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Data Protection and Classification in Microsoft 365

January 28, 2025
5
 Min Read
Data Security
Author Image
Yair Cohen
Co-Founder and VP Product
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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.

Why data classification is important
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.

Microsoft Defender for Cloud - data sensitivity classification
Figure 5: Microsoft Defender data sensitivity classification (Source: Microsoft)

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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.

How DLP policies work
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:

  1. 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.
  2. Automate labeling to minimize errors and quickly capture new datasets.
  3. Establish and enforce data use policies and guardrails automatically to reduce risks of data breaches, compliance failures, and insider threat risks. 
  4. 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.
  5. Define context-appropriate DLP policies based on your business needs; factoring in remote work, ease of collaboration, regional compliance standards, etc.
  6. Apply encryption to safeguard data inside and outside your organization.
  7. 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.
  8. Create audit trails of user activity around data and maintain version histories to prevent and track data loss.
  9. Follow the 3-2-1 backup rule: keep three copies of your data, store two on different media, and one offsite.
  10. Leverage the full suite of Microsoft 365 tools to monitor sensitive data, detect real-time threats, and secure information effectively.
  11. Promptly resolve detected risks to mitigate attacks early.
  12. 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:

  1. 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.
  2. 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.
  3. 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.
  4. 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.
  5. Data Access Governance: Sentra adds to Purview’s data catalog by including metadata on users and applications with data access permissions, ensuring better governance.
  6. Automated Privacy Assessments: Sentra automates privacy evaluations aligned with frameworks like GDPR and CCPA, seamlessly integrating them into Purview’s data catalog.
  7. 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.

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

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Latest Blog Posts

Ron Reiter
Ron Reiter
January 15, 2026
8
Min Read

Cloud Vulnerability Management: Best Practices, Tools & Frameworks

Cloud Vulnerability Management: Best Practices, Tools & Frameworks

Cloud environments evolve continuously - new workloads, APIs, identities, and services are deployed every day. This constant change introduces security gaps that attackers can exploit if left unmanaged.

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Cloud vulnerability management helps organizations identify, prioritize, and remediate security weaknesses across cloud infrastructure, workloads, and services to reduce breach risk, protect sensitive data, and maintain compliance.

This guide explains what cloud vulnerability management is, why it matters in 2026, common cloud vulnerabilities, best practices, tools, and more.

What is Cloud Vulnerability Management?

Cloud vulnerability management is a proactive approach to identifying and mitigating security vulnerabilities within your cloud infrastructure, enhancing cloud data security. It involves the systematic assessment of cloud resources and applications to pinpoint potential weaknesses that cybercriminals might exploit. By addressing these vulnerabilities, you reduce the risk of data breaches, service interruptions, and other security incidents that could have a significant impact on your organization.

Why Cloud Vulnerability Management Matters in 2026

Cloud vulnerability management matters in 2026 because cloud environments are more dynamic, interconnected, and data-driven than ever before, making traditional, periodic security assessments insufficient. Modern cloud infrastructure changes continuously as teams deploy new workloads, APIs, and services across multi-cloud and hybrid environments. Each change can introduce new security vulnerabilities, misconfigurations, or exposed attack paths that attackers can exploit within minutes.

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Several trends are driving the increased importance of cloud vulnerability management in 2026:

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  • Accelerated cloud adoption: Organizations continue to move critical workloads and sensitive data into IaaS, PaaS, and SaaS environments, significantly expanding the attack surface.
  • Misconfigurations remain the leading risk: Over-permissive access policies, exposed storage services, and insecure APIs are still the most common causes of cloud breaches.
  • Shorter attacker dwell time: Threat actors now exploit newly exposed vulnerabilities within hours, not weeks, making continuous vulnerability scanning essential.
  • Increased regulatory pressure: Compliance frameworks such as GDPR, HIPAA, SOC 2, and emerging AI and data regulations require continuous risk assessment and documentation.
  • Data-centric breach impact: Cloud breaches increasingly focus on accessing sensitive data rather than infrastructure alone, raising the stakes of unresolved vulnerabilities.

In this environment, cloud vulnerability management best practices, including continuous scanning, risk-based prioritization, and automated remediation - are no longer optional. They are a foundational requirement for maintaining cloud security, protecting sensitive data, and meeting compliance obligations in 2026.

Common Vulnerabilities in Cloud Security

Before diving into the details of cloud vulnerability management, it's essential to understand the types of vulnerabilities that can affect your cloud environment. Here are some common vulnerabilities that private cloud security experts encounter:

Vulnerable APIs

Application Programming Interfaces (APIs) are the backbone of many cloud services. They allow applications to communicate and interact with the cloud infrastructure. However, if not adequately secured, APIs can be an entry point for cyberattacks. Insecure API endpoints, insufficient authentication, and improper data handling can all lead to vulnerabilities.


# Insecure API endpoint example
import requests

response = requests.get('https://example.com/api/v1/insecure-endpoint')
if response.status_code == 200:
    # Handle the response
else:
    # Report an error

Misconfigurations

Misconfigurations are one of the leading causes of security breaches in the cloud. These can range from overly permissive access control policies to improperly configured firewall rules. Misconfigurations may leave your data exposed or allow unauthorized access to resources.


# Misconfigured firewall rule
- name: allow-http
  sourceRanges:
    - 0.0.0.0/0 # Open to the world
  allowed:
    - IPProtocol: TCP
      ports:
        - '80'

Data Theft or Loss

Data breaches can result from poor data handling practices, encryption failures, or a lack of proper data access controls. Stolen or compromised data can lead to severe consequences, including financial losses and damage to an organization's reputation.


// Insecure data handling example
import java.io.File;
import java.io.FileReader;

public class InsecureDataHandler {
    public String readSensitiveData() {
        try {
            File file = new File("sensitive-data.txt");
            FileReader reader = new FileReader(file);
            // Read the sensitive data
            reader.close();
        } catch (Exception e) {
            // Handle errors
        }
    }
}

Poor Access Management

Inadequate access controls can lead to unauthorized users gaining access to your cloud resources. This vulnerability can result from over-privileged user accounts, ineffective role-based access control (RBAC), or lack of multi-factor authentication (MFA).


# Overprivileged user account
- members:
    - user:johndoe@example.com
  role: roles/editor

Non-Compliance

Non-compliance with regulatory standards and industry best practices can lead to vulnerabilities. Failing to meet specific security requirements can result in fines, legal actions, and a damaged reputation.


Non-compliance with GDPR regulations can lead to severe financial penalties and legal consequences.

Understanding these vulnerabilities is crucial for effective cloud vulnerability management. Once you can recognize these weaknesses, you can take steps to mitigate them.

Cloud Vulnerability Assessment and Mitigation

Now that you're familiar with common cloud vulnerabilities, it's essential to know how to mitigate them effectively. Mitigation involves a combination of proactive measures to reduce the risk and the potential impact of security issues.

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Here are some steps to consider:

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  • Regular Cloud Vulnerability Scanning: Implement a robust vulnerability scanning process that identifies and assesses vulnerabilities within your cloud environment. Use automated tools that can detect misconfigurations, outdated software, and other potential weaknesses.
  • Access Control: Implement strong access controls to ensure that only authorized users have access to your cloud resources. Enforce the principle of least privilege, providing users with the minimum level of access necessary to perform their tasks.
  • Configuration Management: Regularly review and update your cloud configurations to ensure they align with security best practices. Tools like Infrastructure as Code (IaC) and Configuration Management Databases (CMDBs) can help maintain consistency and security.
  • Patch Management: Keep your cloud infrastructure up to date by applying patches and updates promptly. Vulnerabilities in the underlying infrastructure can be exploited by attackers, so staying current is crucial.
  • Encryption: Use encryption to protect data both at rest and in transit. Ensure that sensitive information is adequately encrypted, and use strong encryption protocols and algorithms.
  • Monitoring and Incident Response: Implement comprehensive monitoring and incident response capabilities to detect and respond to security incidents in real time. Early detection can minimize the impact of a breach.
  • Security Awareness Training: Train your team on security best practices and educate them about potential risks and how to identify and report security incidents.

Key Features of Cloud Vulnerability Management

Effective cloud vulnerability management provides several key benefits that are essential for securing your cloud environment. Let's explore these features in more detail:

Better Security

Cloud vulnerability management ensures that your cloud environment is continuously monitored for vulnerabilities. By identifying and addressing these weaknesses, you reduce the attack surface and lower the risk of data breaches or other security incidents. This proactive approach to security is essential in an ever-evolving threat landscape.


# Code snippet for vulnerability scanning
import security_scanner

# Initialize the scanner
scanner = security_scanner.Scanner()

# Run a vulnerability scan
scan_results = scanner.scan_cloud_resources()

Cost-Effective

By preventing security incidents and data breaches, cloud vulnerability management helps you avoid potentially significant financial losses and reputational damage. The cost of implementing a vulnerability management system is often far less than the potential costs associated with a security breach.


# Code snippet for cost analysis
def calculate_potential_cost_of_breach():
    # Estimate the cost of a data breach
    return potential_cost

potential_cost = calculate_potential_cost_of_breach()
if potential_cost > cost_of vulnerability management:
    print("Investing in vulnerability management is cost-effective.")
else:
    print("The cost of vulnerability management is justified by potential savings.")

Highly Preventative

Vulnerability management is a proactive and preventive security measure. By addressing vulnerabilities before they can be exploited, you reduce the likelihood of a security incident occurring. This preventative approach is far more effective than reactive measures.


# Code snippet for proactive security
import preventive_security_module

# Enable proactive security measures
preventive_security_module.enable_proactive_measures()

Time-Saving

Cloud vulnerability management automates many aspects of the security process. This automation reduces the time required for routine security tasks, such as vulnerability scanning and reporting. As a result, your security team can focus on more strategic and complex security challenges.


# Code snippet for automated vulnerability scanning
import automated_vulnerability_scanner

# Configure automated scanning schedule
automated_vulnerability_scanner.schedule_daily_scan()

Steps in Implementing Cloud Vulnerability Management

Implementing cloud vulnerability management is a systematic process that involves several key steps. Let's break down these steps for a better understanding:

Identification of Issues

The first step in implementing cloud vulnerability management is identifying potential vulnerabilities within your cloud environment. This involves conducting regular vulnerability scans to discover security weaknesses.


# Code snippet for identifying vulnerabilities
import vulnerability_identifier

# Run a vulnerability scan to identify issues
vulnerabilities = vulnerability_identifier.scan_cloud_resources()

Risk Assessment

After identifying vulnerabilities, you need to assess their risk. Not all vulnerabilities are equally critical. Risk assessment helps prioritize which vulnerabilities to address first based on their potential impact and likelihood of exploitation.


# Code snippet for risk assessment
import risk_assessment

# Assess the risk of identified vulnerabilities
priority_vulnerabilities = risk_assessment.assess_risk(vulnerabilities)

Vulnerabilities Remediation

Remediation involves taking action to fix or mitigate the identified vulnerabilities. This step may include applying patches, reconfiguring cloud resources, or implementing access controls to reduce the attack surface.


# Code snippet for vulnerabilities remediation
import remediation_tool

# Remediate identified vulnerabilities
remediation_tool.remediate_vulnerabilities(priority_vulnerabilities)

Vulnerability Assessment Report

Documenting the entire vulnerability management process is crucial for compliance and transparency. Create a vulnerability assessment report that details the findings, risk assessments, and remediation efforts.


# Code snippet for generating a vulnerability assessment report
import report_generator

# Generate a vulnerability assessment report
report_generator.generate_report(priority_vulnerabilities)

Re-Scanning

The final step is to re-scan your cloud environment periodically. New vulnerabilities may emerge, and existing vulnerabilities may reappear. Regular re-scanning ensures that your cloud environment remains secure over time.


# Code snippet for periodic re-scanning
import re_scanner

# Schedule regular re-scans of your cloud resources
re_scanner.schedule_periodic_rescans()

By following these steps, you establish a robust cloud vulnerability management program that helps secure your cloud environment effectively.

Challenges with Cloud Vulnerability Management

While cloud vulnerability management offers many advantages, it also comes with its own set of challenges. Some of the common challenges include:

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Challenge Description
Scalability As your cloud environment grows, managing and monitoring vulnerabilities across all resources can become challenging.
Complexity Cloud environments can be complex, with numerous interconnected services and resources. Understanding the intricacies of these environments is essential for effective vulnerability management.
Patch Management Keeping cloud resources up to date with the latest security patches can be a time-consuming task, especially in a dynamic cloud environment.
Compliance Ensuring compliance with industry standards and regulations can be challenging, as cloud environments often require tailored configurations to meet specific compliance requirements.
Alert Fatigue With a constant stream of alerts and notifications from vulnerability scanning tools, security teams can experience alert fatigue, potentially missing critical security issues.

Cloud Vulnerability Management Best Practices

To overcome the challenges and maximize the benefits of cloud vulnerability management, consider these best practices:

  • Automation: Implement automated vulnerability scanning and remediation processes to save time and reduce the risk of human error.
  • Regular Training: Keep your security team well-trained and updated on the latest cloud security best practices.
  • Scalability: Choose a vulnerability management solution that can scale with your cloud environment.
  • Prioritization: Use risk assessments to prioritize the remediation of vulnerabilities effectively.
  • Documentation: Maintain thorough records of your vulnerability management efforts, including assessment reports and remediation actions.
  • Collaboration: Foster collaboration between your security team and cloud administrators to ensure effective vulnerability management.
  • Compliance Check: Regularly verify your cloud environment's compliance with relevant standards and regulations.

Tools to Help Manage Cloud Vulnerabilities

To assist you in your cloud vulnerability management efforts, there are several tools available. These tools offer features for vulnerability scanning, risk assessment, and remediation.

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Here are some popular options:

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1. Sentra: Sentra is a cloud-based data security platform that provides visibility, assessment, and remediation for data security. It can be used to discover and classify sensitive data, analyze data security controls, and automate alerts in cloud data stores, IaaS, PaaS, and production environments.

2. Tenable Nessus: A widely-used vulnerability scanner that provides comprehensive vulnerability assessment and prioritization.

3. Qualys Vulnerability Management: Offers vulnerability scanning, risk assessment, and compliance management for cloud environments.

4. AWS Config: Amazon Web Services (AWS) provides AWS Config, as well as other AWS cloud security tools, to help you assess, audit, and evaluate the configurations of your AWS resources.

5. Azure Security Center: Microsoft Azure's Security Center offers Azure Security tools for continuous monitoring, threat detection, and vulnerability assessment.

6. Google Cloud Security Scanner: A tool specifically designed for Google Cloud Platform that scans your applications for vulnerabilities.

7. OpenVAS: An open-source vulnerability scanner that can be used to assess the security of your cloud infrastructure.

Choosing the right tool depends on your specific cloud environment, needs, and budget. Be sure to evaluate the features and capabilities of each tool to find the one that best fits your requirements.

Conclusion

In an era of increasing cyber threats and data breaches, cloud vulnerability management is a vital practice to secure your cloud environment. By understanding common cloud vulnerabilities, implementing effective mitigation strategies, and following best practices, you can significantly reduce the risk of security incidents. Embracing automation and utilizing the right tools can streamline the vulnerability management process, making it a manageable and cost-effective endeavor.

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Remember that security is an ongoing effort, and regular vulnerability scanning, risk assessment, and remediation are crucial for maintaining the integrity and safety of your cloud infrastructure. With a robust cloud vulnerability management program in place, you can confidently leverage the benefits of the cloud while keeping your data and assets secure.

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See how Sentra identifies cloud vulnerabilities that put sensitive data at risk.

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Nikki Ralston
Nikki Ralston
David Stuart
David Stuart
December 23, 2025
3
Min Read

Securing Sensitive Data in Google Cloud: Sentra Data Security for Modern Cloud and AI Environments

Securing Sensitive Data in Google Cloud: Sentra Data Security for Modern Cloud and AI Environments

As organizations scale their use of Google Cloud, sensitive data is rapidly expanding across cloud storage, data lakes, and analytics platforms, often without clear visibility or consistent control. Native cloud security tools focus on infrastructure and configuration risk, but they do not provide a reliable understanding of what sensitive data actually exists inside cloud environments, or how that data is being accessed and used.

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Sentra secures Google Cloud by delivering deep, AI-driven data discovery and classification across cloud-native services, unstructured data stores, and shared environments. With continuous visibility into where sensitive data resides and how exposure evolves over time, security teams can accurately assess real risk, enforce data governance, and reduce the likelihood of data leaks, without slowing cloud adoption.

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As data extends into Google Workspace and powers Gemini AI, Sentra ensures sensitive information remains governed and protected across collaboration and AI workflows. When integrated with Cloud Security Posture Management (CSPM) solutions, Sentra enriches cloud posture findings with trusted data context, transforming cloud security signals into prioritized, actionable insight based on actual data exposure.

The Challenge:
Cloud, Collaboration, and AI Without Data Context

Modern enterprises face three converging challenges:

  • Massive data sprawl across cloud infrastructure, SaaS collaboration tools, and data lakes
  • Unstructured data dominance, representing ~80% of enterprise data and the hardest to classify
  • AI systems like Gemini that ingest, transform, and generate sensitive data at scale

While CSPMs, like Wiz, excel at identifying misconfigurations, attack paths, and identity risk, they cannot determine what sensitive data actually exists inside exposed resources. Lightweight or native DSPM signals lack the accuracy and depth required to support confident risk decisions.

Security teams need more than posture - they need data truth.

Data Security Built for the Google Ecosystem

Sentra secures sensitive data across Google Cloud, Google Workspace, and AI-driven environments with accuracy, scale, and control -going beyond visibility to actively reduce data risk.

Key Sentra Capabilities

  • AI-Driven Data Discovery & Classification
     Precisely identifies PII, PCI, credentials, secrets, IP, and regulated data across structured and unstructured sources—so teams can trust the results.
  • Best-in-Class Unstructured Data Coverage
     Accurately classifies long-form documents and free text, addressing the largest source of enterprise data risk.
  • Petabyte-Scale, High-Performance Scanning
     Fast, efficient scanning designed for cloud and data lake scale without operational disruption.
  • Unified, Agentless Coverage
     Consistent visibility and classification across Google Cloud, Google Workspace, data lakes, SaaS, and on-prem.
  • Enabling Intelligent Data Loss Prevention (DLP)
     Data-aware controls prevent oversharing, public exposure, and misuse—including in AI workflows—driven by accurate classification, not static rules.‍
  • Continuous Risk Visibility
    Tracks where sensitive data lives and how exposure changes over time, enabling proactive governance and faster response.

Strengthening Security Across Google Cloud & Workspace

Google Cloud

Sentra enhances Google Cloud security by:

  • Discovering and classifying sensitive data in GCS, BigQuery, and data lakes
  • Identifying overexposed and publicly accessible sensitive data
  • Detecting toxic combinations of sensitive data and risky configurations
  • Enabling policy-driven governance aligned to compliance and risk tolerance

Google Workspace

Sentra secures the largest source of unstructured data by:

  • Classifying sensitive content in Docs, Sheets, Drive, and shared files
  • Detecting oversharing and external exposure
  • Identifying shadow data created through collaboration
  • Supporting audit and compliance with clear reporting

Enabling Secure and Responsible Gemini AI

Gemini AI introduces a new class of data risk. Sensitive information is no longer static, it is continuously ingested and generated by AI systems.

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Sentra enables secure and responsible AI adoption by:

  • Providing visibility into what sensitive data feeds AI workflows
  • Preventing regulated or confidential data from entering AI systems
  • Supporting governance policies for responsible AI use
  • Reducing the risk of AI-driven data leakage

Wiz + Sentra: Comprehensive Cloud and Data Security

Wiz identifies where cloud risk exists.
Sentra determines what data is actually at risk.

Together, Sentra + Wiz Deliver:

  • Enrichment of Wiz findings with accurate, context-rich data classification
  • Detection of real exposure, not just theoretical misconfiguration
  • Better alert prioritization based on business impact
  • Clear, defensible risk reporting for executives and boards

Security teams add Sentra because Wiz alone is not enough to accurately assess data risk at scale, especially for unstructured and AI-driven data.

Business Outcomes

With Sentra securing data across Google Cloud, Google Workspace, and Gemini AI—and enhancing Wiz—organizations achieve:

  • Reduced enterprise risk through data-driven prioritization
  • Improved compliance readiness beyond minimum regulatory requirements
  • Higher SOC efficiency with less noise and faster response
  • Confident AI adoption with enforceable governance
  • Clearer executive and board-level risk visibility

“Wiz shows us cloud risk. Sentra shows us whether that risk actually impacts sensitive data. Together, they give us confidence to move fast with Google and Gemini without losing control.”
— CISO, Enterprise Organization

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As cloud, collaboration, and AI converge, security leaders must go beyond infrastructure-only security. Sentra provides the data intelligence layer that makes Google Cloud security stronger, Google Workspace safer, Gemini AI responsible, and Wiz actionable.

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Sentra helps organizations secure what matters most, their critical data.

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Dean Taler
Dean Taler
September 16, 2025
5
Min Read
Compliance

How to Write an Effective Data Security Policy

How to Write an Effective Data Security Policy

Introduction: Why Writing Good Policies Matters

In modern cloud and AI-driven environments, having security policies in place is no longer enough. The quality of those policies directly shapes your ability to prevent data exposure, reduce noise, and drive meaningful response. A well-written policy helps to enforce real control and provides clarity in how to act. A poorly written one, on the other hand, fuels alert fatigue, confusion, or worse - blind spots.

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This article explores how to write effective, low-noise, action-oriented security policies that align with how data is actually used.

What Is a Data Security Policy?

A data security policy is a set of rules that defines how your organization handles sensitive data. It specifies who can access what information, under what conditions, and what happens when those rules are violated. But here's the key difference: a good data security policy isn't just a document that sits in a compliance folder. It's an active control that detects risky behavior and triggers specific responses. While many organizations write policies that sound impressive but create endless alerts, effective policies target real risks and drive meaningful action. The goal isn't to monitor everything, it's to catch the activities that actually matter and respond quickly when they happen.

What Makes a Data Security Policy “Good”?

Before you begin drafting, ask yourself: what problem is this policy solving, and why does it matter? 

A good data security policy isn’t just a technical rule sitting in a console, it’s a sensor for meaningful risk. It should define what activity you want to detect, under what conditions it should trigger, and who or what is in scope, so that it avoids firing on safe, expected scenarios.

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Key characteristics of an effective policy:
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  • Clear intent: protects against a well-defined risk, not a vague category of threats.
  • Actionable outcome: leads to a specific, repeatable response.
  • Low noise: triggers only on unusual or risky patterns, not normal operations.
  • Context-aware: accounts for business processes and expected data use.

💡 Tip: If you can’t explain in one sentence what you want to detect and what action should happen when it triggers, your policy isn’t ready for production.

Turning Risk Into Actionable Policy

Data security policies should always be grounded in real business risk, not just what’s technically possible to monitor. A strong policy targets scenarios that could genuinely harm the organization if left unchecked.

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Questions to ask before creating a policy:
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  • What specific behavior poses a risk to our sensitive or regulated data?
  • Who might trigger it, and why? Is it more likely to be malicious, accidental, or operational?
  • What exceptions or edge cases should be allowed without generating noise?
  • What systems will enforce it and who owns the response when it fires?

Instead of vague statements like “No access to PII”, write with precision:

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“Block and alert on external sharing of customer PII from corporate cloud storage to any domain not on the approved partner list, unless pre-approved via the security exception process.”
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Recommendations:
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  • Treat policies like code - start them in monitor-only mode.
  • Test both sides: validate true positives (catching risky activity) and avoid false positives (triggering on normal behavior).

💡 Tip: The best policies are precise enough to detect real risks, but tested enough to avoid drowning teams in noise.

A Good Data Security Policy Should Drive Action

Policies are only valuable if they lead to a decision or action. Without a clear owner or remediation process, alerts quickly become noise. Every policy should generate an alert that leads to accountability.
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Questions to ask:
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  • Who owns the alert?
  • What should happen when it fires?
  • How quickly should it be resolved?

💡 Tip: If no one is responsible for acting on a policy’s alerts, it’s not a policy — it’s background noise.

Don’t Ignore the Noise

When too many alerts fire, it’s tempting to dismiss them as an annoyance. But noisy policies are often a signal, not a mistake. Sometimes policies are too broad or poorly scoped. Other times, they point to deeper systemic risks, such as overly open sharing practices or misconfigured controls.
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Recommendations:
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  • Investigate noisy policies before silencing them.
  • Treat excess alerts as a clue to systemic risk.

💡 Tip: A noisy policy may be exposing the exact weakness you most need to fix.

Know When to Adjust or Retire a Policy

Policies must evolve as your organization, tools, and data change. A rule that made sense last year might be irrelevant or counterproductive today.
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Recommendations:
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  • Continuously align policies with evolving risks.
  • Track key metrics: how often it triggers, severity, and response actions.
  • Optimize response paths so alerts reach the right owners quickly.
  • Schedule quarterly or biannual reviews with both security and business stakeholders.

💡 Tip: The only thing worse than no policy is a stale one that everyone ignores.

Why Smart Policies Matter for Regulated Data

Data security policies aren’t just an internal safeguard, they are how compliance is enforced in practice. Regulations like GDPR, HIPAA, and PCI DSS require demonstrable control over sensitive data.

Poorly written policies generate alert fatigue, making it harder to detect real violations. Well-crafted ones reduce the risk of noncompliance, streamline audits, and improve breach response.
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Recommendations:
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  • Map each policy directly to a specific regulatory requirement.
  • Retire rules that create noise without reducing actual risk.

💡 Tip: If a policy doesn’t map to a regulation or a real risk, it’s adding effort without adding value.

Making Policy Creation Simple, Powerful, and Built for Results 

An effective solution for policy creation should make it easy to get started, provide the flexibility to adapt to your unique environment, and give you the deep data context you need to make policies that actually work. It should streamline the process so you can move quickly without sacrificing control, compliance, or clarity.

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Sentra is that solution. By combining intuitive policy building with deep data context, Sentra simplifies and strengthens the entire lifecycle of policy creation.
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With Sentra, you can:
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  • Start fast with out-of-the-box, low-noise controls.
  • Create custom policies without complexity.
  • Leverage real-time knowledge of where sensitive data lives and who has access to it.
  • Continuously tune for low noise with performance metrics.
  • Understand which regulations you can adhere to
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💡 Tip: The true value of a policy isn’t how often it triggers, it’s whether it consistently drives the right response.

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Good Policies Start with Good Visibility

The best data security policies are written by teams who know exactly where sensitive data lives, how it moves, who can access it, and what creates risk. Without that visibility, policy writing becomes guesswork. With it, enforcement becomes simple, effective, and sustainable.
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At Sentra, we believe policy creation should be driven by real data, not assumptions. If you’re ready to move from reactive alerts to meaningful control.

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