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DSPM vs Legacy Data Security Tools

July 10, 2024

Min Read

Data Security

Daniel Suissa

Data Team Lead

Businesses must understand where and how their sensitive data is used in their ever-changing data estates because the stakes are higher than ever. IBM’s Cost of a Data Breach 2023 report found that the average global cost of a data breach in 2023 was $4.45 million. And with the rise in generative AI tools, malicious actors develop new attacks and find security vulnerabilities quicker than ever before.

Even if your organization doesn’t experience a data breach, growing data and privacy regulations could negatively impact your business’s bottom line if not heeded.

With all of these factors in play, why haven’t many businesses up-leveled their data security and risen to the new challenges? In many cases, it’s because they are leveraging outdated technologies to secure a modern cloud environment. Tools designed for on premises environments often produce too many false positives, require manual setup and constant reconfiguration, and lack complete visibility into multi-cloud environments. To answer these liabilities, many businesses are turning to data security posture management (DSPM), a relatively new approach to data security that focuses on securing data wherever it goes despite the underlying infrastructure.

Can Legacy Tools Enable Today’s Data Security Best Practices?

As today’s teams look to secure their ever-evolving cloud data stores, a few specific requirements arise. Let’s see how these modern requirements stack up with legacy tools’ capabilities:

Compatibility with a Multi-Cloud Environment

Today, the average organization uses several connected databases, technologies, and storage methods to host its data and operations. Its data estate will likely consist of SaaS applications, a few cloud instances, and, in some cases, on premises data centers.

Legacy tools are incompatible with many multi-cloud environments because:

They cannot recognize all the moving parts of a modern cloud environment and treat cloud and SaaS technologies as though they are full members of the IT ecosystem. They may flag normal cloud operations as threats, leading to lots of false positives and noisy alerts.
They are difficult to maintain in a sprawling cloud environment, as they often require teams to manually configure a connector for each data store. When an organization is spinning up cloud resources rapidly and must connect dozens of stores daily, this process takes tons of effort and limits security, scalability and agility.

Continuous Threat Detection

In addition, today’s businesses need security measures that can keep up with emerging threats. Malicious actors are constantly finding new ways to commit data breaches. For example, generative AI can be used to scan an organization’s environment and identify any weaknesses with unprecedented speed and accuracy. In addition, LLMs often create internal threats which are more prevalent because so many employees have access to sensitive data.

Legacy tools cannot respond adequately to these growing threats because:

They use signature-based malware detection to detect and contain threats.
This technique for detecting risk will inevitably miss novel threats and more nuanced risks within SaaS and cloud environments.

Data-Centric Security Approach

Today’s teams also need a data-centric approach to security. Data democratization happens in most businesses (which is a good thing!). However, this democratization comes with a cost, as it allows any number of employees to access, move, and copy sensitive data.

In addition, newer applications that feature lots of AI and automation require massive amounts of data to function. As they perform tasks within businesses, these modern applications will share, copy, and transform data at a rapid speed — often at a scale unmanageable via manual processes.

As a result, sensitive data proliferates everywhere in the organization, whether within cloud storage like SharePoint, as part of data pipelines for modern applications, or even as downloaded files on an employee’s computer.

Legacy tools tend to be ineffective in finding data across the organization because:

Legacy tools’ best defense against this proliferation is to block any actions that look risky. These hyperactive security defenses become “red tape” for employees or connected applications that just need to access the data to do their jobs.
They also trigger false alarms frequently and tend to miss important signals, such as suspicious activities in SaaS applications.

Accurate Data Classification

Modern organizations also need the ability to classify discovered data in precise and granular ways. The likelihood of exposure for any given data will depend on several contextual factors, including location, usage, and the level of security surrounding it.

Legacy tools fall short in this area because:

They cannot classify data with this level of granularity, which, again, leads to false positives and noisy alerts.
There is inadequate data context to determine the true sensitivity based on business use
Many tools also require agents or sidecars to start classifying data, which requires extensive time and work to set up and maintain.

Big-Picture Visibility of Risk

Organizations require a big-picture view of data context, movement, and risk to successfully monitor the entire data estate. This is especially important because the risk landscape in a modern data environment is extremely prone to change. In addition, many data and privacy regulations require businesses to understand how and where they leverage PII.

Legacy tools make it difficult for organizations to stay on top of these changes because:

Legacy tools can only monitor data stored in on premises storage and SaaS applications, leaving cloud technologies like IaaS and PaaS unaccounted for.
Legacy tools fail to meet emerging regulations. For example, a new addendum to GDPR requires companies to tell individuals how and where they leverage their personal data. It’s difficult to follow these guidelines if you can’t figure out where this sensitive data resides in the first place.

Data Security Posture Management (DSPM): A Modern Approach

As we can see, legacy data security tools lack key functionality to meet the demands of a modern hybrid environment. Instead, today’s organizations need a solution that can secure all areas of their data estate — cloud, on premises, SaaS applications, and more.

Data Security Posture Management (also known as DSPM) is a modern approach that works alongside the complexity and breadth of a modern cloud environment. It offers automated data discovery and classification, continuous monitoring of data movement and access, and a deep focus on data-centric security that goes far beyond just defending network perimeters.

Key Features of Legacy Data Security Tools vs. DSPM

But how does DSPM stack up against some specific legacy tools? Let’s dive into some one-to-one comparisons.

Legacy Tools	Data Security Posture Management
Legacy Data Intelligence While these tried-and-true tools have a large market presence, they take a very rigid and labor-intensive approach to security data. Connector-based, so it is more challenging to scale. No auto-discovery capabilities, so these tools can miss shadow data. A long time-to-value, as it takes months or even years to stand up in your environment.	No connectors required, making it far easier to scale and add different accounts, users, cloud instances, etc. Auto-discovery capabilities, enabling teams to uncover unknown or orphaned data. Time-to-value within hours of implementation.
Cloud DSPM While cloud-only DSPM solutions can help organizations secure data amid rapid cloud data proliferation, they don’t account for any remaining on premises data centers that a company continues to operate. Incompatible with older data formats such as network-attached storage (NAS) and file servers Often lack the ability to scan on prem database formats, such as MSSQL, Oracle, and MySQL.	Scanning capabilities for structured, unstructured, and semi-structured data within both cloud and on prem environments. Visibility into all corners of the data estate to automate and prioritize risk management.
Cloud Access Security Broker (CASB) Although many organizations have traditionally relied on CASB to address cloud data security, these solutions often lack comprehensive visibility. Not compatible with SaaS applications, making it difficult for them to detect new applications and services added over time. Complex deployment, requiring lots of manual intervention to configure and tune to an organization’s specific environment. Ineffective for detecting zero-day threats or insider threats.	Compatible with new SaaS applications, services, and other integrations. Simple to deploy and begin using across the organization’s environments. Effective for detecting emerging threats, thanks to sophisticated data access governance capabilities.
Cloud Security Posture Management (CSPM) /Cloud-Native Application Protection Platform (CNAPP) While these solutions provide strong cloud infrastructure protection, such as flagging misconfigurations and integrating with DevSecOps processes, they lack data context and only offer static controls that can’t adapt to data proliferation. Sometimes, these solutions remove data for analysis, which poses additional risk to the organization. No on prem or SaaS support, making it complex to integrate these tools with an entire data estate. Limited risk-prioritization, as it only tracks the security of cloud storage, not the data that resides within those cloud stores.	Data stays inside the organization’s environments, minimizing third-party risk. Support for all areas of the modern data estate — on prem, SaaS, IaaS, PaaS, etc. Strong risk prioritization, as it takes data context into consideration.

How does DSPM integrate with existing security tools?

DSPM integrates seamlessly with other security tools, such as team collaboration tools (Microsoft Teams, Slack, etc.), observability tools (Datadog), security and incident response tools (such as SIEMs, SOARs, and Jira/ServiceNow ITSM), and more.

Can DSPM help my existing data loss prevention system?

DSPM integrates with existing DLP solutions, providing rich context regarding data sensitivity that can be used to better prioritize remediation efforts/actions. DSPM provides accurate, granular sensitivity labels that can facilitate confident automated actions and better streamline processes.

What are the benefits of using DSPM?

DSPM enables businesses to take a proactive approach to data security, leading to:

Reduced risk of data breaches
Improved compliance posture
Faster incident response times
Optimized security resource allocation

Embrace DSPM for a Future-Proof Security Strategy

Embracing DSPM for your organization doesn’t just support your proactive security initiatives today; it ensures that your data security measures will scale up with your business’s growth tomorrow. Because today’s data estates evolve so rapidly — both in number of components and in data proliferation — it’s in your business’s best interest to find cloud-native solutions that will adapt to these changes seamlessly.

Learn how Sentra’s DSPM can help your team gain data visibility within minutes of deployment.

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Daniel Suissa

Data Team Lead

Daniel is the Data Team Lead at Sentra. He has nearly a decade of experience in engineering, and in the cybersecurity sector. He earned his BSc in Computer Science at NYU.

Latest Blog Posts

Yair Cohen

January 28, 2025

Min Read

Data Security

Data Protection and Classification in Microsoft 365

Imagine the fallout of a single misstep—a phishing scam tricking an employee into sharing sensitive data. The breach doesn’t just compromise information; it shakes trust, tarnishes reputations, and invites compliance penalties. With data breaches on the rise, safeguarding your organization’s Microsoft 365 environment has never been more critical.

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Data classification helps prevent such disasters. This article provides a clear roadmap for protecting and classifying Microsoft 365 data. It explores how data is saved and classified, discusses built-in tools for protection, and covers best practices for maintaining Microsoft 365 data protection.

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How Is Data Saved and Classified in Microsoft 365?

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Microsoft 365 stores data across tools and services. For example, emails are stored in Exchange Online, while documents and data for collaboration are found in Sharepoint and Teams, and documents or files for individual users are stored in OneDrive. This data is primarily unstructured—a format ideal for documents and images but challenging for identifying sensitive information.

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All of this data is largely stored in an unstructured format typically used for documents and images. This format not only allows organizations to store large volumes of data efficiently; it also enables seamless collaboration across teams and departments. However, as unstructured data cannot be neatly categorized into tables or columns, it becomes cumbersome to discern what data is sensitive and where it is stored.

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To address this, Microsoft 365 offers a data classification dashboard that helps classify data of varying levels of sensitivity and data governed by different regulatory compliance frameworks. But how does Microsoft identify sensitive information with unstructured data?

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Microsoft employs advanced technologies such as RegEx scans, trainable classifiers, Bloom filters, and data classification graphs to identify and classify data as public, internal, or confidential. Once classified, data protection and governance policies are applied based on sensitivity and retention labels.

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Data classification is vital for understanding, protecting, and governing data. With your Microsoft 365 data classified appropriately, you can ensure seamless collaboration without risking data exposure.

Figure 1: Why data classification is important

Microsoft 365 Data Protection and Classification Tools

Microsoft 365 includes several key tools and frameworks for classifying and securing data. Here are a few.

Microsoft Purview

Microsoft Purview is a cornerstone of data classification and protection within Microsoft 365.

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Key Features:

Over 200+ prebuilt classifiers and the ability to create custom classifiers tailored to specific business needs.
Purview auto-classifies data across Microsoft 365 and other supported apps, such as Adobe Photoshop and Adobe PDF, while users work on them.
Sensitivity labels that apply encryption, watermarks, and access restrictions to secure sensitive data.‍
Double Key Encryption to ensure that sensitivity labels persist even when file formats change.
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Sensitivity watermarks in M365 — Figure 2: Sensitivity watermarks in Microsoft 365 (Source: Microsoft)

Figure 3: Sensitivity labels for information protection policies in Microsoft 365 (Source: Microsoft)

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Purview autonomously applies sensitivity labels like "confidential" or "highly confidential" based on preconfigured policies, ensuring optimal access control. These labels persist even when files are shared or converted to other formats, such as from Word to PDF.

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Additionally, Purview’s data loss prevention (DLP) policies prevent unauthorized sharing or deletion of sensitive data by flagging and reporting violations in real time. For example, if a sensitive file is shared externally, Purview can immediately block the transfer and alert your security team.

Sensitivity labeling for announcements in M365 — Figure 4: Preventing data loss by using sensitivity labels (Source: Microsoft)

Microsoft Defender

Microsoft Defender for Cloud Apps strengthens security by providing a cloud app discovery window to identify applications accessing data. Once identified, it classifies files within these applications based on sensitivity, applying appropriate protections as per preconfigured policies.

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Key Features:

Data Sensitivity Classification: Defender identifies sensitive files and assigns protection based on sensitivity levels, ensuring compliance and reducing risk. For example, it labels files containing credit card numbers, personal identifiers, or confidential business information with sensitivity classifications like "Highly Confidential."
Threat Detection and Response: Defender detects known threats targeted at sensitive data in emails, collaboration tools (like SharePoint and Teams), URLs, file attachments, and OneDrive. If an admin account is compromised, Microsoft Defender immediately spots the threat, disables the account, and notifies your IT team to prevent significant damage.‍
Automation: Defender automates incident response, ensuring that malicious activities are flagged and remediated promptly.

Intune

Microsoft Intune provides comprehensive device management and data protection, enabling organizations to enforce policies that safeguard sensitive information on both managed and unmanaged smartphones, computers, and other devices.

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Key Features:

Customizable Compliance Policies: Intune allows organizations to enforce device compliance policies that align with internal and regulatory standards. For example, it can block non-compliant devices from accessing sensitive data until issues are resolved.
Data Access Control: Intune disallows employees from accessing corporate data on compromised devices or through insecure apps, such as those not using encryption for emails.‍
Endpoint Security Management: By integrating with Microsoft Defender, Intune provides endpoint protection and automated responses to detected threats, ensuring only secure devices can access your organization’s network.

Endpoint security overview — Figure 6: Intune device management portal (Source: Microsoft)

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Intune supports organizations by enabling the creation and enforcement of device compliance policies tailored to both internal and regulatory standards. These policies detect non-compliant devices, issue alerts, and restrict access to sensitive data until compliance is restored. Conditional access ensures that only secure and compliant devices connect to your network.

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Microsoft 365-managed apps like Outlook, Word, and Excel. These policies define which apps can access specific data, such as emails, and regulate permissible actions, including copying, pasting, forwarding, and taking screenshots. This layered security approach safeguards critical information while maintaining seamless app functionality.

Does Microsoft have a DLP Solution?

Microsoft 365’s data loss prevention (DLP) policies represent the implementation of the zero-trust framework. These policies aim to prevent oversharing, accidental deletion, and data leaks across Microsoft 365 services, including Exchange Online, SharePoint, Teams, and OneDrive, as well as Windows and macOS devices.

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Retention policies, deployed via retention labels, help organizations manage the data lifecycle effectively.These labels ensure that data is retained only as long as necessary to meet compliance requirements, reducing the risks associated with prolonged data storage.

Figure 7: How DLP policies work (Source: Microsoft)

What is the Microsoft 365 Compliance Center?

The Microsoft 365 compliance center offers tools to manage policies and monitor data access, ensuring adherence to regulations. For example, DLP policies allow organizations to define specific automated responses when certain regulatory requirements—like GDPR or HIPAA—are violated.

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Microsoft Purview Compliance Portal: This portal ensures sensitive data is classified, stored, retained, and used in adherence to relevant compliance regulations. Meanwhile, Microsoft 365’s MPIP ensures that only authorized users can access sensitive information, whether collaborating on Teams or sharing files in SharePoint. Together, these tools enable secure collaboration while keeping regulatory compliance at the forefront.

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12 Best Practices for Microsoft 365 Data Protection and Classification

To achieve effective Microsoft 365 data protection and classification, organizations should follow these steps:

Create precise labels, tags, and classification policies; don’t rely solely on prebuilt labels and policies, as definitions of sensitive data may vary by context.
Automate labeling to minimize errors and quickly capture new datasets.
Establish and enforce data use policies and guardrails automatically to reduce risks of data breaches, compliance failures, and insider threat risks.
Regularly review and update data classification and usage policies to reflect evolving threats, new data storage, and changing compliance laws.o policies must stay up to date to remain effective.
Define context-appropriate DLP policies based on your business needs; factoring in remote work, ease of collaboration, regional compliance standards, etc.
Apply encryption to safeguard data inside and outside your organization.
Enforce role-based access controls (RBAC) and least privilege principles to ensure users only have access to data and can perform actions within the scope of their roles. This limits the risk of accidental data exposure, deletion, and cyberattacks.
Create audit trails of user activity around data and maintain version histories to prevent and track data loss.
Follow the 3-2-1 backup rule: keep three copies of your data, store two on different media, and one offsite.
Leverage the full suite of Microsoft 365 tools to monitor sensitive data, detect real-time threats, and secure information effectively.
Promptly resolve detected risks to mitigate attacks early.
Ensure data protection and classification policies do not impede collaboration to prevent teams from creating shadow data, which puts your organization at risk of data breaches.

For example, consider #3. If a disgruntled employee starts transferring sensitive intellectual property to external devices in preparation for a ransomware attack, having the right data use policies in place will allow your organization to stop the threat before it escalates.

Microsoft 365 Data Protection and Classification Limitations

Despite Microsoft 365’s array of tools, there are some key gaps. AI/ML-powered data security posture management (DSPM) and data detection and response (DDR) solutions fill these easily.

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The top limitations of Microsoft 365 data protection and classification are the following:

Limitations Handling Large Volumes of Unstructured Data: Purview struggles to automatically classify and apply sensitivity labels to diverse and vast datasets, particularly in Azure services or non-Microsoft clouds.
Contextless Data Classification: Without considering context, Microsoft Purview’s MPIP can lead to false positives (over-labeling non-sensitive data) or false negatives (missing sensitive data).
Inconsistent Labeling Across Providers: Microsoft tools are limited to its ecosystem, making it difficult for enterprises using multi-cloud environments to enforce consistent organization-wide labeling.
Minimal Threat Response Capabilities: Microsoft Defender relies heavily on IT teams for remediation and lacks robust autonomous responses.
Sporadic Interruption of User Activity: Inaccurate DLP classifications can disrupt legitimate data transfers in collaboration channels, frustrating employees and increasing the risk of shadow IT workarounds.

Sentra Fills the Gap: Protection Measures to Address Microsoft 365 Data Risks

Today’s businesses must get ahead of data risks by instituting Microsoft 365 data protection and classification best practices such as least privilege access and encryption. Otherwise, they risk data exposure, damaging cyberattacks, and hefty compliance fines. However, implementing these best practices depends on accurate and context-sensitive data classification in Microsoft 365.

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Sentra’s Cloud-native Data Security Platform enables secure collaboration and file sharing across all Microsoft 365 services including SharePoint, OneDrive, Teams, OneNote, Office, Word, Excel, and more. Sentra provides data access governance, shadow data detection, and privacy audit automation for M365 data. It also evaluates risks and alerts for policy or regulatory violations.

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Specifically, Sentra complements Purview in the following ways:

Sentra Data Detection & Response (DDR): Continuously monitors for threats such as data exfiltration, weakening of data security posture, and other suspicious activities in real time. While Purview Insider Risk Management focuses on M365 applications, Sentra DDR extends these capabilities to Azure and non-Microsoft applications.
Data Perimeter Protection: Sentra automatically detects and identifies an organization’s data perimeters across M365, Azure, and non-Microsoft clouds. It alerts “organizations when sensitive data leaves its boundaries, regardless of how it is copied or exported.
Shadow Data Reduction: Using context-based analysis powered by Sentra’s DataTreks™, the platform identifies unnecessary shadow data, reducing the attack surface and improving data governance.
Training Data Monitoring: Sentra monitors training datasets continuously, identifying privacy violations of sensitive PII or real-time threats like training data poisoning or suspicious access.
Data Access Governance: Sentra adds to Purview’s data catalog by including metadata on users and applications with data access permissions, ensuring better governance.
Automated Privacy Assessments: Sentra automates privacy evaluations aligned with frameworks like GDPR and CCPA, seamlessly integrating them into Purview’s data catalog.
Rich Contextual Insights: Sentra delivers detailed data context to understand usage, sensitivity, movement, and unique data types. These insights enable precise risk evaluation, threat prioritization, and remediation, and they can be consumed via an API by DLP systems, SIEMs, and other tools.

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By addressing these gaps, Sentra empowers organizations to enhance their Microsoft 365 data protection and classification strategies. Request a demo to experience Sentra’s innovative solutions firsthand.

Team Sentra

December 26, 2024

Min Read

Data Security

Create an Effective RFP for a Data Security Platform & DSPM

This RFP Guide is designed to help organizations create their own RFP for selection of Cloud-native Data Security Platform (DSP) & Data Security Posture Management (DSPM) solutions. The purpose is to identify key essential requirements that will enable effective discovery, classification, and protection of sensitive data across complex environments, including in public cloud infrastructures and in on-premises environments.

Instructions for Vendors

Each section provides essential and recommended requirements to achieve a best practice capability. These have been accumulated over dozens of customer implementations. Customers may also wish to include their own unique requirements specific to their industry or data environment.

1. Data Discovery & Classification

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Requirement	Details
Shadow Data Detection	Can the solution discover and identify shadow data across any data environment (IaaS, PaaS, SaaS, OnPrem)?
Sensitive Data Classification	Can the solution accurately classify sensitive data, including PII, financial data, and healthcare data?
Efficient Scanning	Does the solution support smart sampling of large file shares and data lakes to reduce and optimize the cost of scanning, yet provide full scan coverage in less time and lower cloud compute costs?
AI-based Classification	Does the solution leverage AI/ML to classify data in unstructured documents and stores (Google Drive, OneDrive, SharePoint, etc) and achieve more than 95% accuracy?
Data Context	Can the solution discern and ‘learn’ the business purpose (employee data, customer data, identifiable data subjects, legal data, synthetic data, etc.) of data elements and tag them accordingly?
Data Store Compatibility	Which data stores (e.g., AWS S3, Google Cloud Storage, Azure SQL, Snowflake data warehouse, On Premises file shares, etc.) does the solution support for discovery?
Autonomous Discovery	Can the solution discover sensitive data automatically and continuously, ensuring up to date awareness of data presence?
Data Perimeters Monitoring	Can the solution track data movement between storage solutions and detect risky and non-compliant data transfers and data sprawl?

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2. Data Access Governance

Requirement	Details
Access Controls	Does the solution map access of users and non-human identities to data based on sensitivity and sensitive information types?
Location Independent Control	Does the solution help organizations apply least privilege access regardless of data location or movement?
Identity Activity Monitoring	Does the solution identify over-provisioned, unused or abandoned identities (users, keys, secrets) that create unnecessary exposures?
Data Access Catalog	Does the solution provide an intuitive map of identities, their access entitlements (read/write permissions), and the sensitive data they can access?
Integration with IAM Providers	Does the solution integrate with existing Identity and Access Management (IAM) systems?

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3. Posture, Risk Assessment & Threat Monitoring

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Requirement	Details
Risk Assessment	Can the solution assess data security risks and assign risk scores based on data exposure and data sensitivity?
Compliance Frameworks	Does the solution support compliance with regulatory requirements such as GDPR, CCPA, and HIPAA?
Similar Data Detection	Does the solution identify data that has been copied, moved, transformed or otherwise modified that may disguise its sensitivity or lessen its security posture?
Automated Alerts	Does the solution provide automated alerts for policy violations and potential data breaches?
Data Loss Prevention (DLP)	Does the solution include DLP features to prevent unauthorized data exfiltration?
3rd Party Data Loss Prevention (DLP)	Does the solution integrate with 3rd party DLP solutions?
User Behavior Monitoring	Does the solution track and analyze user behaviors to identify potential insider threats or malicious activity?
Anomaly Detection	Does the solution establish a baseline and use machine learning or AI to detect anomalies in data access or movement?

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4. Incident Response & Remediation

Requirement	Details
Incident Management	Can the solution provide detailed reports, alert details, and activity/change history logs for incident investigation?
Automated Response	Does the solution support automated incident response, such as blocking malicious users or stopping unauthorized data flows (via API integration to native cloud tools or other)?
Forensic Capabilities	Can the solution facilitate forensic investigation, such as data access trails and root cause analysis?
Integration with SIEM	Can the solution integrate with existing Security Information and Event Management (SIEM) or other analysis systems?

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5. Infrastructure & Deployment

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Requirement	Details
Deployment Models	Does the solution support flexible deployment models (on-premise, cloud, hybrid)? Is the solution agentless?
Cloud Native	Does the solution keep all data in the customer’s environment, performing classification via serverless functions? (ie. no data is ever removed from customer environment - only metadata)
Scalability	Can the solution scale to meet the demands of large enterprises with multi-petabyte data volumes?
Performance Impact	Does the solution work asynchronously without performance impact on the data production environment?
Multi-Cloud Support	Does the solution provide unified visibility and management across multiple cloud providers and hybrid environments?

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6. Operations & Support

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Requirement	Details
Onboarding	Does the solution vendor assist customers with onboarding? Does this include assistance with customization of policies, classifiers, or other settings?
24/7 Support	Does the vendor provide 24/7 support for addressing urgent security issues?
Training & Documentation	Does the vendor provide training and detailed documentation for implementation and operation?
Managed Services	Does the vendor (or its partners) offer managed services for organizations without dedicated security teams?
Integration with Security Tools	Can the solution integrate with existing security tools, such as firewalls, DLP systems, and endpoint protection systems?

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7. Pricing & Licensing

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Requirement	Details
Pricing Model	What is the pricing structure (e.g., per user, per GB, per endpoint)?
Licensing	What licensing options are available (e.g., subscription, perpetual)?
Additional Costs	Are there additional costs for support, maintenance, or feature upgrades?

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Conclusion

This RFP template is designed to facilitate a structured and efficient evaluation of DSP and DSPM solutions. Vendors are encouraged to provide comprehensive and transparent responses to ensure an accurate assessment of their solution’s capabilities.

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Sentra’s cloud-native design combines powerful Data Discovery and Classification, DSPM, DAG, and DDR capabilities into a complete Data Security Platform (DSP). With this, Sentra customers achieve enterprise-scale data protection and do so very efficiently - without creating undue burdens on the personnel who must manage it.

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To learn more about Sentra’s DSP, request a demo here and choose a time for a meeting with our data security experts. You can also choose to download the RFP as a pdf.

Gilad Golani

December 16, 2024

Min Read

Data Security

Best Practices: Automatically Tag and Label Sensitive Data

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.

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

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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.‍
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.

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.
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.
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.

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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.

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The process involves three key actions:

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.
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.
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:

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Set up the Microsoft Purview integration - which will allow Sentra to import and sync MPIP sensitivity labels.
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.