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It's Time to Embrace Cloud DLP and DSPM

March 11, 2024
4
Min Read
Data Loss Prevention

What’s the best way to prevent data exfiltration or exposure? In years past, the clear answer was often data loss prevention (DLP) tools. But today, the answer isn’t so clear — especially in light of the data democratization trend and for those who have adopted multi-cloud or cloud-first strategies.

 

Data loss prevention (DLP) emerged in the early 2000s as a way to secure web traffic, which wasn’t encrypted at the time. Without encryption, anyone could tap into data in transit, creating risk for any data that left the safety of on-premise storage. As Cyber Security Review describes, “The main approach for DLP here was to ensure that any sensitive data or intellectual property never saw the outside web. The main techniques included (1) blocking any actions that copy or move data to unauthorized devices and (2) monitoring network traffic with basic keyword matching.”

Although DLP has evolved for securing endpoints, email and more, its core functionality has remained the same: gatekeeping data within a set perimeter. But, this approach simply doesn’t perform well in cloud environments, as the cloud doesn’t have a clear perimeter. Instead, today’s multi-cloud environment includes constantly changing data stores, infrastructure-as-a-service (IaaS), platform-as-a-service (PaaS) and more.

And thanks to data democratization, people across an organization can access all of these areas and move, change, or copy data within seconds. Cloud applications do so as well—even faster.

Traditional DLP tools weren’t built for cloud-native environments and can cause significant challenges for today’s organizations. Data security teams need a new approach, purpose-built for the realities of the cloud, digital transformation and today’s accelerated pace of innovation.

Why Traditional DLP Isn’t Ideal for the Cloud

Traditional DLPs are often unwieldy for the engineers who must work with the solution and ineffective for the leaders who want to see positive results and business continuity from the tool. There are a few reasons why this is the case:

1. Traditional DLP tools often trigger false alarms.

Traditional DLPs are prone to false positives. Because they are meant to detect any sensitive data that leaves a set perimeter, these solutions tend to flag normal cloud activities as security risks. For instance, traditional DLP is notorious for erroneously blocking apps and services in IaaS/PaaS environments. These “false positives” disrupt business continuity and innovation, which is frustrating for users who want to use valuable cloud data in their daily work. Not only do traditional DLPs block the wrong signals, but they also overlook the right ones, such as suspicious activities happening over cloud-based applications like Slack, Google Drive or generative AI/LLM apps. Plus, traditional DLP doesn’t follow data as users move, change or copy it, meaning it can easily miss shadow data.

2. Traditional DLP tools cause alert fatigue.

In addition, these tools lack detailed data context, meaning that they can’t triage alerts based on severity. Combine this factor with the high number of false positives, and teams end up with an overwhelming list of alerts that they must sort manually. This reality leads to alert fatigue and can cause teams to overlook legitimate security issues.

3. Traditional DLP tools rely on lots of manual intervention.

Traditional DLP deployment and maintenance take up lots of time and resources for a cloud-based or hybrid organization. For instance, teams must often install several legacy agents and proxies across the environment to make the solution work accurately. Plus, these legacy tools rely on clear-cut data patterns and keywords to uncover risk. These patterns are often hidden or nonexistent because they are often disguised or transformed in the data that exists in or moves to cloud environments. This means that teams must manually tune their DLP solution to align with what their sensitive cloud data actually looks like. In many cases, this manual intervention is very difficult—if not impossible—since many cloud pipelines rely on ETL data, which isn’t easy to manually alter or inspect. 

Additionally, today’s organizations use vast amounts of unstructured data within cloud file shares such as Sharepoint. They must parse through tens or even hundreds of petabytes of this unstructured data, making it challenging to find hidden sensitive data. Traditional DLP solutions lack the technology that would make this process far easier, such as AI/ML analysis.

Cloud DLP: A Cloud-Native Approach to Data Loss Prevention

Because the cloud is so different from traditional, on-premise environments, today’s cloud-based and hybrid organizations need a new solution. This is where a cloud DLP solution comes into the picture. We are seeing lots of cloud DLP tools hit the market, including solutions that fall into two main categories:

SaaS DLP products that leverage APIs to provide access control. While these products help to protect from loss within some SaaS applications, they are limited in scope, only covering a small percentage of the cloud services that a typical cloud-native organization uses. These limitations mean that a SaaS DLP product can’t provide a truly comprehensive view of all cloud data or trace data lineage if it’s not based in the cloud. 

IaaS + PaaS DLP products that focus on scanning and classifying data. Some of these tools are simply reporting tools that uncover data but don’t take action to remediate any issues. This still leaves extra manual work for security teams. Other IaaS + PaaS DLP offerings include automated remediation capabilities but can cause business interruptions if the automation occurs in the wrong situation.  

To directly address the limitations inherent in traditional DLPs and avoid these pitfalls, next-generation cloud DLPs should include the following:

  • Scalability in complex, multi-cloud environments
  • Automated prioritization for detected risks based on rich data context
  • Auto-detection and remediation capabilities that use deep context to correct configuration issues, creating efficiency without blocking everyday activities
  • Integration and workflows that are compatible with your existing environments
  • Straightforward, cloud-native agentless deployment without extensive tuning or maintenance


Attribute Cloud DLP DSPM DDR
Security Use Case Data Leakage Prevention Data Posture Improvement, Compliance Threat Detection and Response
Environments SaaS, Cloud Storage, Apps Public Cloud, SaaS and OnPremises Public Cloud, SaaS, Networks
Risk Prioritization Limited: based only on predefined policies - not based on discovered data or data context Analyzes Data Context, Access Controls, and Vulnerabilities Threat Activity Context such as anomalous traffic, volume, access
Remediation Block or Redact Data Transfers, Encryption, Alert Alerts, IR/Tool Integration & Workflow Initiation Alerts, Revoke Users/Access, Isolate Data Breach

Further Enhancing Cloud DLP by Integrating DSPM & DDR

While Cloud Data Loss Prevention (DLP) helps to secure data in multi-cloud environments by preventing loss, DSPM and DDR capabilities can complete the picture. These technologies add contextual details, such as user behavior, risk scoring and real-time activity monitoring, to enhance the accuracy and actionability of data threat and loss mitigation. Data Security Posture Management (DSPM) enforces good data hygiene no matter where the data resides. It takes a proactive approach, significantly reducing data exposure by preventing employees from taking risky actions in the first place. Data Detection and Response (DDR) alerts teams to the early warning signs of a breach, including suspicious activities such as data access by an unknown IP address. By bringing together Cloud DLP, DSPM and DDR, your organization can establish holistic data protection with both proactive and reactive controls. There is already much overlap in these technologies. As the market evolves, it is likely they will continue to combine into holistic cloud-native data security platforms.  


Sentra’s data security platform brings a cloud-native approach to DLP by automatically detecting and remediating data risks at scale. Built for complex multi-cloud and premise environments, Sentra empowers you with a unified platform to prioritize all of your most critical data risks in near real-time.

Request a demo to learn more about our cloud DLP, DSPM and DDR offerings.

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David Stuart is Senior Director of Product Marketing for Sentra, a leading cloud-native data security platform provider, where he is responsible for product and launch planning, content creation, and analyst relations. Dave is a 20+ year security industry veteran having held product and marketing management positions at industry luminary companies such as Symantec, Sourcefire, Cisco, Tenable, and ZeroFox. Dave holds a BSEE/CS from University of Illinois, and an MBA from Northwestern Kellogg Graduate School of Management.

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Ward Balcerzak
Ward Balcerzak
January 14, 2026
4
Min Read

The Real Business Value of DSPM: Why True ROI Goes Beyond Cost Savings

The Real Business Value of DSPM: Why True ROI Goes Beyond Cost Savings

As enterprises scale cloud usage and adopt AI, the value of Data Security Posture Management (DSPM) is no longer just about checking a tool category box. It’s about protecting what matters most: sensitive data that fuels modern business and AI workflows.

Traditional content on DSPM often focuses on cost components and deployment considerations. That’s useful, but incomplete. To truly justify DSPM to executives and boards, security leaders need a holistic, outcome-focused view that ties data risk reduction to measurable business impact.

In this blog, we unpack the real, measurable benefits of DSPM, beyond just cost savings, and explain how modern DSPM strategies deliver rapid value far beyond what most legacy tools promise. 

1. Visibility Isn’t Enough - You Need Context

A common theme in DSPM discussions is that tools help you see where sensitive data lives. That’s important, but it’s only the first step. Real value comes from understanding context. Who can access the data, how it’s being used, and where risk exists in the wider security posture. Organizations that stop at discovery often struggle to prioritize risk and justify spend.

Modern DSPM solutions go further by:

  • Correlating data locations with access rights and usage patterns
  • Mapping sensitive data flows across cloud, SaaS, and hybrid environments
  • Detecting shadow data stores and unmanaged copies that silently increase exposure
  • Linking findings to business risk and compliance frameworks

This contextual intelligence drives better decisions and higher ROI because teams aren’t just counting sensitive data, they’re continuously governing it.

2. DSPM Saves Time and Shrinks Attack Surface Fast

One way DSPM delivers measurable business value is by streamlining functions that used to be manual, siloed, and slow:

  • Automated classification reduces manual tagging and human error
  • Continuous discovery eliminates periodic, snapshot-alone inventories
  • Policy enforcement reduces time spent reacting to audit requests

This translates into:

  • Faster compliance reporting
  • Shorter audit cycles
  • Rapid identification and remediation of critical risks

For security leaders, the speed of insight becomes a competitive advantage, especially in environments where data volumes grow daily and AI models can touch every corner of the enterprise.

3. Cost Benefits That Matter, but with Context

Lately I’m hearing many DSPM discussions break down cost components like scanning compute, licensing, operational expenses, and potential cloud savings. That’s a good start because DSPM can reduce cloud waste by identifying stale or redundant data, but it’s not the whole story.

 

Here’s where truly strategic DSPM differs:

Operational Efficiency

When DSPM tools automate discovery, classification, and risk scoring:

  • Teams spend less time on manual reports
  • Alert fatigue drops as noise is filtered
  • Engineers can focus on higher-value work

Breach Avoidance

Data breaches are expensive. According to industry studies, the average cost of a data breach runs into millions, far outweighing the cost of DSPM itself. A DSPM solution that prevents even one breach or major compliance failure pays for itself tenfold

Compliance as a Value Center

Rather than treating compliance as a cost center consider that:

  • DSPM reduces audit overhead
  • Provides automated evidence for frameworks like GDPR, HIPAA, PCI DSS
  • Improves confidence in reporting accuracy

That’s a measurable business benefit CFOs can appreciate and boards expect.

4. DSPM Reduces Risk Vector Multipliers Like AI

One benefit that’s often under-emphasized is how DSPM reduces risk vector multipliers, the factors that amplify risk exponentially beyond simple exposure counts.

In 2026 and beyond, AI systems are increasingly part of the risk profile. Modern DSPM help reduce the heightened risk from AI by:

  • Identifying where sensitive data intersects with AI training or inference pipelines
  • Governing how AI tools and assistants can access sensitive content
  • Providing risk context so teams can prevent data leakage into LLMs

This kind of data-centric, contextual, and continuous governance should be considered a requirement for secure AI adoption, no compromise.

5. Telling the DSPM ROI Story

The most convincing DSPM ROI stories aren’t spreadsheets, they’re narratives that align with business outcomes. The key to building a credible ROI case is connecting metrics, security impact, and business outcomes:

Metric Security Impact Business Outcome
Faster discovery & classification Fewer blind spots Reduced breach likelihood
Consistent governance enforcement Fewer compliance issues Lower audit cost
Contextual risk scoring Better prioritization Efficient resource allocation
AI governance Controlled AI exposure Safe innovation

By telling the story this way, security leaders can speak in terms the board and executives care about: risk reduction, compliance assurance, operational alignment, and controlled growth.

How to Evaluate DSPM for Real ROI

To capture tangible return, don’t evaluate DSPM solely on cost or feature checklists. Instead, test for:

1. Scalability Under Real Load

Can the tool discover and classify petabytes of data, including unstructured content, without degrading performance?

2. Accuracy That Holds Up

Poor classification undermines automation. True ROI requires consistent, top-performing accuracy rates.

3. Operational Cost Predictability

Beware of DSPM solutions that drive unexpected cloud expenses due to inefficient scanning or redundant data reads.

4. Integration With Enforcement Workflows

Visibility without action isn’t ROI. Your DSPM should feed DLP, IAM/CIEM, SIEM/SOAR, and compliance pipelines (ticketing, policy automation, alerts).

ROI Is a Journey, Not a Number

Costs matter, but value lives in context. DSPM is not just a cost center, it’s a force multiplier for secure cloud operations, AI readiness, compliance, and risk reduction. Instead of seeing DSPM as another tool, forward-looking teams view it as a fundamental decision support engine that changes how risk is measured, prioritized, and controlled.

Ready to See Real DSPM Value in Your Environment?

Download Sentra’s “DSPM Dirty Little Secrets” guide, a practical roadmap for evaluating DSPM with clarity, confidence, and production reality in mind.

👉 Download the DSPM Dirty Little Secrets guide now

Want a personalized walkthrough of how Sentra delivers measurable DSPM value?
👉 Request a demo

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Ofir Yehoshua
Ofir Yehoshua
January 13, 2026
3
Min Read

Why Infrastructure Security Is Not Enough to Protect Sensitive Data

Why Infrastructure Security Is Not Enough to Protect Sensitive Data

For years, security programs have focused on protecting infrastructure: networks, servers, endpoints, and applications. That approach made sense when systems were static and data rarely moved. It’s no longer enough.

Recent breach data shows a consistent pattern. Organizations detect incidents, restore systems, and close tickets, yet remain unable to answer the most important question regulators and customers often ask:

Where does my sensitive data reside?

Who or what has access to this data and are they authorized?

Which specific sensitive datasets were accessed or exfiltrated?

Infrastructure security alone cannot answer that question.

Infrastructure Alerts Detect Events, Not Impact

Most security tooling is infrastructure-centric by design. SIEMs, EDRs, NDRs, and CSPM tools monitor hosts, processes, IPs, and configurations. When something abnormal happens, they generate alerts.

What they do not tell you is:

  • Which specific datasets were accessed
  • Whether those datasets contained PHI or PII
  • Whether sensitive data was copied, moved, or exfiltrated

Traditional tools monitor the "plumbing" (network traffic, server logs, etc.) While they can flag that a database was accessed by an unauthorized IP, they often cannot distinguish between an attacker downloading a public template or downloading a table containing 50,000 Social Security numbers. An alert is not the same as understanding the exposure of the data stored inside it. Without that context, incident response teams are forced to infer impact rather than determine it.

The “Did They Access the Data?” Problem

This gap becomes pronounced during ransomware and extortion incidents.

In many cases:

  • Operations are restored from backups
  • Infrastructure is rebuilt
  • Access is reduced
  • (Hopefully!) attackers are removed from the environment

Yet organizations still cannot confirm whether sensitive data was accessed or exfiltrated during the dwell time.

Without data-level visibility:

  • Legal and compliance teams must assume worst-case exposure
  • Breach notifications expand unnecessarily
  • Regulatory penalties increase due to uncertainty, not necessarily damage

The inability to scope an incident accurately is not a tooling failure during the breach, it is a visibility failure that existed long before the breach occurred. Under regulations like GDPR or CCPA/CPRA, if an organization cannot prove that sensitive data wasn’t accessed during a breach, they are often legally required to notify all potentially affected parties. This ‘over-notification’ is costly and damaging to reputation.

Data Movement Is the Real Attack Vulnerability

Modern environments are defined by constant data movement:

  • Cloud migrations
  • SaaS integrations
  • App dev lifecycles
  • Analytics and ETL pipelines
  • AI and ML workflows

Each transition creates blind spots.

Legacy platforms awaiting migration often exist in a “wait state” with reduced monitoring. Data copied into cloud storage or fed into AI pipelines frequently loses lineage and classification context. Posture may vary and traditional controls no longer apply consistently. From an attacker’s perspective, these environments are ideal. From a defender’s perspective, they are blind spots.

Policies Are Not Proof

Most organizations can produce policies stating that sensitive data is encrypted, access-controlled, and monitored. Increasingly, regulators are moving from point-in-time audits to requiring continuous evidence of control.  

Regulators are asking for evidence:

  • Where does PHI live right now?
  • Who or what can access it?
  • How do you know this hasn’t changed since the last audit?

Point-in-time audits cannot answer those questions. Neither can static documentation. Exposure and access drift continuously, especially in cloud and AI-driven environments.

Compliance depends on continuous control, not periodic attestation.

What Data-Centric Security Actually Requires

Accurately proving compliance and scoping breach impact requires security visibility that is anchored to the data itself, not the infrastructure surrounding it.

At a minimum, this means:

  • Continuous discovery and classification of sensitive data
  • Consistent compliance reporting and controls across cloud, SaaS, On-Prem, and migration states
  • Clear visibility into which identities, services, and AI tools can access specific datasets
  • Detection and response signals tied directly to sensitive data exposure and movement

This is the operational foundation of Data Security Posture Management (DSPM) and Data Detection and Response (DDR). These capabilities do not replace infrastructure security controls; they close the gap those controls leave behind by connecting security events to actual data impact.

This is the problem space Sentra was built to address.

Sentra provides continuous visibility into where sensitive data lives, how it moves, and who or what can access it, and ties security and compliance outcomes to that visibility. Without this layer, organizations are forced to infer breach impact and compliance posture instead of proving it.

Why Data-Centric Security Is Required for Today's Compliance and Breach Response

Infrastructure security can detect that an incident occurred, but it cannot determine which sensitive data was accessed, copied, or exfiltrated. Without data-level evidence, organizations cannot accurately scope breaches, contain risk, or prove compliance, regardless of how many alerts or controls are in place. Modern breach response and regulatory compliance require continuous visibility into sensitive data, its lineage, and its access paths. Infrastructure-only security models are no longer sufficient.

Want to see how Sentra provides complete visibility and control of sensitive data?

Schedule a Demo

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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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