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Cloud Vulnerability Management Best Practices for 2025

November 26, 2024

Min Read

Ron Reiter

Co-Founder and CTO

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.

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. Here are some steps to consider:

Regular 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:

‍

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. Here are some popular options:

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.

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

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

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.

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

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

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. 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.

‍

If you want to learn more about how you can implement a robust cloud vulnerability management program to confidently harness the power of the cloud while keeping your data and assets secure, request a demo today.‍

‍

Ron Reiter

Co-Founder and CTO

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.

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.

‍

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.

‍

How Is Data Saved and Classified in Microsoft 365?

‍

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.

‍

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.

‍

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?

‍

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.

‍

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.

‍

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.
‍

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)

‍

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.

‍

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.

‍

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.

‍

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)

‍

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.

‍

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.

‍

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.

‍

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.

‍

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.

‍

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.

‍

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.

‍

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.

‍

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

‍

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?

‍

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?

‍

3. Posture, Risk Assessment & Threat Monitoring

‍

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?

‍

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?

‍

5. Infrastructure & Deployment

‍

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?

‍

6. Operations & Support

‍

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?

‍

7. Pricing & Licensing

‍

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?

‍

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.

‍

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.

‍

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.