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How to Prevent Data Breaches in Healthcare and Protect PHI

January 9, 2026
3
Min Read
Data Security

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

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

From Perimeter Security to Data-Centric PHI Protection

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

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

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

The Importance of Data Security in Healthcare

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

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

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

Healthcare Cybersecurity Regulations & Standards

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

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


Health Insurance Portability and Accountability Act of 1996 (HIPAA)

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

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

HHS 405(d)

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

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

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

Payment Card Industry Data Security Standard (PCI DSS)

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

Quality System Regulation (QSR)

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

Health Information Trust Alliance (HITRUST)

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

Preventing Data Breaches in Healthcare with Sentra

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

Discover and Accurately Classify your High Risk Patient Data

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

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

Sentra Reduces Data Risks by Controlling Posture and Access

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

Stay on Top of Healthcare Data Regulations with Sentra

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

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

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

Conclusion: From Perimeter Security to Continuous Data Governance

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

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

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

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

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Yair brings a wealth of experience in cybersecurity and data product management. In his previous role, Yair led product management at Microsoft and Datadog. With a background as a member of the IDF's Unit 8200 for five years, he possesses over 18 years of expertise in enterprise software, security, data, and cloud computing. Yair has held senior product management positions at Datadog, Digital Asset, and Microsoft Azure Protection.

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David Stuart
David Stuart
January 28, 2026
3
Min Read

Data Privacy Day: Why Discovery Isn’t Enough

Data Privacy Day: Why Discovery Isn’t Enough

Data Privacy Day is a good reminder for all of us in the tech world: finding sensitive data is only the first step. But in today’s environment, data is constantly moving -across cloud platforms, SaaS applications, and AI workflows. The challenge isn’t just knowing where your sensitive data lives; it’s also understanding who or what can touch it, whether that access is still appropriate, and how it changes as systems evolve.

I’ve seen firsthand that privacy breaks down not because organizations don’t care, but because access decisions are often disconnected from how data is actually being used. You can have the best policies on paper, but if they aren’t continuously enforced, they quickly become irrelevant.

Discovery is Just the Beginning

Most organizations start with data discovery. They run scans, identify sensitive files, and map out where data lives. That’s an important first step, and it’s necessary, but it’s far from sufficient. Data is not static. It moves, it gets copied, it’s accessed by humans and machines alike. Without continuously governing that access, all the discovery work in the world won’t stop privacy incidents from happening.

The next step, and the one that matters most today, is real-time governance. That means understanding and controlling access as it happens. 

Who can touch this data? Why do they have access? Is it still needed? And crucially, how do these permissions evolve as your environment changes?

Take, for example, a contractor who needs temporary access to sensitive customer data. Or an AI workflow that processes internal HR information. If those access rights aren’t continuously reviewed and enforced, a small oversight can quickly become a significant privacy risk.

Privacy in an AI and Automation Era

AI and automation are changing the way we work with data, but they also change the privacy equation. Automated processes can move and use data in ways that are difficult to monitor manually. AI models can generate insights using sensitive information without us even realizing it. This isn’t a hypothetical scenario, it’s happening right now in organizations of all sizes.

That’s why privacy cannot be treated as a once-a-year exercise or a checkbox in an audit report. It has to be embedded into daily operations, into the way data is accessed, used, and monitored. Organizations that get this right build systems that automatically enforce policies and flag unusual access - before it becomes a problem.

Beyond Compliance: Continuous Responsibility

The companies that succeed in protecting sensitive data are those that treat privacy as a continuous responsibility, not a regulatory obligation. They don’t wait for audits or compliance reviews to take action. Instead, they embed privacy into how data is accessed, shared, and used across the organization.

This approach delivers real results. It reduces risk by catching misconfigurations before they escalate. It allows teams to work confidently with data, knowing that sensitive information is protected. And it builds trust - both internally and with customers because people know their data is being handled responsibly.

A New Mindset for Data Privacy Day

So this Data Privacy Day, I challenge organizations to think differently. The question is no longer “Do we know where our sensitive data is?” Instead, ask:

“Are we actively governing who can touch our data, every moment, everywhere it goes?”

In a world where cloud platforms, AI systems, and automated workflows touch nearly every piece of data, privacy isn’t a one-time project. It’s a continuous practice, a mindset, and a responsibility that needs to be enforced in real time.

Organizations that adopt this mindset don’t just meet compliance requirements, they gain a competitive advantage. They earn trust, strengthen security, and maintain a dynamic posture that adapts as systems and access needs evolve.

Because at the end of the day, true privacy isn’t something you achieve once a year. It’s something you maintain every day, in every process, with every decision. This Data Privacy Day, let’s commit to moving beyond discovery and audits, and make continuous data privacy the standard.

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David Stuart
David Stuart
January 27, 2026
4
Min Read

DSPM for Modern Fintech: From Masking to AI-Aware Data Protection

DSPM for Modern Fintech: From Masking to AI-Aware Data Protection

Fintech leaders, from digital-first banks to API-driven investment platforms, face a major data dilemma today. With cloud-native architectures, real-time analytics, and the rapid integration of AI, the scale, speed, and complexity of sensitive data have skyrocketed. Fintech platforms are quickly surpassing what legacy Data Loss Prevention (DLP) and Data Security Posture Management (DSPM) tools can handle.

Why? Fintech companies now need more than surface-level safeguards. They require true depth: AI-driven data classification, dynamic masking, and fluid integrations across a massive tech stack that includes Snowflake, AWS Bedrock, and Microsoft 365. Below, we look at why DSPM in financial services is at a defining moment, what recurring pain points exist with traditional, and even many emerging, tools, and how Sentra is reimagining what the modern data protection stack should deliver.

The Pitfalls of Legacy DLP and Early DSPM in Fintech

Legacy DLP wasn’t built for fintech’s speed or expanding data footprint. These tools focus on rigid rules and tight boundaries, which aren’t equipped to handle petabyte-scale, multi-cloud, or AI-powered environments. Early DSPM tools brought some improvements in visibility, but problems persisted: incomplete data discovery, basic classification, lots of manual steps, and limited support for dynamic masking.

For fintech companies, this creates mounting regulatory risk as compliance pressures rise, and slow, manual processes lead to both security and operational headaches. Teams waste hours juggling alerts and trying to piece together patchwork fixes, often resorting to clunky add-on masking tools. The cost is obvious: a scattered protection strategy, long breach response times, and constant exposure to regulatory issues - especially as environments get more distributed and complex.

Why "Good Enough" DSPM Isn’t Enough Anymore

Change in fintech moves faster than ever. The DSPM for the financial services sector is growing at breakneck speed. But as financial applications get more sophisticated, and with cloud and AI adoption soaring, the old "good enough" DSPM falls short. Sensitive data is everywhere now. 82% percent of breaches happen in the cloud, with 39% stretching across multi-cloud or hybrid setups according to The Future of Data Security: Why DSPM is Here to Stay. Enterprise data is set to exceed 181 zettabytes by 2025, raising the stakes for automation, real-time classification, and tight integration with core infrastructure.

AI and automation are no longer optional. To effectively reduce risk and keep compliance manageable and truly auditable, DSPM systems need to automate classification, masking, remediation, and reporting as a central part of operations, not as last-minute additions.

Where Most DSPM Solutions Fall Short

Fintech organizations often struggle to scale legacy or early DSPM and DLP products, especially those similar to emerging DSPM or large CNAPP vendors. These tools might offer broad control and AI-powered classification, but they usually require too much manual orchestration to achieve full remediation, only automate certain pieces of the workflow, and rely on separate masking add-ons.

That leads to gaps in AI and multi-cloud data context, choppy visibility, and much of the workflow stuck in manual gear, a recipe for persistent exposure of sensitive data, especially in fast-moving fintech environments.

Fintech buyers, especially those scaling quickly, also point to a crucial need: ensuring DSPM tools natively and deeply support platforms like Snowflake, AWS Bedrock, and Macie. They want automated, business-driven policy enforcement without constantly babysitting the system.

Sentra’s Next-Gen DSPM: AI-Native, Masking-Aware, and Stack-Integrated for Fintech

Sentra was created with these modern fintech challenges in mind. It offers real-time, continuous, agentless classification and deep context for cloud, SaaS, and AI-powered environments.

What makes Sentra different?

  • Petabyte-scale agentless discovery: Always-on, friction-free classification, with no heavy infrastructure or manual tweaks.
  • AI-native contextualization: Pinpoints sensitive data at a business level and connects instantly with masking policies across Snowflake, Microsoft Purview, and more inferred masking synergy.
  • Automation-driven compliance: Handles everything from discovery to masking to changing permissions, with clear, auditable reporting automated masking/remediation.
  • Integrated for modern stacks: Ready-made, with out-of-the-box connections for Snowflake, Bedrock, Microsoft 365, and the wider AWS/fintech ecosystem.

More and more fintech companies are switching to Sentra DSPM to achieve true cross-cloud visibility and meet regulations without slowing down. By plugging into fintech data flows and covering AI model pipelines, Sentra lets organizations use DSPM with the same speed as their business.

Building a Future-Ready DSPM Strategy in Financial Services

Managing and protecting sensitive data is a competitive edge for fintech, not just a security concern. With compliance rising up the agenda - 84% of IT and security leaders now list it as a top driver - your DSPM investments need to focus on automation, consistent visibility, and enforceable policies throughout your architecture.

Next-gen DSPM means: less busywork, no more juggling between masking and classification tools, and instant, actionable insight into data risk, wherever your information lives. In other words, you spend less time firefighting, move faster, and can assure partners and customers that their data is in good hands.

See How SoFi

Request a demo and technical assessment to discover how Sentra’s AI-aware DSPM can speed up both your compliance and your innovation.

Conclusion

Legacy data protection simply can’t keep up with the size, complexity, and regulatory demands of financial data today. DSPM is now table stakes - as long as it’s automated, built with AI at its core, and actively reduces risk in real time, not just points it out.

Sentra helps you move forward confidently: always-on, agentless classification, automated fixes and masking, and deep stack integration designed for the most complex fintech systems. As you build the future of financial services, your DSPM should make it easier to stay compliant, agile, and protected - no matter how quickly your technology changes.

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Romi Minin
Romi Minin
Nikki Ralston
Nikki Ralston
January 26, 2026
4
Min Read

How to Choose a Data Access Governance Tool

How to Choose a Data Access Governance Tool

Introduction: Why Data Access Governance Is Harder Than It Should Be

Data access governance should be simple: know where your sensitive data lives, understand who has access to it, and reduce risk without breaking business workflows. In practice, it’s rarely that straightforward. Modern organizations operate across cloud data stores, SaaS applications, AI pipelines, and hybrid environments. Data moves constantly, permissions accumulate over time, and visibility quickly degrades. Many teams turn to data access governance tools expecting clarity, only to find legacy platforms that are difficult to deploy, noisy, or poorly suited for dynamic, fast-proliferating cloud environments.

A modern data access governance tool should provide continuous visibility into who and what can access sensitive data across cloud and SaaS environments, and help teams reduce overexposure safely and incrementally.

What Organizations Actually Need from Data Access Governance

Before evaluating vendors, it’s important to align on outcomes, just not features. Most teams are trying to solve the same core problems:

  • Unified visibility across cloud data stores, SaaS platforms, and hybrid environments
  • Clear answers to “which identities have access to what, and why?”
  • Risk-based prioritization instead of long, unmanageable lists of permissions
  • Safe remediation that tightens access without disrupting workflows

Tools that focus only on periodic access reviews or static policies often fall short in dynamic environments where data and permissions change constantly.

Why Legacy and Over-Engineered Tools Fall Short

Many traditional data governance and IGA tools were designed for on-prem environments and slower change cycles. In cloud and SaaS environments, these tools often struggle with:

  • Long deployment timelines and heavy professional services requirements
  • Excessive alert noise without clear guidance on what to fix first
  • Manual access certifications that don’t scale
  • Limited visibility into modern SaaS and cloud-native data stores

Overly complex platforms can leave teams spending more time managing the tool than reducing actual data risk.

Key Capabilities to Look for in a Modern Data Access Governance Tool

1. Continuous Data Discovery and Classification

A strong foundation starts with knowing where sensitive data lives. Modern tools should continuously discover and classify data across cloud, SaaS, and hybrid environments using automated techniques, not one-time scans.

2. Access Mapping and Exposure Analysis

Understanding data sensitivity alone isn’t enough. Tools should map access across users, roles, applications, and service accounts to show how sensitive data is actually exposed.

3. Risk-Based Prioritization

Not all exposure is equal. Effective platforms correlate data sensitivity with access scope and usage patterns to surface the highest-risk scenarios first, helping teams focus remediation where it matters most.

4. Low-Friction Deployment

Look for platforms that minimize operational overhead:

  • Agentless or lightweight deployment models
  • Fast time-to-value
  • Minimal disruption to existing workflows

5. Actionable Remediation Workflows

Visibility without action creates frustration. The right tool should support guided remediation, tightening access incrementally and safely rather than enforcing broad, disruptive changes.

How Teams Are Solving This Today

Security teams that succeed tend to adopt platforms that combine data discovery, access analysis, and real-time risk detection in a single workflow rather than stitching together multiple legacy tools. For example, platforms like Sentra focus on correlating data sensitivity with who or what can actually access it, making it easier to identify over-permissioned data, toxic access combinations, and risky data flows, without breaking existing workflows or requiring intrusive agents.

The common thread isn’t the tool itself, but the ability to answer one question continuously:

“Who can access our most sensitive data right now, and should they?”

Teams using these approaches often see faster time-to-value and more actionable insights compared to legacy systems.

Common Gotchas to Watch Out For

When evaluating tools, buyers often overlook a few critical issues:

  • Hidden costs for deployment, tuning, or ongoing services
  • Tools that surface risk but don’t help remediate it
  • Point-in-time scans that miss rapidly changing environments
  • Weak integration with identity systems, cloud platforms, and SaaS apps

Asking vendors how they handle these scenarios during a pilot can prevent surprises later.
Download The Dirt on DSPM POVs: What Vendors Don’t Want You to Know

How to Run a Successful Pilot

A focused pilot is the best way to evaluate real-world effectiveness:

  1. Start with one or two high-risk data stores
  2. Measure signal-to-noise, not alert volume
  3. Validate that remediation steps work with real teams and workflows
  4. Assess how quickly the tool delivers actionable insights

The goal is to prove reduced risk, not just improved reporting.

Final Takeaway: Visibility First, Enforcement Second

Effective data access governance starts with visibility. Organizations that succeed focus first on understanding where sensitive data lives and how it’s exposed, then apply controls gradually and intelligently. Combining DAG with DSPM is an effective way to achieve this.

In 2026, the most effective data access governance tools are continuous, risk-driven, and cloud-native, helping security teams reduce exposure without slowing the business down.

Frequently Asked Questions (FAQs)

What is data access governance?

Data access governance is the practice of managing and monitoring who can access sensitive data, ensuring access aligns with business needs and security requirements.

How is data access governance different from IAM?

IAM focuses on identities and permissions. Data access governance connects those permissions to actual data sensitivity and exposure, and alerts when violations occur.

How do organizations reduce over-permissioned access safely?

By using risk-based prioritization and incremental remediation instead of broad access revocations.

What should teams look for in a modern data access governance tool?

This question comes up frequently in real-world evaluations, including Reddit discussions where teams share what’s worked and what hasn’t. Teams should prioritize tools that give fast visibility into who can access sensitive data, provide context-aware insights, and allow incremental, safe remediation - all without breaking workflows or adding heavy operational overhead. Cloud- and SaaS-aware platforms tend to outperform legacy or overly complex solutions.

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