Tip: If your remaining 2025 budget won’t suffice for a full DSPM deployment, you can scope an initial implementation with the remaining budget, then expand to full coverage in 2026.

DSPM solutions provide that clarity by helping teams:

  • Map and classify sensitive data across multi-cloud and SaaS environments.
  • Identify access misconfigurations or risky sharing patterns.
  • Detect policy violations or overexposure before they become incidents.

Beyond security operations, DSPM delivers something finance and leadership appreciate, measurable proof. Dashboards and reports make risk tangible, allowing CISOs to demonstrate progress in data protection and compliance.

The takeaway: DSPM isn’t just a good way to use remaining funds, it’s a baseline investment every forward-looking security program should plan for in 2026 and beyond.

3. Invest in Testing

Training builds capability. Visibility builds understanding. Testing builds credibility.

External red team, purple team, or security posture assessments continue to be among the most effective ways to validate your defenses and generate actionable findings.

Security practitioners often point out that testing engagements create outcomes leadership understands:

“Training is great, but it’s hard to quantify. An external assessment gives you findings, metrics, and a roadmap you can point to when defending next year’s budget.”

Well-scoped assessments do more than uncover vulnerabilities—they benchmark performance, expose process gaps, and generate data-backed justification for continued investment.

4. Preserve Flexibility with a Retainer

If your team can’t launch a new project before year-end, a retainer with a trusted partner is an efficient way to preserve funds without waste. Retainers can cover services like penetration testing, incident response, or advisory hours, providing flexibility when unpredictable needs arise. This approach, often recommended by veteran CISOs, allows teams to close their books responsibly while keeping agility for the next fiscal year.

5. Strengthen Your Foundations

Not every valuable investment requires new tools. Several practitioners emphasized the long-term returns from process improvements and collaboration-focused initiatives:

  • Threat modeling workshops that align development and security priorities.
  • Framework assessments (like NIST CSF or ISO 27001) that provide measurable baselines.
  • Automation pilots to eliminate repetitive manual work.
  • Internal tabletop exercises that enhance cross-team coordination.

These lower-cost efforts improve resilience and efficiency, two metrics that always matter in budget conversations.

How to Decide: A Simple, Measurable Framework

When evaluating where to allocate remaining or future funds, apply a simple framework:

  1. Identify what’s lagging. Which pillar - people, visibility, or process most limits your current effectiveness?
  2. Choose something measurable. Prioritize initiatives that produce clear, demonstrable outputs: reports, dashboards, certifications.
  3. Aim for dual impact. Every investment should strengthen both your operations and your ability to justify next year’s funding.

Final Thoughts

A strong security budget isn’t just about defense, it’s about direction. Every spend tells a story about how your organization prioritizes resilience, efficiency, and visibility.

Whether you’re closing out this year’s funds or preparing your 2026 plan, focus on investments that create both operational value and executive clarity. Because while technologies evolve and threats shift, understanding where your data is, who can access it, and how it’s protected remains the cornerstone of a mature security program.

Or, as one practitioner summed it up: “Spend on the things that make next year’s budget conversation easier.”

DSPM fits that description perfectly.

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Meni Besso
Meni Besso
October 15, 2025
3
Min Read
Compliance

Hybrid Environments: Expand DSPM with On-Premises Scanners

Hybrid Environments: Expand DSPM with On-Premises Scanners

Data Security Posture Management (DSPM) has quickly become a must-have for organizations moving to the cloud. By discovering, classifying, and protecting sensitive data across SaaS apps and cloud services, DSPM gave security teams visibility into data risks they never knew they had before.

But here’s the reality: most enterprises aren’t 100% cloud. Legacy file shares, private databases, and hybrid workloads still hold massive amounts of sensitive data. Without visibility into these environments, even the most advanced DSPM platforms leave critical blind spots.

That’s why DSPM platform support is evolving - from cloud-only to truly hybrid.

The Evolution of DSPM

DSPM emerged as a response to the visibility problem created by rapid cloud adoption. As organizations moved to cloud services, SaaS applications, and collaboration platforms, sensitive data began to sprawl across environments at a pace traditional security tools couldn’t keep up with. Security teams suddenly faced oversharing, inconsistent access controls, and little clarity on where critical information actually lived.

DSPM helped fill this gap by delivering a new level of insight into cloud data. It allowed organizations to map sensitive information across their environments, highlight risky exposures, and begin enforcing least-privilege principles at scale. For cloud-native companies, this represented a huge leap forward - finally, there was a way to keep up with constant data changes and movements, helping customers safely adopt the cloud while maintaining data security best practices and compliance and without slowing innovation.

But for large enterprises, the model was incomplete. Decades of IT infrastructure meant that vast amounts of sensitive information still lived in legacy databases, file shares, and private cloud environments. While DSPM gave them visibility in the cloud, it left everything else in the dark.

The Blind Spot of On-Prem & Private Data

Despite rapid cloud adoption and digital transformation progress, large organizations still rely heavily on hybrid and on-prem environments, since data movement to the cloud can be a year’s long process. On-premises file shares such as NetApp ONTAP, SMB, and NTFS, alongside enterprise databases like Oracle, SQL Server, and MySQL, remain central to operations. Private cloud applications are especially common in regulated industries like healthcare, finance, and government, where compliance demands keep critical data on-premises.

To scan on premises data, many DSPM providers offer partial solutions by taking ephemeral ‘snapshots’ of that data and temporarily moving it to the cloud (either within customer environment, as Sentra does, or to the vendor cloud as some others do) for classification analysis. This can satisfy some requirements, but often is seen as a compliance risk for very sensitive or private data which must remain on-premises. What’s left are two untenable alternatives - ignoring the data which leaves serious visibility gaps or utilizing manual techniques which do not scale.

These approaches were clearly not built for today’s security or operational requirements. Sensitive data is created and proliferates rapidly, which means it may be unclassified, unmonitored, and overexposed, but how do you even know? From a compliance and risk standpoint, DSPM without on-prem visibility is like watching only half the field, and leaving the other half open to attackers or accidental exposure.

Expanding with On-Prem Scanners

Sentra is changing the equation. With the launch of its on-premise scanners, the platform now extends beyond the cloud to hybrid and private environments, giving organizations a single pane of glass for all their data security.

With Sentra, organizations can:

  • Discover and classify sensitive data across traditional file shares (SMB, NFS, CIFS, NTFS) and enterprise databases (Oracle, SQL Server, MySQL, MSSQL, PostgreSDL, MongoDB, MariaDB, IBM DB2, Teradata).
  • Detects and protects critical data as it moves between on-prem and cloud environments.
  • Apply AI-powered classification and enforce Microsoft Purview labeling consistently across environments.
  • Strengthen compliance with frameworks that demand full visibility across hybrid estates.
  • Have a choice of deployment models that best fits their security, compliance, and operational requirements.

Crucially, Sentra’s architecture allows customers to ensure private data always remains in their own environment. They need not move data outside their premises and nothing is ever copied into Sentra’s cloud, making it a trusted choice for enterprises that require secure, private data processing.

Extending the Hybrid Vision

This milestone builds on Sentra’s proven track record as the only cloud-native data security platform that guarantees data always remains within the customer’s cloud environments - never copied or stored in Sentra’s cloud.

Now, Sentra’s AI-powered classification and governance engine can also be deployed in organizations that require onsite data processing, giving them the flexibility to protect both structured and unstructured data across cloud and on-premises systems.

By unifying visibility and governance across all environments while maintaining complete data sovereignty, Sentra continues to lead the next phase of DSPM, one built for modern, hybrid enterprises.

Real-World Impact

Picture a global bank: with modern customer-facing websites and mobile applications hosted in the public cloud, providing agility and scalability for digital services. At the same time, the bank continues to rely on decades-old operational databases running in its private cloud — systems that power core banking functions such as transactions and account management. Without visibility into both, security teams can’t fully understand the risks these stores may pose and enforce least privilege, prevent oversharing, or ensure compliance.

With hybrid DSPM powered by on-prem scanners, that same bank can unify classification and governance across every environment - cloud or on-prem, and close the gaps that attackers or AI systems could otherwise exploit.

Conclusion

DSPM solved the cloud problem. But enterprises aren’t just in the cloud, they’re hybrid. Legacy systems and private environments still hold critical data, and leaving them out of your security posture is no longer an option.

Sentra’s on-premise scanners mark the next stage of DSPM evolution: one unified platform for cloud, on-prem, and private environments. With full visibility, accurate classification, and consistent governance, enterprises finally have the end-to-end data security they need for the AI era. Because protecting half your data is no longer enough.

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Shiri Nossel
Shiri Nossel
September 28, 2025
4
Min Read
Compliance

The Hidden Risks Metadata Catalogs Can’t See

The Hidden Risks Metadata Catalogs Can’t See

In today’s data-driven world, organizations are dealing with more information than ever before. Data pours in from countless production systems and applications, and data analysts are tasked with making sense of it all - fast. To extract valuable insights, teams rely on powerful analytics platforms like Snowflake, Databricks, BigQuery, and Redshift. These tools make it easier to store, process, and analyze data at scale.

But while these platforms are excellent at managing raw data, they don't solve one of the most critical challenges organizations face: understanding and securing that data.

That’s where metadata catalogs come in.

Metadata Catalogs Are Essential But They’re Not Enough

Metadata catalogs such as AWS Glue, Hive Metastore, and Apache Iceberg are designed to bring order to large-scale data ecosystems. They offer a clear inventory of datasets, making it easier for teams to understand what data exists, where it’s stored, and who is responsible for it.

This organizational visibility is essential. With a good catalog in place, teams can collaborate more efficiently, minimize redundancy, and boost productivity by making data discoverable and accessible.

But while these tools are great for discovery, they fall short in one key area: security. They aren’t built to detect risky permissions, identify regulated data, or prevent unintended exposure. And in an era of growing privacy regulations and data breach threats, that’s a serious limitation.

Different Data Tools, Different Gaps

It’s also important to recognize that not all tools in the data stack work the same way. For example, platforms like Snowflake and BigQuery come with fully managed infrastructure, offering seamless integration between storage, compute, and analytics. Others, like Databricks or Redshift, are often layered on top of external cloud storage services like S3 or ADLS, providing more flexibility but also more complexity.

Metadata tools have similar divides. AWS Glue is tightly integrated into the AWS ecosystem, while tools like Apache Iceberg and Hive Metastore are open and cloud-agnostic, making them suitable for diverse lakehouse architectures.

This variety introduces fragmentation, and with fragmentation comes risk. Inconsistent access policies, blind spots in data discovery, and siloed oversight can all contribute to security vulnerabilities.

The Blind Spots Metadata Can’t See

Even with a well-maintained catalog, organizations can still find themselves exposed. Metadata tells you what data exists, but it doesn’t reveal when sensitive information slips into the wrong place or becomes overexposed.

This problem is particularly severe in analytics environments. Unlike production environments, where permissions are strictly controlled, or SaaS applications, which have clear ownership and structured access models, data lakes and warehouses function differently. They are designed to collect as much information as possible, allowing analysts to freely explore and query it.

In practice, this means data often flows in without a clear owner and frequently without strict permissions. Anyone with warehouse access, whether users or automated processes, can add information, and analysts typically have broad query rights across all data. This results in a permissive, loosely governed environment where sensitive data such as PII, financial records, or confidential business information can silently accumulate. Once present, it can be accessed by far more individuals than appropriate.

The good news is that the remediation process doesn't require a heavy-handed approach. Often, it's not about managing complex permission models or building elaborate remediation workflows. The crucial step is the ability to continuously identify and locate sensitive data, understand its location, and then take the correct action whether that involves removal, masking, or locking it down.

How Sentra Bridges the Gap Between Data Visibility & Security

This is where Sentra comes in.

Sentra’s Data Security Posture Management (DSPM) platform is designed to complement and extend the capabilities of metadata catalogs, not just to address their limitations, but to elevate your entire data security strategy. Instead of replacing your metadata layer, Sentra works alongside it enhancing your visibility with real-time insights and powerful security controls.

Sentra scans across modern data platforms like Snowflake, S3, BigQuery, and more. It automatically classifies and tags sensitive data, identifies potential exposure risks, and detects compliance violations as they happen.

With Sentra, your metadata becomes actionable.

sentra dashboard datasets

From Static Maps to Live GPS

Think of your metadata catalog as a map. It shows you what’s out there and how things are connected. But a map is static. It doesn’t tell you when there’s a roadblock, a detour, or a collision. Sentra transforms that map into a live GPS. It alerts you in real time, enforces the rules of the road, and helps you navigate safely no matter how fast your data environment is moving.

Conclusion: Visibility Without Security Is a Risk You Can’t Afford

Metadata catalogs are indispensable for organizing data at scale. But visibility alone doesn’t stop a breach. It doesn’t prevent sensitive data from slipping into the wrong place, or from being accessed by the wrong people.

To truly safeguard your business, you need more than a map of your data—you need a system that continuously detects, classifies, and secures it in real time. Without this, you’re leaving blind spots wide open for attackers, compliance violations, and costly exposure.

Sentra turns static visibility into active defense. With real-time discovery, context-rich classification, and automated protection, it gives you the confidence to not only see your data, but to secure it.

See clearly. Understand fully. Protect confidently with Sentra.

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