All Resources
In this article:
minus iconplus icon
Share the Blog

Understanding Data Movement to Avert Proliferation Risks

April 10, 2024
4
 Min Read
Data Sprawl

Understanding the perils your cloud data faces as it proliferates throughout your organization and ecosystems is a monumental task in the highly dynamic business climate we operate in. Being able to see data as it is being copied and travels, monitor its activity and access, and assess its posture allows teams to understand and better manage the full effect of data sprawl. 

It ‘connects the dots’ for security analysts who must continually evaluate true risks and threats to data so they can prioritize their efforts. Data similarity and movement are important behavioral indicators in assessing and addressing those risks. This blog will explore this topic in depth.

What Is Data Movement

Data movement is the process of transferring data from one location or system to another – from A to B. This transfer can be between storage locations, databases, servers, or network locations. Copying data from one location to another is simple, however, data movement can get complicated when managing volume, velocity, and variety.

  • Volume: Handling large amounts of data.
  • Velocity: Overseeing the pace of data generation and processing.
  • Variety: Managing a variety of data types.

How Data Moves in the Cloud

Data is free and can be shared anywhere. The way organizations leverage data is an integral part of their success. Although there are many business benefits to moving and sharing data (at a rapid pace), there are also many concerns that arise, mainly dealing with privacy, compliance, and security. Data needs to move quickly, securely, and have the proper security posture at all times.  

These are the main ways that data moves in the cloud:

1. Data Distribution in Internal Services: Internal services and applications manage data, saving it across various locations and data stores.

2. ETLs: Extract, Transform, Load processes, involve combining data from multiple sources into a central repository known as a data warehouse. This centralized view supports applications in aggregating diverse data points for organizational use.

3. Developer and Data Scientist Data Usage: Developers and data scientists utilize data for testing and development purposes. They require both real and synthetic data to test applications and simulate real-life scenarios to drive business outcomes.

4. AI/ML/LLM and Customer Data Integration: The utilization of customer data in AI/ML learning processes is on the rise. Organizations leverage such data to train models and apply the results across various organizational units, catering to different use-cases.

What Is Misplaced Data

"Misplaced data" refers to data that has been moved from an approved environment to an unapproved environment. For example, a folder that is stored in the wrong location within a computer system or network. This can result from human error, technical glitches, or issues with data management processes. 

When unauthorized data is stored in an environment that is not designed for the type of data, it can lead to data leaks, security breaches, compliance violations, and other negative outcomes.

With companies adopting more cloud services, and being challenged with properly managing the subsequent data sprawl, having misplaced data is becoming more common, which can lead to security, privacy, and compliance issues.

The Challenge of Data Movement and Misplaced Data

Organizations strive to secure their sensitive data by keeping it within carefully defined and secure environments. The pervasive data sprawl faced by nearly every organization in the cloud makes it challenging to effectively protect data, given its rapid multiplication and movement.

It is encouraged for business productivity to leverage data and use it for various purposes that can help enhance and grow the business. However, with the advantages, come disadvantages. There are risks to having multiple owners and duplicate data..

To address this challenge, organizations can leverage the analysis of similar data patterns to gain a comprehensive understanding on how data flows within the organization and help security teams first get visibility of those movement patterns, and then identify whether this movement is authorized. Then they can protect it accordingly and understand which unauthorized movement should be blocked.

This proactive approach allows them to position themselves strategically. It can involve ensuring robust security measures for data at each location, re-confining it by relocating, or eliminating unnecessary duplicates. Additionally, this analytical capability proves valuable in scenarios tied to regulatory and compliance requirements, such as ensuring GDPR - compliant data residency.

 Identifying Redundant Data and Saving Cloud Storage Costs

The identification of similarities empowers Chief Information Security Officers (CISOs) to implement best practices, steering clear of actions that lead to the creation of redundant data.

Detecting redundant data helps reduce cloud storage costs and drive up operational efficiency from targeted and prioritized remediation efforts that focus on the critical data risks that matter. 

This not only enhances data security posture, but also contributes to a more streamlined and efficient data management strategy.

“Sentra has helped us to reduce our risk of data breaches and to save money on cloud storage costs.”

-Benny Bloch, CISO at Global-e

Security Concerns That Arise

  1. Data Security Posture Variations Across Locations: Addressing instances where similar data, initially secure, experiences a degradation in security posture during the copying process (e.g., transitioning from private to public, or from encrypted to unencrypted).
  1. Divergent Access Profiles for Similar Data: Exploring scenarios where data, previously accessible by a limited and regulated set of identities, now faces expanded access by a larger number of identities (users), resulting in a loss of control.
  1. Data Localization and Compliance Violations: Examining situations where data, mandated to be localized in specific regions, is found to be in violation of organizational policies or compliance rules (with GDPR as a prominent example). By identifying similar sensitive data, we can pinpoint these issues and help users mitigate them.
  1. Anonymization Challenges in ETL Processes: Identifying issues in ETL processes where data is not only moved but also anonymized. Pinpointing similar sensitive data allows users to detect and mitigate anonymization-related problems.
  1. Customer Data Migration Across Environments: Analyzing the movement of customer data from production to development environments. This can be used by engineers to test real-life use-cases.
  2. Data Data Democratization and Movement Between Cloud and Personal Stores: Investigating instances where users export data from organizational cloud stores to personal drives (e.g., OneDrive) for purposes of development, testing, or further business analysis. Once this data is moved to personal data stores, it typically is less secure. This is due to the fact that these personal drives are less monitored and protected, and in control of the private entity (the employee), as opposed to the security/dev teams. These personal drives may be susceptible to security issues arising from misconfiguration, user mistakes or insufficient knowledge.

How Sentra’s DSPM Helps Navigate Data Movement Challenges

  1. Discover and accurately classify the most sensitive data and provide extensive context about it, for example:
  • Where it lives
  • Where it has been copied or moved to
  • Who has access to it
  1. Highlight misconfigurations by correlating similar data that has different security posture. This helps you pinpoint the issue and adjust it according to the right posture.
  2. Quickly identify compliance violations, such as GDPR - when European customer data moves outside of the allowed region, or when financial data moves outside a PCI compliant environment.
  3. Identify access changes, which helps you to understand the correct access profile by correlating similar data pieces that have different access profiles.

For example, the same data is well kept in a specific environment and can be accessed by 2 very specific users. When the same data moves to a developers environment, it can then be accessed by the whole data engineering team, which exposes more risks.

Leveraging Data Security Posture Management (DSPM) and Data Detection and Response (DDR) tools proves instrumental in addressing the complexities of data movement challenges. These tools play a crucial role in monitoring the flow of sensitive data, allowing for the swift remediation of exposure incidents and vulnerabilities in real-time. The intricacies of data movement, especially in hybrid and multi-cloud deployments, can be challenging, as public cloud providers often lack sufficient tooling to comprehend data flows across various services and unmanaged databases. 

Our innovative cloud DLP tooling takes the lead in this scenario, offering a unified approach by integrating static and dynamic monitoring through DSPM and DDR. This integration provides a comprehensive view of sensitive data within your cloud account, offering an updated inventory and mapping of data flows. Our agentless solution automatically detects new sensitive records, classifies them, and identifies relevant policies. In case of a policy violation, it promptly alerts your security team in real time, safeguarding your crucial data assets.

In addition to our robust data identification methods, we prioritize the implementation of access control measures. This involves establishing Role-based Access Control (RBAC) and Attribute-based Access Control (ABAC) policies, so that the right users have permissions at the right times.

Identifying data movement with Sentra

Identifying Data Movement With Sentra

Sentra has developed different methods to identify data movements and similarities based on the content of two assets. Our advanced capabilities allow us to pinpoint fully duplicated data, identify similar data, and even uncover instances of partially duplicated data that may have been copied or moved across different locations. 

Moreover, we recognize that changes in access often accompany the relocation of assets between different locations. 

As part of Sentra’s Data Security Posture Management (DSPM) solution, we proactively manage and adapt access controls to accommodate these transitions, maintaining the integrity and security of the data throughout its lifecycle.

These are the 3 methods we are leveraging:

  1. Hash similarity - Using each asset unique identifier to locate it across the different data stores of the customer environment.
  2. Schema similarity - Locate the exact or similar schemas that indicated that there might be similar data in them and then leverage other metadata and statistical methods to simplify the data and find necessary correlations.
  3. Entity Matching similarity - Detects when parts of files or tables are copied to another data asset. For example, an ETL that extracts only some columns from a table into a new table in a data warehouse. 

Another example would be if PII is found in a lower environment, Sentra could detect if this is real or mock customer PII, based on whether this PII was also found in the production environment.

PII found in a lower environment

Conclusion

Understanding and managing data sprawl are critical tasks in the dynamic business landscape. Monitoring data movement, access, and posture enable teams to comprehend the full impact of data sprawl, connecting the dots for security analysts in assessing true risks and threats. 

Sentra addresses the challenge of data movement by utilizing advanced methods like hash, schema, and entity similarity to identify duplicate or similar data across different locations. Sentra's holistic Data Security Posture Management (DSPM) solution not only enhances data security but also contributes to a streamlined data management strategy. 

The identified challenges and Sentra's robust methods emphasize the importance of proactive data management and security in the dynamic digital landscape.

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

Ran is a passionate product and customer success leader with over 12 years of experience in the cybersecurity sector. He combines extensive technical knowledge with a strong passion for product innovation, research and development (R&D), and customer success to deliver robust, user-centric security solutions. His leadership journey is marked by proven managerial skills, having spearheaded multidisciplinary teams towards achieving groundbreaking innovations and fostering a culture of excellence. He started at Sentra as a senior product manager, and is currently Sentra's senior technical account manager in NYC.

Subscribe

Latest Blog Posts

Yoav Regev
Yoav Regev
April 23, 2025
3
Min Read
Data Security

Your AI Is Only as Secure as Your Data: Celebrating a $100M Milestone

Your AI Is Only as Secure as Your Data: Celebrating a $100M Milestone

Over the past year, we’ve seen an incredible surge in enterprise AI adoption. Companies across industries are integrating AI agents and generative AI into their operations to move faster, work smarter, and unlock innovation. But behind every AI breakthrough lies a foundational truth: AI is only as secure as the data behind it.

At Sentra, securing that data has always been our mission, not just to prevent breaches and data leaks, but to empower prosperity and innovation with confidence and control.

Data Security: The Heartbeat of Your Organization

As organizations push forward with AI, massive volumes of data, often sensitive, regulated, or business-critical are being used to train models or power AI agents. Too often, this happens without full visibility or governance. 


The explosion of the data security market reflects how critical this challenge has become. At Sentra, we’ve long believed that a Data Security Platform (DSP) must be cloud-native, scalable, and adaptable to real-world enterprise environments. We’ve been proud to lead the way, and our continued growth, especially among Fortune 500 customers, is a testament to the urgency and relevance of our approach.

Scaling for What's Next

With the announcement of our $50 million Series B funding round, bringing our total funding to over $100 million, we’re scaling Sentra to meet the moment. We're counting on strong customer momentum and more than tripling revenue year-over-year, and we’re using this investment to grow our team, strengthen our platform, and continue defining what modern data security looks like.

We’ve always said security shouldn’t slow innovation - it should fuel it. And that’s exactly what we’re enabling.

It's All About the People


At the end of the day, it’s people who build it, scale it, and believe in it. I want to extend a heartfelt thank you to our investors, customers, and, most importantly, our team. It’s all about you! Your belief in Sentra and your relentless execution make everything possible. We couldn’t make it without each and every one of you.

We’re not just building a product, we’re setting the gold standard for data security, because securing your data is the heartbeat of your organization!

Innovation without security isn’t progress. Let’s shape a future where both go together!

Read More
Meni Besso
Meni Besso
April 21, 2025
Min Read
Compliance

How to Scale DSAR Compliance (Without Breaking Your Team)

How to Scale DSAR Compliance (Without Breaking Your Team)

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.

Read More
David Stuart
David Stuart
April 3, 2025
3
Min Read
Data Security

The Rise of Next-Generation DSPs

The Rise of Next-Generation DSPs

Recently there has been a significant shift from standalone Data Security Posture Management (DSPM) solutions to comprehensive Data Security Platforms (DSPs). These platforms integrate DSPM functionality, but also encompass access governance, threat detection, and data loss prevention capabilities to provide a more holistic data protection solution. Additionally, the critical role of data in AI and LLM training requires holistic data security platforms that can manage data sensitivity, ensure security and compliance, and maintain data integrity.

This consolidation will improve security effectiveness and help organizations manage the growing complexity of their IT environments. Originally more of a governance/compliance tool, DSPs have evolved into a critical necessity for organizations managing sensitive data in sprawling cloud environments. With the explosion of cloud adoption, stricter regulatory landscapes, and the increasing sophistication of cyber threats, DSPs will continue to evolve to address the monumental data scale expected.

DSP Addressing Modern Challenges in 2025

As the threat landscape evolves, DSP is shifting to address modern challenges. New trends such as AI integration, real-time threat detection, and cloud-native architectures are transforming how organizations approach data security. DSPM is no longer just about assuring compliance and proper data governance, it’s about mitigating all data risks, monitoring for new threats, and proactively resolving them in real time.

Must-Have DSP Features for 2025

Over the years, Data Security Platforms (DSPs) have evolved significantly, with a range of providers emerging to address the growing need for robust data security in cloud environments. Initially, smaller startups began offering innovative solutions, and in 2024, several of these providers were acquired, signaling the increasing demand for comprehensive data protection. As organizations continue to prioritize securing their cloud data, it's essential to carefully evaluate DSP solutions to ensure they meet key security needs. When assessing DSP options for 2025, certain features stand out as critical for ensuring a comprehensive and effective approach to data security.

Below are outlined the must-have features for any DSP solution in the coming year:

  1. Cloud-Native Architecture

Modern DSPs are built for the cloud and address vast data scale with cloud-native technologies that leverage provider APIs and functions. This allows data discovery and classification to occur autonomously, within the customer cloud environment leveraging existing compute resources. Agentless approaches reduce administrative burdens as well.

  1. AI-Based Classification

AI has revolutionized data classification, providing context-aware accuracy exceeding 95%. By understanding data in its unique context, AI-driven DSP solutions ensure the right security measures are applied without overburdening teams with false positives.

  1. Anomaly Detection and Real-Time Threat Detection

Anomaly detection, powered by Data Detection and Response (DDR), identifies unusual patterns in data usage to spotlight risks such as ransomware and insider threats. Combined with real-time, data-aware detection of suspicious activities, modern DSP solutions proactively address cloud-native vulnerabilities, stopping breaches before they unfold and ensuring swift, effective action.

  1. Automatic Labeling

Manual tagging is too cumbersome and time consuming. When choosing DSP solutions, it’s critical to make sure that you choose ones that automate data tagging and labeling, seamlessly integrating with Data Loss Prevention (DLP), Secure Access Service Edge (SASE), and governance platforms. This reduces errors and accelerates compliance processes.

  1. Data Zones and Perimeters

As data moves across cloud environments, maintaining control is paramount. Leading DSP solutions monitor data movement, alerting teams when data crosses predefined perimeters or storage zones, ensuring compliance with internal and external policies.

  1. Automatic Remediation and Enforcement

Automation extends to remediation, with DSPs swiftly addressing data risks like excessive permissions or misconfigurations. By enforcing protection policies across cloud environments, organizations can prevent breaches before they occur.

The Business Case for DSP in 2025

Proactive Security

Cloud-native DSP represents a shift from reactive to proactive security practices. By identifying and addressing risks early, and across their entire data estate from cloud to on-premises, organizations can mitigate potential threats and strengthen their security posture.

Regulatory Compliance

As regulations such as GDPR and CCPA continue to evolve, DSPM solutions play a crucial role in simplifying compliance by automating data discovery and labeling. This automation reduces the manual effort required to meet regulatory requirements. In fact, 84% of security and IT professionals consider data protection frameworks like GDPR and CCPA to be mandatory for their industries, emphasizing the growing need for automated solutions to ensure compliance.

The Rise of Gen AI

The rise of Gen AI is expected to be a main theme in 2025. Gen AI is a driver for data proliferation in the cloud and for a transition between legacy data technologies and modern ones that require an updated data security program.

Operational Efficiency

By automating repetitive tasks, DSPM significantly reduces the workload for security teams. This efficiency allows teams to focus on strategic initiatives rather than firefighting. According to a 2024 survey, organizations using DSPM reported a 40% reduction in time spent on manual data management tasks, demonstrating its impact on operational productivity.

Future-Proofing Your Organization with Cloud-Native DSP

To thrive in the evolving security landscape, organizations must adopt forward-looking strategies. Cloud-native DSP tools integrate seamlessly with broader security frameworks, ensuring resilience and adaptability. As technology advances, features like predictive analytics and deeper AI integration will further enhance capabilities.

Conclusion

Data security challenges are only becoming more complex, but new Data Security Platforms (DSPs) provide the tools to meet them head-on. Now is the time for organizations to take a hard look at their security posture and consider how DSPs can help them stay protected, compliant, and trusted. DSPs are quickly becoming essential to business operations, influencing strategic decisions and enabling faster, more secure innovation.

Ready to see it in action?

Request a demo to discover how a modern DSP can strengthen your security and support your goals.

Read More
decorative ball