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Top 6 Azure Security Tools, Features, and Best Practices

November 7, 2022
6
Min Read

Nowadays, it is evident that the rapid growth of cloud computing has changed how organizations operate. Many organizations increasingly rely on the cloud to drive their daily business operations. The cloud is a single place for storing, processing and accessing data; it’s no wonder that people are becoming addicted to its convenience.

However, as the dependence on cloud service providers continues, the need for security also increases. One needs to measure and safeguard sensitive data to protect against possible threats. Remember that security is a shared responsibility - even if your cloud provider secures your data, the security will not be absolute. Thus, understanding the security features of a particular cloud service provider becomes significant.

Introduction to Microsoft Azure Security Services

Image of Microsoft Azure, explaining how to strengthen security posture with Azure

Microsoft Azure offers services and tools for businesses to manage their applications and infrastructure. Utilizing Azure ensures robust security measures are in place to protect sensitive data, maintain privacy, and mitigate potential threats.

This article will tackle Azure’s security features and tools to help organizations and individuals safeguard and protect their data while they continue their innovation and growth. 

There’s a collective set of security features, services, tools, and best practices offered by Microsoft to protect cloud resources. In this section, let's explore some layers to gain some insights.

The Layers of Security in Microsoft Azure:

Layers of Security Description
Physical Security Microsoft Azure has a strong foundation of physical security measures, and it operates state-of-the-art data centers worldwide with strict physical access controls, which ensures that Azure's infrastructure protects itself against unauthorized physical access.
Network Security Virtual networks, network security groups (NSGs), and distributed denial of service (DDoS) protection create isolated and secure network environments. Microsoft Azure network security mechanisms secure data in transit and protect against unauthorized network access. Of course, we must recognize Azure Virtual Network Gateway, which secures connections between on-premises networks and Azure resources.
Identity and Access Management (IAM) Microsoft Azure offers identity and access management capabilities to control and secure access to cloud resources. The Azure Active Directory (AD) is a centralized identity management platform that allows organizations to manage user identities, enforce robust authentication methods, and implement fine-grained access controls through role-based access control (RBAC).
Data Security Microsoft Azure offers Azure Storage Service Encryption (SSE) which encrypts data at rest, while Azure Disk Encryption secures virtual machine disks. Azure Key Vault provides a secure and centralized location for managing cryptographic keys and secrets.
Threat Detection and Monitoring Microsoft Azure offers Azure Security Center, which provides a centralized view of security recommendations, threat intelligence, and real-time security alerts. Azure Sentinel offers cloud-native security information that helps us quickly detect, alert, investigate, and resolve security incidents.
Compliance and Governance Microsoft Azure offers Azure Policy to define and enforce compliance controls across Azure resources within the organization. Moreover, it helps provide compliance certifications and adhere to industry-standard security frameworks.

Let’s explore some features and tools, and discuss their key features and best practices.

Azure Active Directory Identity Protection

Image of Azure’s Identity Protection page, explaining what is identity protection

Identity protection is a cloud-based service for the Azure AD suite. It focuses on helping organizations protect their user identities and detect potential security risks. Moreover, it uses advanced machine learning algorithms and security signals from various sources to provide proactive and adaptive security measures. Furthermore, leveraging machine learning and data analytics can identify risky sign-ins, compromised credentials, and malicious or suspicious user behavior. How’s that? Sounds great, right?

Key Features

1. Risk-Based User Sign-In Policies

It allows organizations to define risk-based policies for user sign-ins which evaluate user behavior, sign-in patterns, and device information to assess the risk level associated with each sign-in attempt. Using the risk assessment, organizations can enforce additional security measures, such as requiring multi-factor authentication (MFA), blocking sign-ins, or prompting password resets.

2. Risky User Detection and Remediation

The service detects and alerts organizations about potentially compromised or risky user accounts. It analyzes various signals, such as leaked credentials or suspicious sign-in activities, to identify anomalies and indicators of compromise. Administrators can receive real-time alerts and take immediate action, such as resetting passwords or blocking access, to mitigate the risk and protect user accounts.

Best Practices

  • Educate Users About Identity Protection - Educating users is crucial for maintaining a secure environment. Most large organizations now provide security training to increase the awareness of users. Training and awareness help users protect their identities, recognize phishing attempts, and follow security best practices.
  • Regularly Review and Refine Policies - Regularly assessing policies helps ensure their effectiveness, which is why it is good to continuously improve the organization’s Azure AD Identity Protection policies based on the changing threat landscape and your organization's evolving security requirements.

Azure Firewall

Image of Azure Firewall page, explaining what is Azure Firewall

Microsoft offers an Azure Firewall, which is a cloud-based network security service. It acts as a barrier between your Azure virtual networks and the internet. Moreover, it provides centralized network security and protection against unauthorized access and threats. Furthermore, it operates at the network and application layers, allowing you to define and enforce granular access control policies.

Thus, it enables organizations to control inbound and outbound traffic for virtual and on-premises networks connected through Azure VPN or ExpressRoute. Of course, we can’t ignore the filtering traffic of source and destination IP addresses, ports, protocols, and even fully qualified domain names (FQDNs).

Key Features

1. Network and Application-Level Filtering

This feature allows organizations to define rules based on IP addresses (source and destination), including ports, protocols, and FQDNs. Moreover, it helps organizations filter network and application-level traffic, controlling inbound and outbound connections.

2. Fully Stateful Firewall

Azure Firewall is a stateful firewall, which means it can intelligently allow return traffic for established connections without requiring additional rules. The beneficial aspect of this is it simplifies rule management and ensures that legitimate traffic flows smoothly.

3. High Availability and Scalability

Azure Firewall is highly available and scalable. It can automatically scale with your network traffic demand increases and provides built-in availability through multiple availability zones.

Best Practices

  • Design an Appropriate Network Architecture - Plan your virtual network architecture carefully to ensure proper placement of Azure Firewall. Consider network segmentation, subnet placement, and routing requirements to enforce security policies and control traffic flow effectively.
  • Implement Network Traffic Filtering Rules - Define granular network traffic filtering rules based on your specific security requirements. Start with a default-deny approach and allow only necessary traffic. Regularly review and update firewall rules to maintain an up-to-date and effective security posture.
  • Use Application Rules for Fine-Grain Control - Leverage Azure Firewall's application rules to allow or deny traffic based on specific application protocols or ports. By doing this, organizations can enforce granular access control to applications within their network.

Azure Resource Locks

Image of Azure Resource Locks page, explaining how to lock your resources to protect your infrastructure

Azure Resource Locks is a Microsoft Azure feature that allows you to restrict Azure resources to prevent accidental deletion or modification. It provides an additional layer of control and governance over your Azure resources, helping mitigate the risk of critical changes or deletions.

Key Features

Two types of locks can be applied:

1. Read-Only (CanNotDelete)

This lock type allows you to mark a resource as read-only, meaning modifications or deletions are prohibited.

2. CanNotDelete (Delete)

This lock type provides the highest level of protection by preventing both modifications and deletions of a resource; it ensures that the resource remains completely unaltered.

Best Practices

  • Establish a Clear Governance Policy - Develop a governance policy that outlines the use of Resource Locks within your organization. The policy should define who has the authority to apply or remove locks and when to use locks, and any exceptions or special considerations.
  • Leverage Azure Policy for Lock Enforcement - Use Azure Policy alongside Resource Locks to enforce compliance with your governance policies. It is because Azure Policy can automatically apply locks to resources based on predefined rules, reducing the risk of misconfigurations.

Azure Secure SQL Database Always Encrypted

Image of Azure Always Encrypted page, explaining how it works

Azure Secure SQL Database Always Encrypted is a feature of Microsoft Azure SQL Database that provides another security-specific layer for sensitive data. Moreover, it protects data at rest and in transit, ensuring that even database administrators or other privileged users cannot access the plaintext values of the encrypted data.

Key Features

1. Client-Side Encryption

Always Encrypted enables client applications to encrypt sensitive data before sending it to the database. As a result, the data remains encrypted throughout its lifecycle and can be decrypted only by an authorized client application.

2. Column-Level Encryption

Always Encrypted allows you to selectively encrypt individual columns in a database table rather than encrypting the entire database. It gives organizations fine-grained control over which data needs encryption, allowing you to balance security and performance requirements.

3. Transparent Data Encryption

The database server stores the encrypted data using a unique encryption format, ensuring the data remains protected even if the database is compromised. The server is unaware of the data values and cannot decrypt them.

Best Practices

The organization needs to plan and manage encryption keys carefully. This is because encryption keys are at the heart of Always Encrypted. Consider the following best practices.

  • Use a Secure and Centralized Key Management System - Store encryption keys in a safe and centralized location, separate from the database. Azure Key Vault is a recommended option for managing keys securely.
  • Implement Key Rotation and Backup - Regularly rotate encryption keys to mitigate the risks of key compromise. Moreover, establish a key backup strategy to recover encrypted data due to a lost or inaccessible key.
  • Control Access to Encryption Keys - Ensure that only authorized individuals or applications have access to the encryption keys. Applying the principle of least privilege and robust access control will prevent unauthorized access to keys.

Azure Key Vault

Image of Azure Key Vault page

Azure Key Vault is a cloud service provided by Microsoft Azure that helps safeguard cryptographic keys, secrets, and sensitive information. It is a centralized storage and management system for keys, certificates, passwords, connection strings, and other confidential information required by applications and services. It allows developers and administrators to securely store and tightly control access to their application secrets without exposing them directly in their code or configuration files.

Key Features

1. Key Management

Key Vault provides a secure key management system that allows you to create, import, and manage cryptographic keys for encryption, decryption, signing, and verification.

2. Secret Management

It enables you to securely store (as plain text or encrypted value) and manage secrets such as passwords, API keys, connection strings, and other sensitive information.

3. Certificate Management

Key Vault supports the storage and management of X.509 certificates, allowing you to securely store, manage, and retrieve credentials for application use.

4. Access Control

Key Vault provides fine-grained access control to manage who can perform operations on stored keys and secrets. It integrates with Azure Active Directory (Azure AD) for authentication and authorization.

Best Practices

  • Centralized Secrets Management - Consolidate all your application secrets and sensitive information in Key Vault rather than scattering them across different systems or configurations. The benefit of this is it simplifies management and reduces the risk of accidental exposure.
  • Use RBAC and Access Policies - Implement role-based access control (RBAC) and define granular access policies to power who can perform operations on Key Vault resources. Follow the principle of least privilege, granting only the necessary permissions to users or applications.
  • Secure Key Vault Access - Restrict access to Key Vault resources to trusted networks or virtual networks using virtual network service or private endpoints because it helps prevent unauthorized access to the internet.

Azure AD Multi-Factor Authentication

Image of Azure AD Multi-Factor Authentication page, explaining how it works

It is a security feature provided by Microsoft Azure that adds an extra layer of protection to user sign-ins and helps safeguard against unauthorized access to resources. Users must give additional authentication factors beyond just a username and password.

Key Features

1. Multiple Authentication Methods

Azure AD MFA supports a range of authentication methods, including phone calls, text messages (SMS), mobile app notifications, mobile app verification codes, email, and third-party authentication apps. This flexibility allows organizations to choose the methods that best suit their users' needs and security requirements.

2. Conditional Access Policies

Azure AD MFA can configure conditional access policies, allowing organizations to define specific conditions under which MFA (is required), once applied to an organization, on the user location, device trust, application sensitivity, and risk level. This granular control helps organizations strike a balance between security and user convenience.

Best Practices

  • Enable MFA for All Users - Implement a company-wide policy to enforce MFA for all users, regardless of their roles or privileges, because it will ensure consistent and comprehensive security across the organization.
  • Use Risk-Based Policies - Leverage Azure AD Identity Protection and its risk-based policies to dynamically adjust the level of authentication required based on the perceived risk of each sign-in attempt because it will help balance security and user experience by applying MFA only when necessary.
  • Implement Multi-Factor Authentication for Privileged Accounts - Ensure that all privileged accounts, such as administrators and IT staff, are protected with MFA. These accounts have elevated access rights and are prime targets for attackers. Enforcing MFA adds an extra layer of protection to prevent unauthorized access.

Conclusion

In this post, we have introduced the importance of cybersecurity in the cloud space due to dependence on cloud providers. After that we discussed some layers of security in Azure to gain insights about its landscape and see some tools and features available. Of course we can’t ignore the features such as Azure Active Directory Identity Protection, Azure Firewall, Azure Resource Locks, Azure Secure SQL Database Always Encrypted, Azure Key Vault and Azure AD Multi-Factor Authentication by giving an overview on each, its key features and the best practices we can apply to our organization.

Ready to go beyond native Azure tools?

While Azure provides powerful built-in security features, securing sensitive data across multi-cloud environments requires deeper visibility and control.

Request a demo with Sentra to see how our platform complements Azure by discovering, classifying, and protecting sensitive data - automatically and continuously.

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Discover Ron’s expertise, shaped by over 20 years of hands-on tech and leadership experience in cybersecurity, cloud, big data, and machine learning. As a serial entrepreneur and seed investor, Ron has contributed to the success of several startups, including Axonius, Firefly, Guardio, Talon Cyber Security, and Lightricks, after founding a company acquired by Oracle.

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Latest Blog Posts

Ariel Rimon
Ariel Rimon
Daniel Suissa
Daniel Suissa
February 16, 2026
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Min Read

How Modern Data Security Discovers Sensitive Data at Cloud Scale

How Modern Data Security Discovers Sensitive Data at Cloud Scale

Modern cloud environments contain vast amounts of data stored in object storage services such as Amazon S3, Google Cloud Storage, and Azure Blob Storage. In large organizations, a single data store can contain billions (or even tens of billions) of objects. In this reality, traditional approaches that rely on scanning every file to detect sensitive data quickly become impractical.

Full object-level inspection is expensive, slow, and difficult to sustain over time. It increases cloud costs, extends onboarding timelines, and often fails to keep pace with continuously changing data. As a result, modern data security platforms must adopt more intelligent techniques to build accurate data inventories and sensitivity models without scanning every object.

Why Object-Level Scanning Fails at Scale

Object storage systems expose data as individual objects, but treating each object as an independent unit of analysis does not reflect how data is actually created, stored, or used.

In large environments, scanning every object introduces several challenges:

  • Cost amplification from repeated content inspection at massive scale
  • Long time to actionable insights during the first scan
  • Operational bottlenecks that prevent continuous scanning
  • Diminishing returns, as many objects contain redundant or structurally identical data

The goal of data discovery is not exhaustive inspection, but rather accurate understanding of where sensitive data exists and how it is organized.

The Dataset as the Correct Unit of Analysis

Although cloud storage presents data as individual objects, most data is logically organized into datasets. These datasets often follow consistent structural patterns such as:

  • Time-based partitions
  • Application or service-specific logs
  • Data lake tables and exports
  • Periodic reports or snapshots

For example, the following objects are separate files but collectively represent a single dataset:

logs/2026/01/01/app_events_001.json

logs/2026/01/02/app_events_002.json

logs/2026/01/03/app_events_003.json

While these objects differ by date, their structure, schema, and sensitivity characteristics are typically consistent. Treating them as a single dataset enables more accurate and scalable analysis.

Analyzing Storage Structure Without Reading Every File

Modern data discovery platforms begin by analyzing storage metadata and object structure, rather than file contents.

This includes examining:

  • Object paths and prefixes
  • Naming conventions and partition keys
  • Repeating directory patterns
  • Object counts and distribution

By identifying recurring patterns and natural boundaries in storage layouts, platforms can infer how objects relate to one another and where dataset boundaries exist. This analysis does not require reading object contents and can be performed efficiently at cloud scale.

Configurable by Design

Sampling can be disabled for specific data sources, and the dataset grouping algorithm can be adjusted by the user. This allows teams to tailor the discovery process to their environment and needs.


Automatic Grouping into Dataset-Level Assets

Using structural analysis, objects are automatically grouped into dataset-level assets. Clustering algorithms identify related objects based on path similarity, partitioning schemes, and organizational patterns. This process requires no manual configuration and adapts as new objects are added. Once grouped, these datasets become the primary unit for further analysis, replacing object-by-object inspection with a more meaningful abstraction.

Representative Sampling for Sensitivity Inference

After grouping, sensitivity analysis is performed using representative sampling. Instead of inspecting every object, the platform selects a small, statistically meaningful subset of files from each dataset.

Sampling strategies account for factors such as:

  • Partition structure
  • File size and format
  • Schema variation within the dataset

By analyzing these samples, the platform can accurately infer the presence of sensitive data across the entire dataset. This approach preserves accuracy while dramatically reducing the amount of data that must be scanned.

Handling Non-Standard Storage Layouts

In some environments, storage layouts may follow unconventional or highly customized naming schemes that automated grouping cannot fully interpret. In these cases, manual grouping provides additional precision. Security analysts can define logical dataset boundaries, often supported by LLM-assisted analysis to better understand complex or ambiguous structures. Once defined, the same sampling and inference mechanisms are applied, ensuring consistent sensitivity assessment even in edge cases.

Scalability, Cost, and Operational Impact

By combining structural analysis, grouping, and representative sampling, this approach enables:

  • Scalable data discovery across millions or billions of objects
  • Predictable and significantly reduced cloud scanning costs
  • Faster onboarding and continuous visibility as data changes
  • High confidence sensitivity models without exhaustive inspection

This model aligns with the realities of modern cloud environments, where data volume and velocity continue to increase.

From Discovery to Classification and Continuous Risk Management

Dataset-level asset discovery forms the foundation for scalable classification, access governance, and risk detection. Once assets are defined at the dataset level, classification becomes more accurate and easier to maintain over time. This enables downstream use cases such as identifying over-permissioned access, detecting risky data exposure, and managing AI-driven data access patterns.

Applying These Principles in Practice

Platforms like Sentra apply these principles to help organizations discover, classify, and govern sensitive data at cloud scale - without relying on full object-level scans. By focusing on dataset-level discovery and intelligent sampling, Sentra enables continuous visibility into sensitive data while keeping costs and operational overhead under control.

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Elie Perelman
Elie Perelman
February 13, 2026
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Best Data Access Governance Tools

Best Data Access Governance Tools

Managing access to sensitive information is becoming one of the most critical challenges for organizations in 2026. As data sprawls across cloud platforms, SaaS applications, and on-premises systems, enterprises face compliance violations, security breaches, and operational inefficiencies. Data Access Governance Tools provide automated discovery, classification, and access control capabilities that ensure only authorized users interact with sensitive data. This article examines the leading platforms, essential features, and implementation strategies for effective data access governance.

Best Data Access Governance Tools

The market offers several categories of solutions, each addressing different aspects of data access governance. Enterprise platforms like Collibra, Informatica Cloud Data Governance, and Atlan deliver comprehensive metadata management, automated workflows, and detailed data lineage tracking across complex data estates.

Specialized Data Access Governance (DAG) platforms focus on permissions and entitlements. Varonis, Immuta, and Securiti provide continuous permission mapping, risk analytics, and automated access reviews. Varonis identifies toxic combinations by discovering and classifying sensitive data, then correlating classifications with access controls to flag scenarios where high-sensitivity files have overly broad permissions.

User Reviews and Feedback

Varonis

  • Detailed file access analysis and real-time protection capabilities
  • Excellent at identifying toxic permission combinations
  • Learning curve during initial implementation

BigID

  • AI-powered classification with over 95% accuracy
  • Handles both structured and unstructured data effectively
  • Strong privacy automation features
  • Technical support response times could be improved

OneTrust

  • User-friendly interface and comprehensive privacy management
  • Deep integration into compliance frameworks
  • Robust feature set requires organizational support to fully leverage

Sentra

  • Effective data discovery and automation capabilities (January 2026 reviews)
  • Significantly enhances security posture and streamlines audit processes
  • Reduces cloud storage costs by approximately 20%

Critical Capabilities for Modern Data Access Governance

Effective platforms must deliver several core capabilities to address today's challenges:

Unified Visibility

Tools need comprehensive visibility across IaaS, PaaS, SaaS, and on-premises environments without moving data from its original location. This "in-environment" architecture ensures data never leaves organizational control while enabling complete governance.

Dynamic Data Movement Tracking

Advanced platforms monitor when sensitive assets flow between regions, migrate from production to development, or enter AI pipelines. This goes beyond static location mapping to provide real-time visibility into data transformations and transfers.

Automated Classification

Modern tools leverage AI and machine learning to identify sensitive data with high accuracy, then apply appropriate tags that drive downstream policy enforcement. Deep integration with native cloud security tools, particularly Microsoft Purview, enables seamless policy enforcement.

Toxic Combination Detection

Platforms must correlate data sensitivity with access permissions to identify scenarios where highly sensitive information has broad or misconfigured controls. Once detected, systems should provide remediation guidance or trigger automated actions.

Infrastructure and Integration Considerations

Deployment architecture significantly impacts governance effectiveness. Agentless solutions connecting via cloud provider APIs offer zero impact on production latency and simplified deployment. Some platforms use hybrid approaches combining agentless scanning with lightweight collectors when additional visibility is required.

Integration Area Key Considerations Example Capabilities
Microsoft Ecosystem Native integration with Microsoft Purview, Microsoft 365, and Azure Varonis monitors Copilot AI prompts and enforces consistent policies
Data Platforms Direct remediation within platforms such as Snowflake BigID automatically enforces dynamic data masking and tagging
Cloud Providers API-based scanning without performance overhead Sentra’s agentless architecture scans environments without deploying agents

Open Source Data Governance Tools

Organizations seeking cost-effective or customizable solutions can leverage open source tools. Apache Atlas, originally designed for Hadoop environments, provides mature governance capabilities that, when integrated with Apache Ranger, support tag-based policy management for flexible access control.

DataHub, developed at LinkedIn, features AI-powered metadata ingestion and role-based access control. OpenMetadata offers a unified metadata platform consolidating information across data sources with data lineage tracking and customized workflows.

While open source tools provide foundational capabilities, metadata cataloging, data lineage tracking, and basic access controls, achieving enterprise-grade governance typically requires additional customization, integration work, and infrastructure investment. The software is free, but self-hosting means accounting for operational costs and expertise needed to maintain these platforms.

Understanding the Gartner Magic Quadrant for Data Governance Tools

Gartner's Magic Quadrant assesses vendors on ability to execute and completeness of vision. For data access governance, Gartner examines how effectively platforms define, automate, and enforce policies controlling user access to data.

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Gilad Golani
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David Stuart
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February 12, 2026
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How to Supercharge Microsoft Purview DLP and Make Copilot Safe by Fixing Labels at the Source

How to Supercharge Microsoft Purview DLP and Make Copilot Safe by Fixing Labels at the Source

For organizations invested in Microsoft 365, Purview and Copilot now sit at the center of both data protection and productivity. Purview offers rich DLP capabilities, along with sensitivity labels that drive encryption, retention, and policy. Copilot promises to unlock new value from content in SharePoint, OneDrive, Teams, and other services.

But there is a catch. Both Purview DLP and Copilot depend heavily on labels and correct classification.

If labels are missing, wrong, or inconsistent, then:

  • DLP rules fire in the wrong places (creating false positives) or miss critical data (worse!).
  • Copilot accesses content you never intended it to see and can inadvertently surface it in responses.

In many environments, that’s exactly what’s happening. Labels are applied manually. Legacy content, exports from non‑Microsoft systems, and AI‑ready datasets live side by side with little or no consistent tagging. Purview has powerful controls, it just doesn’t always have the accurate inputs it needs.

The fastest way to boost performance of Purview DLP and make Copilot safe is to fix labels at the source using a DSPM platform, then let Microsoft’s native controls do the work they’re already good at.

The limits of M365‑only classification

Purview’s built-in classifiers understand certain patterns and can infer sensitivity from content inside the Microsoft 365 estate. That can be useful, but it doesn’t solve two big problems.

First, PHI, PCI, PII, and IP often originate in systems outside of M365; core banking platforms, claims systems, Snowflake, Databricks, and third‑party SaaS applications. When that data is exported or synced into SharePoint, OneDrive, or Teams, it often arrives without accurate labels.

Second, even within M365, there are years of accumulated documents, emails, and chat history that have never been systematically classified. Applying labels retroactively is time‑consuming and error‑prone if you rely on manual tagging or narrow content rules. And once there, without contextual analysis and deeper understanding of the unstructured files in which the data lives, it becomes extremely difficult to apply precise sensitivity labels.When you add Copilot (or any AI agent/assistant) into the mix, any mislabeling or blind spots in classification can quickly turn into AI‑driven data exposure. The stakes are higher, and so is the need for a more robust foundation.

Using DSPM to fix labels at the source

A DSPM platform like Sentra plugs into your environment at a different layer. It connects not just to Microsoft 365, but also to cloud providers, data warehouses, SaaS applications, collaboration tools, and AI platforms. It then builds a cross‑environment view of where sensitive data lives and what it contains, based on multi‑signal, AI‑assisted classification that’s tuned to your business context.

Once it has that view, Sentra can automatically apply or correct Microsoft Purview Information Protection (MPIP) labels across M365 content and, where appropriate, back into other systems. Instead of relying on spotty manual tagging and local heuristics, you get labels that reflect a consistent, enterprise‑wide understanding of sensitivity.

Supercharging Microsoft Purview DLP with Sentra



Those labels become the language that Purview DLP, encryption, retention, and Copilot controls understand. You are effectively giving Microsoft’s native tools a richer, more accurate map of your data, enabling them to confidently apply appropriate controls and streamline remediations.

Making Purview DLP work smarter

When labels are trustworthy, Purview DLP policies become easier to design and maintain. Rather than creating sprawling rule sets that combine patterns, locations, and exceptions, you can express policies in simple, label‑centric terms:

  • “Encrypt and allow PHI sent to approved partners; block PHI sent anywhere else.”
  • “Block Highly Confidential documents shared with external accounts; prompt for justification when Internal documents leave the tenant.”

DSPM’s role is to ensure that content carrying PHI or other regulated data is actually labeled as such, whether it started life in M365 or came from elsewhere. Purview then enforces DLP based on those labels, with far fewer false positives and far fewer edge cases. During rollout, you can run new label‑driven policies in audit mode to observe how they would behave, work with business stakeholders to adjust where necessary, and then move the most critical rules into full enforcement.

Keeping Copilot inside the guardrails

Copilot adds another dimension to this story. By design, it reads and reasons over large swaths of your content, then generates responses or summaries based on that content. If you don’t control what Copilot can see, it may surface PHI in a chat about scheduling, or include sensitive IP in a generic project update.

Here again, labels should be the control plane. Once DSPM has ensured that sensitive content is labeled accurately and consistently, you can use those labels to govern Copilot:

  • Limit Copilot’s access to certain labels or sites, especially those holding PHI, PCI, or trade secrets.
  • Restrict certain operations (such as summarization or sharing) when output would be based on Highly Confidential content.
  • Exclude specific labeled datasets from Copilot’s index entirely.

Because DSPM also tracks where labeled data moves, it can alert you when sensitive content is copied into a location with different Copilot rules. That gives you an opportunity to remediate before an incident, rather than discovering the issue only after a problematic AI response.

A practical path for Microsoft‑centric organizations

For organizations that have standardized on Microsoft 365, the message is not “replace Purview” or “turn off Copilot.” It’s to recognize that Purview and Copilot need a stronger foundation of data intelligence to act safely and predictably.

That foundation comes from pairing DSPM and auto‑labeling with Purview’s native capabilities, which combined enable you to:

  1. Discover and classify sensitive data across your full estate, including non‑Microsoft sources.
  2. Auto‑apply MPIP labels so that M365 content is tagged accurately and consistently.
  3. Simplify DLP and Copilot policies to be label‑driven rather than pattern‑driven.
  4. Iterate in audit mode before expanding enforcement.

Once labels are fixed at the source, you can lean on Purview DLP and Copilot with much more confidence. You’ll spend less time chasing noisy alerts and unexpected AI behavior, and more time using the Microsoft ecosystem the way it was intended: as a powerful, integrated platform for secure productivity.

Ready to supercharge Purview DLP and make M365 Copilot safe by fixing labels at the source? Schedule a Sentra demo.

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