Gilad Golani

Scaling Automated Data Security in Cloud and AI Environments

Modern cloud and AI environments move faster than human response. By the time a manual workflow catches up, sensitive data may already be at risk. Organizations need automated remediation to reduce response time, enforce policy at scale, and safeguard sensitive data the moment it becomes exposed. Comprehensive data discovery and accurate data classification are foundational to this effort. Without knowing what data exists and how it's handled, automation can't succeed.

‍

Sentra’s cloud-native Data Security Platform (DSP) delivers precisely that. With built-in, context-aware automation, data discovery, and classification, Sentra empowers security teams to shift from reactive alerting to proactive defense. From discovery to remediation, every step is designed for precision, speed, and seamless integration into your existing security stack. precisely that. With built-in, context-aware automation, Sentra empowers security teams to shift from reactive alerting to proactive defense. From discovery to remediation, every step is designed for precision, speed, and seamless integration into your existing security stack.

Automated Remediation: Turning Data Risk Into Action

Sentra doesn't just detect risk, it acts. At the core of its value is its ability to execute automated remediation through native integrations and a powerful API-first architecture. This lets organizations immediately address data risks without waiting for manual intervention.

Key Use Cases for Automated Data Remediation

Sensitive Data Tagging & Classification Automation

‍

Sentra accurately classifies and tags sensitive data across environments like Microsoft 365, Amazon S3, Azure, and Google Cloud Platform. Its Automation Rules Page enables dynamic labels based on data type and context, empowering downstream tools to apply precise protections.

‍

‍Automated Access Revocation & Insider Risk Mitigation

‍

Sentra identifies excessive or inappropriate access and revokes it in real time. With integrations into IAM and CNAPP tools, it enforces least-privilege access. Advanced use cases include Just-In-Time (JIT) access via SOAR tools like Tines or Torq.

‍

Enforced Data Encryption & Masking Automation

‍

Sentra ensures sensitive data is encrypted and masked through integrations with Microsoft Purview, Snowflake DDM, and others. It can remediate misclassified or exposed data and apply the appropriate controls, reducing exposure and improving compliance.

‍

Integrated Remediation Workflow Automation

‍

Sentra streamlines incident response by triggering alerts and tickets in ServiceNow, Jira, and Splunk. Context-rich events accelerate triage and support policy-driven automated remediation workflows.

Architecture Built for Scalable Security Automation

Cloud & AI Data Visibility with Actionable Remediation

Sentra provides visibility across AWS, Azure, GCP, and M365 while minimizing data movement. It surfaces actionable guidance, such as missing logging or improper configurations, for immediate remediation.

Dynamic Policy Enforcement via Tagging

Sentra’s tagging flows directly into cloud-native services and DLP platforms, powering dynamic, context-aware policy enforcement.

API-First Architecture for Security Automation

With a REST API-first design, Sentra integrates seamlessly with security stacks and enables full customization of workflows, dashboards, and automation pipelines.

Why Sentra for Automated Remediation?

Sentra offers a unified platform for security teams that need visibility, precision, and automation at scale. Its advantages include:

‍

• No agents or connectors required
• High-accuracy data classification for confident automation
• Deep integration with leading security and IT platforms
• Context-rich tagging to drive intelligent enforcement
• Built-in data discovery that powers proactive policy decisions
• OpenAPI interface for tailored remediation workflows

These capabilities are particularly valuable for CISOs, Heads of Data Security, and AI Security teams tasked with securing sensitive data in complex, distributed environments. 

Automate Data Remediation and Strengthen Cloud Security

Today’s cloud and AI environments demand more than visibility, they require decisive, automated action. Security leaders can no longer afford to rely on manual processes when sensitive data is constantly in motion.

Sentra delivers the speed, precision, and context required to protect what matters most. By embedding automated remediation into core security workflows, organizations can eliminate blind spots, respond instantly to risk, and ensure compliance at scale.

‍

<blogcta-big>

‍

Why New Healthcare Cybersecurity Regulations Are Critical

In today’s healthcare landscape, cyberattacks on hospitals and health services have become increasingly common and devastating. For organizations that handle vast amounts of sensitive patient information, a single breach can mean exposing millions of records, causing not only financial repercussions but also risking patient privacy, trust, and care continuity.

Top Data Breaches in Hospitals in 2024: A Year of Costly Cyber Incidents

In 2024, there have been a series of high-profile data breaches in the healthcare sector, exposing critical vulnerabilities and emphasizing the urgent need for stronger cybersecurity measures in 2025 and beyond. Among the most significant incidents was the breach at Change Healthcare, Inc., which resulted in the exposure of 100 million records. As one of the largest healthcare data breaches in history, this event highlighted the challenges of securing patient data at scale and the immense risks posed by hacking incidents. Similarly, HealthEquity, Inc. suffered a breach impacting 4.3 million individuals, highlighting the vulnerabilities associated with healthcare business associates who manage data for multiple organizations. Finally, Concentra Health Services, Inc. experienced a breach that compromised nearly 4 million patient records, raising critical concerns about the adequacy of cybersecurity defenses in healthcare facilities. These incidents have significantly impacted patients and providers alike, highlighting the urgent need for robust cybersecurity measures and stricter regulations to protect sensitive data.

New York’s New Cybersecurity Reporting Requirements for Hospitals

In response to the growing threat of cyberattacks, many healthcare organizations and communities are implementing stronger cybersecurity protections. In October, New York State took a significant step by introducing new cybersecurity regulations for general hospitals aimed at safeguarding patient data and reinforcing security measures across healthcare systems. Under these regulations, hospitals in New York must report any “material cybersecurity incident” to the New York State Department of Health (NYSDOH) within 72 hours of discovery.

This 72-hour reporting window aligns with other global regulatory frameworks, such as the European Union’s GDPR and the SEC’s requirements for public companies. However, its application in healthcare represents a critical shift, ensuring incidents are addressed and reported promptly.

‍

The rapid reporting requirement aims to:

• Enable the NYSDOH to assess and respond to cyber incidents across the state’s healthcare network.
• Help mitigate potential fallout by ensuring hospitals promptly address vulnerabilities.
• Protect patients by fostering transparency around data breaches and associated risks.

For hospitals, meeting this requirement means refining incident response protocols to act swiftly upon detecting a breach. Compliance with these regulations not only safeguards patient data but also strengthens trust in healthcare services.

With these regulations, New York is setting a precedent that could reshape healthcare cybersecurity standards nationwide. By emphasizing proactive cybersecurity and quick incident response, the state is establishing a higher bar for protecting sensitive data in healthcare organizations, inspiring other states to potentially follow as well.

HIPAA Updates and the Role of HHS

While New York leads with immediate, state-level action, the Department of Health and Human Services (HHS) is also working to update the HIPAA Security Rule with new cybersecurity standards. These updates, expected to be proposed later this year, will follow a lengthy regulatory process, including a notice of proposed rulemaking, a public comment period, and the eventual issuance of a final rule. Once finalized, healthcare organizations will have time to comply.

‍

In the interim, the HHS has outlined voluntary cybersecurity goals, announced in January 2024. While these recommendations are a step forward, they lack the urgency and enforceability of New York’s state-level regulations. The contrast between the swift action in New York and the slower federal process highlights the critical role state initiatives play in bridging gaps in patient data protection.

‍

Together, these developments—New York’s rapid reporting requirements and the ongoing HIPAA updates—show a growing recognition of the need for stronger cybersecurity measures in healthcare. They emphasize the importance of immediate action at the state level while federal efforts progress toward long-term improvements in data security standards.

Penalties for Healthcare Cybersecurity Non-Compliance in NY

Non-compliance with any health law or regulation in New York State, including cybersecurity requirements, may result in penalties. However, the primary goal of these regulations is not to impose financial penalties but to ensure that healthcare facilities are equipped with the necessary resources and guidance to defend against cyberattacks. Under Section 12 of health law regulations in New York State, violations can result in civil penalties of up to $2,000 per offense, with increased fines for more severe or repeated infractions. If a violation is repeated within 12 months and poses a serious health threat, the fine can rise to $5,000. For violations directly causing serious physical harm to a patient, penalties may reach $10,000. A portion of fines exceeding $2,000 is allocated to the Patient Safety Center to support its initiatives. These penalties aim to ensure compliance, with enforcement actions carried out by the Commissioner or the Attorney General. Additionally, penalties may be negotiated or settled under certain circumstances, providing flexibility while maintaining accountability.

Importance of Prioritizing Breach Reporting

With the rapid digitization of healthcare services, regulations are expected to tighten significantly in the coming years. HIPAA, in particular, is anticipated to evolve with stronger privacy protections and expanded rules to address emerging challenges. Healthcare providers must make cybersecurity a top priority to protect patients from cyber threats. This involves adopting proactive risk assessments, implementing strong data protection strategies, and optimizing breach detection, response, and reporting capabilities to meet regulatory requirements effectively.

Data Security Platforms (DSPs) are essential for safeguarding sensitive healthcare data. These platforms enable organizations to locate and classify patient information, such as lab results, prescriptions, personally identifiable information, or medical images - across multiple formats and environments, ensuring comprehensive protection and regulatory compliance.

Breach Reporting With Sentra

A proper classification solution is essential for understanding the nature and sensitivity of your data at all times. With Sentra, you gain a clear, real-time view of your data's classification, making it easier to determine if sensitive data was involved in a breach, identify the types of data affected, and track who had access to it. This ensures that your breach reports are accurate, comprehensive, and aligned with regulatory requirements.

‍

Sentra can help you to adhere to many compliance frameworks, including PCI, GDPR, SOC2 and more, that may be applicable to your sensitive data as it travels around the organization. It automatically will alert you to violations, provide insight into the impact of any compromise, help you to prioritize associated risks, and integrate with common IR tools to streamline remediation. Sentra automates these processes so you can focus energies on eliminating risks.

If you want to learn more about Sentra's Data Security Platform, and how you can get started with adhering to the different compliance frameworks, please visit Sentra's demo page.

‍

‍

<blogcta-big>

‍

Data Loss Prevention (DLP) is a keystone of enterprise security, yet traditional DLP solutions continue to suffer from high rates of both false positives and false negatives, primarily because they struggle to accurately identify and classify sensitive data in cloud-first environments.

‍

New advanced data discovery and contextual classification technology directly addresses this gap, transforming DLP from an imprecise, reactive tool into a proactive, highly effective solution for preventing data loss.

Why DLP Solutions Can’t Work Alone

DLP solutions are designed to prevent sensitive or confidential data from leaving your organization, support regulatory compliance, and protect intellectual property and reputation. A noble goal indeed.  Yet DLP projects are notoriously anxiety-inducing for CISOs. On the one hand,  they often generate a high amount of false positives that disrupt legitimate business activities and further exacerbate alert fatigue for security teams.

‍

What’s worse than false positives? False negatives. Today traditional DLP solutions too often fail to prevent data loss because they cannot efficiently discover and classify sensitive data in dynamic, distributed, and ephemeral cloud environments.

‍

Traditional DLP faces a twofold challenge: 

• High False Positives: DLP tools often flag benign or irrelevant data as sensitive, overwhelming security teams with unnecessary alerts and leading to alert fatigue.
  
  
• High False Negatives: Sensitive data is frequently missed due to poor or outdated classification, leaving organizations exposed to regulatory, reputational, and operational risks.
  
  

These issues stem from DLP’s reliance on basic pattern-matching, static rules, and limited context. As a result, DLP cannot keep pace with the ways organizations use, store, and share data, resulting in the dual-edged sword of both high false positives and false negatives. Furthermore, the explosion of unstructured data types and shadow IT creates blind spots that traditional DLP solutions cannot detect. As a result, DLP often can’t  keep pace with the ways organizations use, store, and share data. It isn’t that DLP solutions don’t work, rather they lack the underlying discovery and classification of sensitive data needed to work correctly.

AI-Powered Data Discovery & Classification Layer

Continuous, accurate data classification is the foundation for data security. An AI-powered data discovery and classification platform can act as the intelligence layer that makes DLP work as intended. Here’s how Sentra complements the core limitations of DLP solutions:

1. Continuous, Automated Data Discovery

• Comprehensive Coverage: Discovers sensitive data across all data types and locations - structured and unstructured sources, databases, file shares, code repositories, cloud storage, SaaS platforms, and more.
  
  
• Cloud-Native & Agentless: Scans your entire cloud estate (AWS, Azure, GCP, Snowflake, etc.) without agents or data leaving your environment, ensuring privacy and scalability.
  ‍
• Shadow Data Detection: Uncovers hidden or forgotten (“shadow”) data sets that legacy tools inevitably miss, providing a truly complete data inventory.

2. Contextual, Accurate Classification

• AI-Driven Precision: Sentra proprietary LLMs and hybrid models achieve over 95% classification accuracy, drastically reducing both false positives and false negatives.
  
  
• Contextual Awareness: Sentra goes beyond simple pattern-matching to truly understand business context, data lineage, sensitivity, and usage, ensuring only truly sensitive data is flagged for DLP action.
  ‍
• Custom Classifiers: Enables organizations to tailor classification to their unique business needs, including proprietary identifiers and nuanced data types, for maximum relevance.

3. Real-Time, Actionable Insights

• Sensitivity Tagging: Automatically tags and labels files with rich metadata, which can be fed directly into your DLP for more granular, context-aware policy enforcement.
  
  
• API Integrations: Seamlessly integrates with existing DLP, IR, ITSM, IAM, and compliance tools, enhancing their effectiveness without disrupting existing workflows.
• Continuous Monitoring: Provides ongoing visibility and risk assessment, so your DLP is always working with the latest, most accurate data map.

How Sentra Supercharges DLP Solutions

Better Classification Means Less Noise, More Protection

• Reduce Alert Fatigue: Security teams focus on real threats, not chasing false alarms, which results in better resource allocation and faster response times.
  
  
• Accelerate Remediation: Context-rich alerts enable faster, more effective incident response, minimizing the window of exposure.
  
  
• Regulatory Compliance: Accurate classification supports GDPR, PCI DSS, CCPA, HIPAA, and more, reducing audit risk and ensuring ongoing compliance.
  
  
• Protect IP and Reputation: Discover and secure proprietary data, customer information, and business-critical assets, safeguarding your organization’s most valuable resources.
  
  

Why Sentra Outperforms Legacy Approaches

Sentra’s hybrid classification framework combines rule-based systems for structured data with advanced LLMs and zero-shot learning for unstructured and novel data types.

‍

This versatility ensures:

‍

• Scalability: Handles petabytes of data across hybrid and multi-cloud environments, adapting as your data landscape evolves.
  
• Adaptability: Learns and evolves with your business, automatically updating classifications as data and usage patterns change.
  
• Privacy: All scanning occurs within your environment - no data ever leaves your control, ensuring compliance with even the strictest data residency requirements.

Use Case: Where DLP Alone Fails, Sentra Prevails

A financial services company uses a leading DLP solution to monitor and prevent the unauthorized sharing of sensitive client information, such as account numbers and tax IDs, across cloud storage and email. The DLP is configured with pattern-matching rules and regular expressions for identifying sensitive data.

‍

What Goes Wrong:

‍
An employee uploads a spreadsheet to a shared cloud folder. The spreadsheet contains a mix of client names, account numbers, and internal project notes. However, the account numbers are stored in a non-standard format (e.g., with dashes, spaces, or embedded within other text), and the file is labeled with a generic name like “Q2_Projects.xlsx.” The DLP solution, relying on static patterns and file names, fails to recognize the sensitive data and allows the file to be shared externally. The incident goes undetected until a client reports a data breach.

‍

How Sentra Solves the Problem:

‍
To address this, the security team set out to find a solution capable of discovering and classifying unstructured data without creating more overhead. They selected Sentra for its autonomous ability to continuously discover and classify all types of data across their hybrid cloud environment. Once deployed, Sentra immediately recognizes the context and content of files like the spreadsheet that enabled the data leak. It accurately identifies the embedded account numbers—even in non-standard formats—and tags the file as highly sensitive.

‍

This sensitivity tag is automatically fed into the DLP, which then successfully enforces strict sharing controls and alerts the security team before any external sharing can occur. As a result, all sensitive data is correctly classified and protected, the rate of false negatives was dramatically reduced, and the organization avoids further compliance violations and reputational harm.

Getting Started with Sentra is Easy

1. Deploy Agentlessly: No complex installation. Sentra integrates quickly and securely into your environment, minimizing disruption.
   
   
2. Automate Discovery & Classification: Build a living, accurate inventory of your sensitive data assets, continuously updated as your data landscape changes.
   
   
3. Enhance DLP Policies: Feed precise, context-rich sensitivity tags into your DLP for smarter, more effective enforcement across all channels.
   
   
4. Monitor Continuously: Stay ahead of new risks with ongoing discovery, classification, and risk assessment, ensuring your data is always protected.
   
   

“Sentra’s contextual classification engine turns DLP from a reactive compliance checkbox into a proactive, business-enabling security platform.”

‍

Fuel DLP with Automatic Discovery & Classification

DLP is an essential data protection tool, but without accurate, context-aware data discovery and classification, it’s incomplete and often ineffective. Sentra supercharges your DLP with continuous data discovery and accurate classification, ensuring you find and protect what matters most—while eliminating noise, inefficiency, and risk. 

‍

Ready to see how Sentra can supercharge your DLP? Contact us for a demo today.

<blogcta-big>

‍

The Importance of Data Labeling and Tagging

In today's fast-paced business environment, data rarely stays in one place. It moves across devices, applications, and services as individuals collaborate with internal teams and external partners. This mobility is essential for productivity but poses a challenge: how can you ensure your data remains secure and compliant with business and regulatory requirements when it's constantly on the move?

Why Labeling and Tagging Data Matters

Data labeling and tagging provide a critical solution to this challenge. By assigning sensitivity labels to your data, you can define its importance and security level within your organization. These labels act as identifiers that abstract the content itself, enabling you to manage and track the data type without directly exposing sensitive information. With the right labeling, organizations can also control access in real-time.

‍

For example, labeling a document containing social security numbers or credit card information as Highly Confidential allows your organization to acknowledge the data's sensitivity and enforce appropriate protections, all without needing to access or expose the actual contents.

Why Sentra’s AI-Based Classification Is a Game-Changer

Sentra’s AI-based classification technology enhances data security by ensuring that the sensitivity labels are applied with exceptional accuracy. Leveraging advanced LLM models, Sentra enhances data classification with context-aware capabilities, such as:
‍

• Detecting the geographic residency of data subjects.
• Differentiating between Customer Data and Employee Data.
• Identifying and treating Synthetic or Mock Data differently from real sensitive data.

This context-based approach eliminates the inefficiencies of manual processes and seamlessly scales to meet the demands of modern, complex data environments. By integrating AI into the classification process, Sentra empowers teams to confidently and consistently protect their data—ensuring sensitive information remains secure, no matter where it resides or how it is accessed.

‍Benefits of Labeling and Tagging in Sentra

Sentra enhances your ability to classify and secure data by automatically applying sensitivity labels to data assets. By automating this process, Sentra removes the manual effort required from each team member—achieving accuracy that’s only possible through a deep understanding of what data is sensitive and its broader context.

‍

Here are some key benefits of labeling and tagging in Sentra:

‍

1. Enhanced Security and Loss Prevention: Sentra’s integration with Data Loss Prevention (DLP) solutions prevents the loss of sensitive and critical data by applying the right sensitivity labels. Sentra’s granular, contextual tags help to provide the detail necessary to action remediation automatically so that operations can scale.
   ‍‍
2. Easily Build Your Tagging Rules: Sentra’s Intuitive Rule Builder allows you to automatically apply sensitivity labels to assets based on your pre-existing tagging rules and or define new ones via the builder UI (see screen below). Sentra imports discovered Microsoft Purview Information Protection (MPIP) labels to speed this process.

3. Labels Move with the Data: Sensitivity labels created in Sentra can be mapped to Microsoft Purview Information Protection (MPIP) labels and applied to various applications like SharePoint, OneDrive, Teams, Amazon S3, and Azure Blob Containers. Once applied, labels are stored as metadata and travel with the file or data wherever it goes, ensuring consistent protection across platforms and services.
   ‍
4. Automatic Labeling: Sentra allows for the automatic application of sensitivity labels based on the data's content. Auto-tagging rules, configured for each sensitivity label, determine which label should be applied during scans for sensitive information.
   ‍
5. Support for Structured and Unstructured Data: Sentra enables labeling for files stored in cloud environments such as Amazon S3 or EBS volumes and for database columns in structured data environments like Amazon RDS. By implementing these labeling practices, your organization can track, manage, and protect data with ease while maintaining compliance and safeguarding sensitive information. Whether collaborating across services or storing data in diverse cloud environments, Sentra ensures your labels and protection follow the data wherever it goes.

Applying Sensitivity Labels to Data Assets in Sentra

In today’s rapidly evolving data security landscape, ensuring that your data is properly classified and protected is crucial. One effective way to achieve this is by applying sensitivity labels to your data assets. Sensitivity labels help ensure that data is handled according to its level of sensitivity, reducing the risk of accidental exposure and enabling compliance with data protection regulations.

‍

Below, we’ll walk you through the necessary steps to automatically apply sensitivity labels to your data assets in Sentra. By following these steps, you can enhance your data governance, improve data security, and maintain clear visibility over your organization's sensitive information.

‍

The process involves three key actions:
‍

1. Create Sensitivity Labels: The first step in applying sensitivity labels is creating them within Sentra. These labels allow you to categorize data assets according to various rules and classifications. Once set up, these labels will automatically apply to data assets based on predefined criteria, such as the types of classifications detected within the data. Sensitivity labels help ensure that sensitive information is properly identified and protected.
   ‍
2. Connect Accounts with Data Assets: The next step is to connect your accounts with the relevant data assets. This integration allows Sentra to automatically discover and continuously scan all your data assets, ensuring that no data goes unnoticed. As new data is created or modified, Sentra will promptly detect and categorize it, keeping your data classification up to date and reducing manual efforts.
   ‍
3. Apply Classification Tags: Whenever a data asset is scanned, Sentra will automatically apply classification tags to it, such as data classes, data contexts, and sensitivity labels. These tags are visible in Sentra’s data catalog, giving you a comprehensive overview of your data’s classification status. By applying these tags consistently across all your data assets, you’ll have a clear, automated way to manage sensitive data, ensuring compliance and security.

By following these steps, you can streamline your data classification process, making it easier to protect your sensitive information, improve your data governance practices, and reduce the risk of data breaches.

Applying MPIP Labels

In order to apply Microsoft Purview Information Protection (MPIP) labels based on Sentra sensitivity labels, you are required to follow a few additional steps:

‍

1. Set up the Microsoft Purview integration - which will allow Sentra to import and sync MPIP sensitivity labels.
   ‍
2. Create tagging rules - which will allow you to map Sentra sensitivity labels to MPIP sensitivity labels (for example “Very Confidential” in Sentra would be mapped to “ACME - Highly Confidential” in MPIP), and choose to which services this rule would apply (for example, Microsoft 365 and Amazon S3).

Using Sensitivity Labels in Microsoft DLP

Microsoft Purview DLP (as well as all other industry-leading DLP solutions) supports MPIP labels in its policies so admins can easily control and prevent data loss of sensitive data across multiple services and applications.For instance, a MPIP ‘highly confidential’ label may instruct Microsoft Purview DLP to restrict transfer of sensitive data outside a certain geography. Likewise, another similar label could instruct that confidential intellectual property (IP) is not allowed to be shared within Teams collaborative workspaces. Labels can be used to help control access to sensitive data as well. Organizations can set a rule with read permission only for specific tags. For example, only production IAM roles can access production files. Further, for use cases where data is stored in a single store, organizations can estimate the storage cost for each specific tag.

Build a Stronger Foundation with Accurate Data Classification

Effectively tagging sensitive data unlocks significant benefits for organizations, driving improvements across accuracy, efficiency, scalability, and risk management. With precise classification exceeding 95% accuracy and minimal false positives, organizations can confidently label both structured and unstructured data. Automated tagging rules reduce the reliance on manual effort, saving valuable time and resources. Granular, contextual tags enable confident and automated remediation, ensuring operations can scale seamlessly. Additionally, robust data tagging strengthens DLP and compliance strategies by fully leveraging Microsoft Purview’s capabilities. By streamlining these processes, organizations can consistently label and secure data across their entire estate, freeing resources to focus on strategic priorities and innovation.

<blogcta-big>