What Is Data Loss Prevention? Technologies and Best Practices in 2025

What Is Data Loss Prevention (DLP)?

DLP refers to Data Loss Prevention, a set of tools and processes used by organizations to prevent sensitive data from being leaked, stolen, or misused. DLP solutions work by identifying, monitoring, and protecting sensitive data in motion, at rest, and in use across an organization’s IT infrastructure, helping to ensure compliance and improve data security.

DLP solutions typically employ a combination of policies, technologies, and processes to detect, track, and control the flow of sensitive data within an organization’s network and systems. Personally identifiable information (PII), financial data, intellectual property, trade secrets, and other proprietary or confidential information are better secured through DLP data loss prevention tools.

Why Is Data Loss Prevention Important?

There are several reasons why investing in data loss prevention security is wise and should be part of any organization’s cybersecurity strategy. Here are 8 reasons why DLP security tools are more relevant than ever.

1. Rapidly Increasing Data Volume: Businesses run on data and with the proliferation of digital technologies and the digitization of the vast majority of business processes, users and tools are generating data and organizations are having to manage the storage and accessibility of an ever-increasing volume of data.
2. Bad Actor’s Interests in Valuable Data: Criminals are targeting businesses who collect or manage sensitive and valuable information that, if compromised, can have severe upside for them and significant consequences for the organizations and individual victims.
3. Adherence to Regulations and Laws: Governments and regulatory bodies around the world have implemented stringent data protection and privacy regulations, such as the General Data Protection Regulation (GDPR) in Europe and the California Consumer Privacy Act (CCPA) in the U.S. Failure to adhere to regulations or to protect personal and sensitive data can result in significant financial penalties and immeasurable damage to reputation. Data loss prevention security helps organizations meet compliance requirements by preventing unauthorized access and data breaches.
4. Evolving Threat Landscape: Cybersecurity threats and attack methods are constantly evolving, becoming more sophisticated and targeted. Cybercriminals are increasingly motivated to gain unauthorized access to sensitive data for financial gain or to exploit it for other malicious purposes.
5. Sophisticated Threat Actors and Accidental Insider Threats: DLP technologies and cybersecurity enforcement of cyber hygiene best practices help organizations detect and mitigate these threats by monitoring data flows and implementing preventive controls.
6. Growing Acceptance of the Remote Workforce: The pandemic expedited adoption of remote work. With it came a greater dependence on cloud services which allowed workers to access, store, and share data. Separating work and life is still proving a challenge for most organizations who need to ensure that sensitive data remains secure when accessed and stored in cloud environments or accessed remotely from various devices and networks. DLP monitoring tracks data movement and enforces security policies across these diverse environments. There is an opportunity with new technology, like Venn, to improve Bring Your Own Device (BYOD) for unmanaged devices without incorporating DLP controls.
7. Reputational Damage and Financial Impact: Breaches and data loss incidents can have severe consequences for an organization’s reputation and finances. Data loss protection is necessary to safeguard customer trust, avoid negative publicity, and reduce legal liabilities. Data theft and losing the faith of the customer can lead to devastating financial losses and long-term damage to brand value. DLP minimizes the risk of data loss, mitigates the impact of breaches, and helps maintain the integrity and trustworthiness of an organization’s data.
8. Industry-specific Data Protection Requirements: Highly regulated industries, such as healthcare, finance, and legal sectors, handle highly sensitive data. They must comply with specific obligations and face unique security challenges. Tailored features and capabilities of data loss prevention security helps to address industry-specific requirements and protect the sensitive data associated with these sectors.

What Type of Data is Monitored by DLP Solutions?

1. File Content: Data formats that include the contents of files, emails, documents, or other data formats containing sensitive or privileged information. This can include personally identifiable information (PII), financial data, intellectual property, and other confidential or regulated data.
2. Contextual data: The context surrounding data, such as user behavior, data location, or data movement patterns are tracked to help determine if data handling actions are in compliance with security policies and regulations or a problem needs to be addressed.
3. Data in Motion: Network traffic monitoring is used to identify unauthorized or suspicious data transfers, whether taking place within the organization’s network or being distributed to external destinations.
4. Data at Rest: Data stored on endpoints, servers, databases, or cloud storage is scanned to identify sensitive information residing in files, databases, or structured/unstructured data repositories.

How is Data Monitored by DLP Solutions?

Various techniques and technologies are used to monitor data within a DLP solution.

• Data Analysis: Advanced algorithms, machine learning, and pattern matching techniques are used to identify and classify sensitive data. They can detect numerical patterns, such as credit card numbers and social security numbers, or specific keywords associated with sensitive information.
• Endpoint Agents: DLP software can be installed on endpoints, (ex. workstations, laptops, tablets and smartphones), to monitor data activity on those devices. Endpoint agents provide real-time visibility and control over data movement as well as enforce security policies.
• Network Monitoring: Network traffic examination and deep packet inspection (DPI) techniques are used to analyze data packets and identify sensitive information crossing the network. IT teams can examine email communications, web uploads, file transfers, and other network activities through the solution.
• Integration with Data Repositories: Integration with data repositories, such as databases or file servers, allows scanning and analysis of data at rest. This allows organizations to identify sensitive information and apply appropriate security measures.

How Does DLP Fit Into a Cybersecurity Strategy?

DLP solutions enable organizations to take various actions based on policy violations or suspicious activities. They generate alerts and notifications when potential data breaches or policy violations are detected. These alerts can be sent to security administrators, compliance officers, IT teams, or other designated personnel for further investigation and response.

DLP solutions can trigger incident response processes when a data leak or violation occurs. This may involve quarantining or blocking data, encrypting sensitive content, or terminating suspicious network connections to prevent further data loss.

By having real-time feedback, users can be educated and made aware when they engage in potentially risky actions which helps promote security awareness and encourages them to make better data handling choices.

Policy enforcement helps prevent or block certain actions, such as unauthorized data transfers, access to restricted information, or sharing sensitive data through unapproved channels, which is enabled through DLP solutions.

Understanding the Data Loss Problem: Three Types of Data Loss

Now that we have reviewed the key elements of DLP elements, let’s take a step back and understand how data loss happens. The primary types of data loss are data breach, data leak, and data exfiltration.

Data Breach

A data breach occurs when unauthorized individuals gain access to protected or sensitive data. This often results from security vulnerabilities, such as weak passwords, unpatched systems, or successful phishing attacks. Breaches may expose personal information, financial records, intellectual property, or other confidential data.

Data breaches are typically large-scale events and may involve external attackers, insiders, or a combination of both. The consequences include regulatory penalties, loss of customer trust, and reputational harm.

DLP helps prevent breaches by enforcing access controls, monitoring user activity, and identifying abnormal behavior. It also helps detect unauthorized attempts to access or move sensitive data, enabling organizations to respond before data is exposed.

Data Leakage

Data leakage refers to the unintentional or unauthorized transmission of sensitive data to external parties or unapproved internal recipients. Unlike breaches, leaks often occur passively, without a targeted attack. Examples include emailing sensitive files to the wrong person or using unsecured cloud storage.

Leaks can happen via email, messaging apps, file-sharing platforms, or even printed documents. These incidents are often the result of human error or insufficient security policies.

DLP solutions address leakage by scanning data in motion and enforcing rules that block or flag risky actions, such as attaching sensitive files to personal emails or uploading confidential data to unauthorized services.

Data Exfiltration

Data exfiltration involves the deliberate and unauthorized transfer of data from an organization’s network to an external destination. This is typically carried out by malicious insiders or external attackers who have breached the system.

Exfiltration can be performed through various channels, including email, cloud apps, removable media, or covert command-and-control systems. It is often stealthy, with attackers attempting to evade detection by fragmenting data or using encryption.

DLP combats exfiltration by analyzing data flow patterns, applying content-aware policies, and blocking suspicious transfers. It also uses behavioral analysis to detect anomalies, such as large data uploads or access from atypical locations or devices.

Main Causes of Data Loss

Data loss can occur for a variety of reasons, both intentional and accidental. Understanding the primary causes helps organizations design effective DLP strategies.

• Human error is the leading cause of data loss. Common examples include sending emails to the wrong recipients, accidentally deleting critical files, or misconfiguring access controls. These errors are often due to a lack of training or oversight.
• Malicious insiders, employees or contractors who misuse their access privileges, can intentionally leak or steal sensitive information. These insider threats are difficult to detect because they come from trusted individuals.
• External attacks from cybercriminals, such as phishing, ransomware, or data exfiltration through malware, are another major cause. Attackers exploit vulnerabilities to gain unauthorized access and extract data.
• Lost or stolen devices, like laptops, phones, or USB drives, pose risks when they contain unencrypted sensitive data. Physical security gaps can lead to major breaches.
• Software and system failures can also lead to data loss. These include corrupted files, failed backups, and system crashes that result in lost or inaccessible data.
• Poor data management practices, such as lack of version control, inadequate backups, or unclassified sensitive data, create opportunities for data loss due to misplacement or exposure.

What Is a DLP Policy?

A DLP policy is a set of predefined rules and conditions that determine how sensitive data should be handled, accessed, and protected within an organization. It defines what constitutes sensitive information, how to identify it, and what actions to take when that data is accessed or moved.

Policies typically include conditions based on data type (e.g., credit card numbers, health records), data location (e.g., cloud storage, endpoints), and user behavior (e.g., large file transfers, access from untrusted devices). When these conditions are met, the policy can trigger actions such as blocking, encrypting, alerting, or logging the event.

Effective DLP policies are aligned with business needs and regulatory requirements. They should be regularly updated to reflect changes in data types, business processes, and threat landscapes. Customization by user role and risk level ensures policies are both secure and operationally practical.

Types of DLP Solutions

There are three types of DLP Prevention solutions: Network, Endpoint, and Cloud.

Endpoint DLP

Endpoint DLP focuses on protecting data on workstations, laptops, or mobile devices. It involves monitoring and controlling data activity on these devices to prevent data loss, theft, or unauthorized access.

Endpoint DLP solutions are typically installed as software agents on endpoints, providing visibility and control over data in use.

Key features of Endpoint DLP include:

1. Data Activity Monitoring: Monitoring file access, printing, copying, or transferring of data to detect and prevent unauthorized or suspicious data handling actions.
2. Device Control: Enforcing policies that restrict or control the use of external devices (ex. USB drives or external hard drives) to prevent data exfiltration.
3. Application Control: Restricting the use of certain applications or monitoring application behavior to prevent data leaks through unauthorized or vulnerable applications.
4. User Behavior Analytics: Incorporating user behavior analytics to identify suspicious anomalies or patterns indicative of insider threats.

Network DLP

Network DLP focuses on monitoring and securing data as it moves across a network infrastructure. It involves inspecting network traffic, analyzing data packets, and enforcing security policies to prevent unauthorized data transfers or leaks.

Network DLP solutions typically employ deep packet inspection (DPI) techniques to examine the content and context of data in motion.

Key features of Network DLP include:

1. Network Traffic Monitoring: Monitoring network traffic to detect and analyze data transfers.
2. Policy Enforcement: Applying predefined security policies to identify sensitive information and enforce restrictions on data movement, including blocking or encrypting data.
3. Intrusion Detection: Identifying and preventing external threats attempting to exploit vulnerabilities in the network to exfiltrate sensitive data.
4. Network Integration: Integrating a DLP with network infrastructure, (ex. firewalls, routers, or proxies), to implement real-time data monitoring and control.

Cloud DLP

Cloud DLP focuses on protecting sensitive data stored, accessed, or shared within cloud environments and services. It helps organizations maintain control and visibility of data in cloud applications, platforms, or storage repositories.

Key features of Cloud DLP include:

1. Cloud Application Integration: Integrating with cloud platforms and applications, (ex. cloud storage services, collaboration tools, or Software-as-a-Service (SaaS) applications), to monitor and protect data within these environments.
2. Data Classification and Policy Enforcement: Applying data classification techniques to identify sensitive information within cloud repositories. Enforcing security policies to control data access, sharing, and storage within cloud services.
3. Cloud Storage Monitoring: Monitoring data activity within cloud storage, including uploads, downloads, and sharing, to detect and prevent unauthorized data transfers or leaks.

How DLP Helps Keep Organizations Secure

DLP for Regulated Industry Compliance

DLP software can help ensure compliance with industry regulations. An organization’s failure to comply can result in severe penalties and legal consequences. An example of maintaining HIPAA compliance using DLP tools is ensuring protected patient information is securely handled. The tools may be used for:

• Monitoring outbound emails containing patient records and preventing unauthorized sharing of sensitive data.
• Detecting attempts to copy patient data onto USB drives or other external devices, enforcing policies to prevent data exfiltration.
• Applying encryption to sensitive data at rest and in transit to maintain HIPAA compliance.

An example of maintaining GDPR compliance using DLP tools is ensuring a multinational company ensures data isn’t misused. The tools may be used for:

• Monitoring data uploads and storage within the cloud service, scanning for personally identifiable information (PII) or other sensitive data.
• Applying data classification techniques to identify and tag sensitive data.
• Enforcing policies that control access permissions, data sharing, and retention periods within the cloud storage environment.

DLP for Intellectual Property (IP)

IP Protection for trade secrets or sensitive information is critical for protecting the investments an organization has made. Safeguarding data from theft or leaks is crucial to maintaining a competitive advantage. An example of using DLP tools to help protect IP is safeguarding proprietary source code from unauthorized access. DLP tools can be used for:

• Monitoring and preventing unauthorized transfers of source code through email or file-sharing platforms.
• Detecting and blocking attempts to copy source code to external devices or cloud storage.
• Applying access controls to restrict access to the source code repository to authorized individuals only.

DLP for Data Visibility

Organizations need visibility into the flow of data within their network to identify potential security risks, track data movement, and detect anomalous activities. DLP tools facilitate monitoring, analysis and real-time investigations. An example of using DLP tools for data visibility is to help monitor the flow of customer data to ensure compliance and detect potential data breaches. DLP tools can be used for:

• Organizations need visibility into the flow of data within their network to identify potential security risks, track data movement, and detect anomalous activities. DLP tools facilitate monitoring, analysis and real-time investigations. An example of using DLP tools for data visibility is to help monitor the flow of customer data to ensure compliance and detect potential data breaches. DLP tools can be used for:
• Analyzing email communication to prevent employees from sending customer account details to personal email accounts.
• Generating real-time alerts when unusual data transfers occur, allowing security and IT teams to investigate and take appropriate action.

How to Improve DLP at Your Organization

To improve Data Loss Prevention, start by conducting a comprehensive assessment of the organization’s data landscape. Identify sensitive data repositories, data flows, and potential vulnerabilities. Interview departments to understand the organization’s specific DLP requirements, compliance obligations, and data protection goals.

Once you have assessed what exists within the data landscape, implement a data classification framework that categorizes data based on its sensitivity and criticality. This will help prioritize protection efforts and ensure appropriate controls are applied to different types of data.

Create clear and comprehensive DLP policies and procedures that outline acceptable use of data, data handling guidelines, data access controls, and incident response protocols. These policies should align with regulatory requirements and the organization’s risk appetite.

Once this work is completed, you may choose to deploy a DLP solution. Choose a tool that fits the needs you outlined during your assessment and consider which functionalities will have the greatest impact for your organization. Which integrations you need for your existing IT infrastructure will help shortlist your options. Then determine whether you need more or less content analysis, data monitoring, policy enforcement, and incident management.

Even before you roll out a DLP solution, develop and deliver employee training programs to raise awareness about data security risks. After you’ve incorporated the DLP tool, communicate with end users the importance of DLP and best practices for data handling. Educate employees on recognizing and responding to potential data loss incidents, such as phishing emails or improper data sharing.

As part of ongoing cyber hygiene, implement strong access controls across systems and applications to ensure that sensitive data is accessible only to authorized personnel. Apply the principle of least privilege, granting access rights based on job roles and responsibilities.

Use your DLP tool to monitor and audit data activity, both within the organization’s network and on endpoints. Monitor data flows, user behavior, and network traffic for potential indicators of data leaks or unauthorized data transfers.

Implement encryption mechanisms to protect sensitive data at rest and in transit. Encryption helps safeguard data even if it falls into the wrong hands, providing an additional layer of protection against unauthorized access.

Periodically evaluate the effectiveness of DLP controls and measure compliance with policies and regulations. Perform internal audits and assessments to identify gaps, address vulnerabilities, and improve DLP implementation.

Best Practices for Data Loss Prevention and Data Leak Prevention

The five most impactful best practices for data loss prevention and data leak prevention are:

1. Classification and Inventory of Data: Implement a comprehensive data classification framework to categorize and label sensitive data based on its sensitivity, regulatory requirements, and business impact. Maintain an updated inventory of sensitive data assets, including their location, owners, and access controls. This enables better visibility and targeted protection of critical data.
2. Robust Access Controls and User Awareness: Enforce strong access controls to ensure that only authorized individuals can access sensitive data. Implement a principle of least privilege, granting access rights based on job roles and responsibilities. Regularly educate and train employees on data security policies, best practices for handling sensitive data, and the potential risks of data leaks. Foster a culture of security awareness and responsibility among employees.
3. Encryption and Data protection: Implement encryption mechanisms to protect data at rest, in transit, and in use. apply encryption to sensitive files, databases, emails, and removable media. utilize encryption protocols and algorithms that align with industry standards and regulatory requirements. consider implementing data loss prevention mechanisms within encryption solutions to ensure sensitive data remains protected even if it falls into unauthorized hands.
4. Ongoing Monitoring and Incident Response: Implement continuous monitoring solutions to detect and respond to data loss incidents in real-time. Employ data loss prevention technologies that monitor network traffic, endpoint activities, and data repositories for policy violations, unusual behavior, or unauthorized data transfers. Establish an incident response plan with defined roles, responsibilities, and procedures to promptly address and mitigate data loss incidents. Regularly review and update incident response plans based on lessons learned.
5. Regular Auditing, Testing, and Evaluation: Conduct regular audits and assessments of DLP controls, policies, and procedures to identify vulnerabilities, gaps, or areas for improvement. Perform penetration testing and vulnerability assessments to identify potential weaknesses in DLP implementations. Collaborate with internal or external auditors to ensure compliance with industry regulations and standards. Stay informed about emerging threats and evolving best practices to continuously adapt and enhance DLP strategies.

What to Look for in a Data Loss Solution

When evaluating a DLP solution, assess how well it addresses your organization’s specific data protection requirements, compliance regulations, and industry best practices. Consider factors such as ease of deployment, usability, vendor support, and overall cost-effectiveness to choose a solution that best fits your organization’s needs. The following are categories to evaluate when choosing the right data loss solution for your unique environment:

1. Data Discovery and Classification: The solution should have robust capabilities for discovering and classifying sensitive data across various data repositories, including structured and unstructured data. Look for features such as automated scanning, content analysis, and machine learning algorithms that can accurately identify and categorize sensitive information based on predefined policies or custom rules.
2. Policy Management: The solution should provide a flexible and centralized policy management interface. It should allow you to define and customize security policies based on your organization’s requirements and compliance regulations.
3. Policy Enforcement: The solution should offer a wide range of policy enforcement options, such as blocking, encrypting, quarantining, or alerting, to prevent unauthorized data transfers and leaks.
4. Endpoint Protection: The solution should offer endpoint protection capabilities. This involves deploying agents or clients on endpoint devices, such as workstations, laptops, and mobile devices, to monitor and control data activity at the endpoint level. Look for features such as data monitoring, USB device control, application control, and screen capture prevention to prevent data exfiltration and enforce data handling policies on endpoints.
5. Network Monitoring and Traffic Analysis: The solution should offer network monitoring and traffic analysis capabilities. It should be able to inspect network traffic, both on-premises and in the cloud, to detect and prevent unauthorized data transfers and policy violations. Look for features such as deep packet inspection (DPI), email monitoring, web filtering, and integration with network infrastructure to provide comprehensive network-level protection.
6. Cloud Data Protection: The solution should offer features specifically designed for cloud environments. Look for integration with popular cloud platforms and services, such as cloud storage and collaboration tools. Ensure that the solution offers capabilities for monitoring, classifying, and protecting sensitive data stored and shared within cloud environments. This may include features such as cloud application scanning, data loss prevention for cloud storage, and visibility into data flows in the cloud.
7. Incident Detection and Response: The solution should have robust incident detection and response capabilities. It should provide real-time alerts and notifications when policy violations or data leakage incidents occur. Look for features such as intelligent alerts, incident dashboards, and workflow management to facilitate prompt incident response and investigation. Integration with Security Information and Event Management (SIEM) solutions can also be beneficial for centralized incident management and correlation.
8. Reporting and Analytics: The solution should offer robust reporting and analytics. Look for features that provide comprehensive visibility into data protection activities, policy compliance, and incident trends. The solution should offer customizable reports, dashboards, and metrics to help you assess the effectiveness of your data loss prevention program and demonstrate compliance to stakeholders.
9. Integration: The solution must integrate with your existing IT infrastructure, including email systems, data repositories, network devices, and SIEM solutions. Look for compatibility with common platforms and protocols.
10. Scalability and Performance: The solution should be able to accommodate the growing data volumes and changing business needs of your organization, quickly. It should be able to handle the volume of data and network traffic within your organization ensuring it can maintain efficient performance without impacting network or system operations.

Optional features to consider:

1. Data-in-Use Protection: Protection for data while it is being accessed and used by authorized users. This includes capabilities such as dynamic watermarking, data redaction, and rights management to control and track data usage within the organization.
2. Data Discovery Tool Integration: Integration with data discovery tools to enhance the accuracy and efficiency of data classification. This includes leveraging existing data discovery scans and metadata to inform DLP policies and reduce the effort required to identify and classify sensitive data.
3. User Behavior Analytics (UBA): UBA can analyze user activities, behavior patterns, and anomalies to detect insider threats or malicious actions. These features can enhance the effectiveness of DLP by providing insights into user intent and identifying potential risks.
4. Compliance Reporting and Auditing: Comprehensive reporting capabilities including audit logs, compliance reports, and other documentation required to demonstrate adherence to industry regulations and internal policies. The solution should assist in meeting regulatory requirements for data protection and provide evidence of compliance during audits.
5. Ease of Management and Administration: Features such as a centralized management console, policy configuration wizards, and user-friendly interfaces. The solution should provide intuitive workflows and facilitate efficient policy updates, rule management, and overall administration.
6. Vendor Reputation and Support: Research the reputation and track record of the DLP solution vendor. Assess their experience in the data protection field, customer reviews, and industry recognition. Ensure that the vendor provides reliable technical support, regular software updates, and timely responses to any issues or inquiries that may arise.

Consider Venn’s Blue Border™

Venn’s Blue Border™ protects company data and applications on BYOD computers used by contractors and remote employees. Similar to an MDM solution but for laptops – work lives in a company-controlled Secure Enclave installed on the user’s PC or Mac, where all data is encrypted and access is managed. Work applications run locally within the Enclave – visually indicated by Venn’s Blue Border™ – protecting and isolating business activity while ensuring end-user privacy

The result: stronger DLP controls, reduced IT overhead and a better user experience.

DLP FAQs

1. What is DLP?

DLP stands for Data Loss Prevention. It refers to a set of technologies, strategies, and practices aimed at identifying, monitoring, and preventing the unauthorized or accidental loss, leakage, or exposure of sensitive data within an organization.

2. What are the main types of DLP?

The main types of Data Loss Prevention (DLP) solutions are Network DLP, Endpoint DLP, and Data-at-Rest DLP. These types focus on different aspects of data protection within an organization.

3. What is the best way to prevent data loss?

Preventing data loss requires a comprehensive and layered approach to data protection. While there is no one-size-fits-all solution, implementing a technology to help automatically restrict access and educate users is critical.

4. What does DLP mean in cyber security?

In the context of cybersecurity, DLP stands for Data Loss Prevention. DLP is a set of technologies, strategies, and practices aimed at protecting sensitive data from unauthorized disclosure, loss, or leakage. It is a critical component of an organization’s overall cybersecurity strategy.

5. Is DLP a firewall?

No, DLP (Data Loss Prevention) is not a firewall. While both DLP and firewalls are components of an organization’s cybersecurity infrastructure, they serve different purposes and have distinct functionalities.

6. What are the 5 methods of loss prevention?

The 5 methods of loss prevention are: Physical Security, Procedural Controls, Personnel Controls, Technical Controls, and Legal and Regulatory Compliance.

7. What are the 5 steps in loss prevention?

The 5 steps of risk loss prevention are: Assessing Risk, Implementing Preventive Measures, Detecting and Monitoring, Incident Response, and Continuous Improvement.

8. Why is DLP important for organizations?

DLP is important for organizations to ensure data protection, compliance with regulations, risk management, and safeguarding their financial and reputational well-being. It helps organizations maintain trust, secure valuable assets, and demonstrate a commitment to data privacy and security in an increasingly interconnected and data-driven world.

9. What is a data loss prevention policy?

A data loss prevention policy is a set of guidelines, rules, and procedures that outline how an organization will manage and protect sensitive data. It serves as a framework for implementing DLP measures and ensuring consistent data protection practices throughout the organization.