What is Data Loss Prevention (DLP)? 2025 Guide

Data loss prevention (DLP) is a practice that seeks to improve information security and protect business information from data breaches by preventing users from moving key information outside the network. DLP also refers to tools that enable a network administrator to monitor data that is accessed and shared by end users.

DLP solutions can be used to classify and prioritize data security. You can also use these solutions to ensure access policies meet regulatory compliance, including HIPAA, GDPR, and PCI-DSS. DLP solutions can also go beyond simple detection, providing alerts, enforcing encryption, and isolating data.

Some other common features of DLP solutions are:

• Monitoring – provide visibility into who is accessing data and systems, and from where
• Filtering – filter data streams to restrict suspicious or unidentified activity
• Reporting – logging and reports helpful for incident response and auditing
• Analysis – identify vulnerabilities and suspicious behavior, and provide forensic context to security teams

This is part of an extensive series of guides about data security.

Types of Data Loss

Organizations commonly face three types of data loss:

External cyber attacks: Outsiders typically use cyber attacks—such as phishing, ransomware, or malware—to gain unauthorized access to an organization’s sensitive data and exfiltrate or compromise it. These attacks require comprehensive defensive measures, ongoing monitoring, and fast response processes to reduce impact.

Accidental data loss: Accidents by employees or third parties—such as deleting files unintentionally, misplacing portable drives, or sending confidential emails to the wrong recipient—can cause data loss. This type of data loss is usually due to user error or unclear data handling practices.

Malicious insider threats: Internal threats can arise from employees or contractors deliberately stealing or leaking sensitive information. Insiders have authorized access and knowledge of security measures, making such losses difficult to detect and prevent without proper monitoring.

Main Causes of Data Leaks

Data leaks typically happen through several common issues:

Weak or stolen credentials: Attackers often exploit weak or reused passwords and compromised user accounts. Credential theft through phishing campaigns, brute force attacks, and credential stuffing techniques gives attackers unauthorized access.

Unsecured endpoints and devices: Mobile devices, laptops, USB drives, and other portable devices are vulnerable to theft, loss, and remote attacks. If sensitive information is stored on these endpoints and not properly secured with encryption and robust access controls, the data may be compromised.

Misconfigured security settings: Misconfigured data storage systems, cloud services, servers, or databases may unintentionally expose sensitive information. Without proper security configurations—restricting public access, enabling authentication and encryption—data breaches frequently occur.

Unsecured data transfer and email mistakes: Sensitive information sent via unsecured channels or incorrectly addressed emails can leak confidential data. Employees can inadvertently attach or forward sensitive files, leading to accidental breaches.

Outdated or vulnerable software: Unpatched software with known exploits increases vulnerability to cyberattacks. Attackers actively scan systems to exploit vulnerabilities; timely patching and vulnerability management processes are critical in lowering these risks.ed software with known exploits increases vulnerability to cyberattacks. Attackers actively scan systems to exploit vulnerabilities; timely patching and vulnerability management processes are critical in lowering these risks.

How does DLP work?

There are two main technical approaches to DLP:

1. Context analysis looks only at metadata or other properties of the document, such as header, size, and format. 
2. Content awareness involves reading and analyzing a document’s content to determine if it includes sensitive information.

Modern DLP solutions combine both of these approaches. At the first stage, DLP examines the context of a document to see if it can be classified. If context is insufficient, it explores within the document using content awareness. 

There are several techniques commonly used for content awareness:

• Statistical analysis – can use machine learning algorithms for Bayesian analysis to identify content that violates a policy or contains sensitive data. The effectiveness of these techniques can be increased by feeding more labeled data to the algorithm for training.
• Rule-based – analyzing a document’s content using certain rules or regular expressions, for example, searching for credit card numbers or social security numbers. This approach is very effective as an initial filter, because it is easy to configure and process, but it is usually combined with additional techniques.
• Dictionaries – by combining the use of dictionaries, taxonomies, and lexical rules, the DLP solution can identify concepts that indicate sensitive information in unstructured data. This requires careful customization to each organization’s data.
• Exact data matching – creates a “fingerprint” of the data, and searches for exact matches in a database dump or currently running database. However, creating a data dump or accessing live databases can negatively affect performance —  one drawback of this technique.
• Exact file matching – creates a hash of the entire file, and looks for files that match this hash. This technique is very accurate, but cannot be used for files with multiple versions.
• Partial document match – can identify files where there is a partial match; for example, the same form filled out by different users.

DLP use cases

DLP solutions can be helpful in a variety of use cases, including:

Central management of sensitive data – DLP solutions provide central control over all sensitive data assets, enabling you to set policies, grant or revoke access, and generate compliance reports.

Ensuring compliance for personally identifiable information (PII) – If your organization needs to comply with regulations like GDPR or HIPAA, DLP can help identify and classify sensitive information, add required security controls, and help you set up monitoring and reporting to protect the data.

Preventing data leakage user endpoints – DLP solutions can protect data stored on endpoints such as mobile devices and laptops, which are at high risk because they connect to unsecured networks, and may be lost or stolen. DLP can identify suspicious events on a device and alert security teams that there is a risk of data loss.

Data discovery – DLP can continuously discover and classify the organization’s sensitive data, whether it is stored on endpoints, storage systems, or servers. It can also provide visibility into who is using the data and what actions they are performing.

Prevent data exfiltration – Sophisticated attackers carry out targeted cyber attacks, usually with the aim of stealing sensitive data. In the event of a breach, DLP solutions can prevent data exfiltration by identifying a suspicious data transfer, blocking it, and alerting security teams.

Types of Data Loss Prevention Solutions

Network DLP

Network data loss prevention solutions monitor, analyze, and control data traveling across the corporate network. They actively inspect traffic transmitted over various channels and protocols—such as email, web applications, instant messaging, FTP, and other communication methods. 

Network DLP helps enforce data protection policies by scanning network packets to detect sensitive content based on predefined or customized rules. These solutions can block or reroute data transfers if policies are violated, issue real-time alerts, and generate detailed logging and reporting for compliance audits and incident investigations.

Endpoint DLP

Endpoint data loss prevention solutions protect sensitive information directly on user endpoint devices such as laptops, desktops, tablets, and mobile phones. Endpoint DLP monitors user actions like copying or transferring files, printing documents, using removable media, and sending emails. 

It applies policy-based controls to detect sensitive data when users attempt risky operations, immediately enforcing protections such as encrypting data, preventing transfer, displaying security warnings, or alerting administrators. This solution helps mitigate risks associated with device theft, loss, user errors, and malicious insider threats.

Cloud DLP

Cloud data loss prevention solutions secure sensitive data stored and processed on cloud services and applications—including software-as-a-service (SaaS) platforms, cloud storage providers, and infrastructure-as-a-service (IaaS) environments. Cloud DLP continuously monitors cloud resource usage, analyzes data access patterns and sharing permissions, and scans for sensitive or regulated content stored in cloud services. 

It helps enforce consistent data protection policies and regulatory compliance across cloud environments by alerting administrators, automatically correcting misconfigurations, controlling unauthorized sharing, and restricting inappropriate data movement or access.

Building your data loss prevention policy

Individuals in organizations are privy to company information and can share it, which can lead to data loss — whether accidental or intentional. The distributed nature of today’s computer systems magnifies the problem.

Modern data storage can be accessed from remote locations and through cloud services. Laptops and mobile phones contain sensitive information, and these endpoints are often vulnerable to hacking, theft, and loss. It is becoming increasingly difficult to ensure that company data is secure, making DLP a critical strategy.

3 reasons for implementing a data loss prevention policy

Data visibility – Implementing a DLP policy can provide insight into how stakeholders use data. In order to protect sensitive information, organizations must first know that it exists, where it resides, who accesses it, and for what purposes it is used.

Compliance – Businesses are subject to mandatory compliance standards imposed by governments (such as HIPAA, SOX, PCI DSS). These standards often stipulate how businesses should secure personally identifiable information (PII) and other sensitive data. A DLP policy is a basic first step to compliance, and most DLP tools are built to address the requirements of common standards.

Intellectual property and intangible assets – An organization may have trade secrets, other strategic proprietary information, or intangible assets, such as customer lists and business strategies. Loss of this type of information can be extremely damaging, making it a direct target for attackers and malicious insiders. A DLP policy can help identify and safeguard critical information assets.

Tips for creating a successful DLP policy

• Don’t save unnecessary data – A business should only use, save, and store essential information. If it’s not needed, remove it; data that was never stored can never go missing.
• Classify and interpret data – Identify which information needs to be protected by evaluating risk factors and its level of vulnerability. Invest in classifying and interpreting data because this is the basis for implementing a data protection policy that suits your organization’s needs.
• Allocate roles – Clearly define the role of each individual involved in the data loss prevention strategy.
• Begin by securing the most sensitive data – Start by selecting a specific kind of information to protect, which represents the biggest risk to the business.
• Automate as much as possible – The more DLP processes are automated, the more broadly you’ll be able to deploy them in your organization. Manual DLP processes are inherently limited in their scope and the amount of data they can cover.
• Use anomaly detection – Some modern DLP tools use machine learning and behavioral analytics, rather than simple statistical analysis and correlation rules, to identify abnormal user behavior. Each user and group of users is modeled with a behavioral baseline, allowing accurate detection of data actions that might represent malicious intent.
• Involve leaders in the organization – Management is key to making DLP work, because policies are worthless if they are unable to be enforced at the organizational level.
• Educate stakeholders – Simply putting a DLP policy in place is not enough. Invest in making stakeholders and users of data aware of the policy, its significance, and what they need to do to safeguard your organization’s data.
• Documenting DLP strategy – Documenting the DLP policy is required by several compliance standards. It also provides clarity around policy requirements and enforcement, both at the individual and organizational level.
• Establish metrics – Measure DLP effectiveness using metrics like percentage of false positives, number of incidents and Mean Time to Response.

4 data loss prevention best practices

1. Data classification must be central to DLP execution

Before implementing a DLP solution, pay special attention to the nature of your company’s sensitive information, and how it flows from one system to another. Identify how information is transferred to its consumers; this will reveal transmission paths and data repositories. Classify sensitive data by categorizing it with labels, such as “employee data,” “intellectual property,” and “financial data.” 

Investigate and record all data exit points. Organizational processes may not be documented, and not all data movement happens as part of a routine practice.

2. Establish policies upfront

Engage IT and business staff in the early stages of policy development. This stage of the process should include identifying:

• Data categories that have been singled out
• Steps that need to be implemented to combat malpractice
• Future growth of the DLP strategy
• Steps that need to be taken if there is any unusual activity

Before putting the DLP strategy into practice, it is essential to establish incident management processes and ensure they are practical for each data category.

3. How to start

The first step to implementing DLP is monitoring organizational data. This lets you anticipate and refine the effect that the DLP may have on organizational culture and operations. By blocking sensitive information too soon, you may negatively impact central business activities.

DLP provides a lot of information, such as the transmission path and location of all sensitive information, which can be overwhelming. Resist the temptation to try to solve all of your data protection issues at once.

A good way to start your DLP implementation is with the low hanging fruit. Establish rules and ensure they are continually considered and improved. Involve all relevant stakeholders and ensure they provide feedback on new data types, formats, or transmission paths that are not listed in the current DLP strategy or not currently protected.

4. Know that DLP technology has its limitations

• Encryption – DLP tools can only examine encrypted information that they initially decrypt. If users encrypt data with keys that the DLP system operators can’t access, the information is invisible.
• Rich media – DLP tools are generally not useful when working with rich media such as images and video, because they cannot parse and classify their content.
• Mobile – DLP solutions cannot track all types of modern mobile communication, like messages sent from a user’s private mobile device.

Trends in Data Loss Prevention

Several emerging trends are reshaping the data landscape, requiring organizations to update their DLP policies and tools to effectively address new challenges in data security. The key trends impacting data loss prevention include:

• Hybrid and multi-cloud environments: More organizations store their data across diverse environments, spanning on-premises infrastructure and multiple cloud platforms, often located in different regions or countries. While this model provides flexibility and cost benefits, it greatly increases complexity and raises the risk of data breaches.
• Generative AI: The rapid adoption of generative AI and large language models (LLMs) introduces new challenges for secure data handling. These models handle massive data volumes that need specialized tracking, storage processes, and defenses against unique threats like prompt injection. Generative AI is forecasted to become involved in nearly 17% of all cyberattacks or data breaches by 2027.
• Increased regulation: Data breaches and privacy abuses are catalyzing stronger data security regulations worldwide. Compliance demands are mounting due to frameworks such as the EU AI Act and California’s updated CCPA rules, focusing specifically on artificial intelligence and stricter data privacy and protection requirements.
• Mobile workforce and remote work: By 2026, around 64% of all employees are expected to work remotely or in hybrid environments, and this greatly increases the complexity of managing and safeguarding sensitive data. Employees accessing systems remotely or holding multiple employment and contracting arrangements amplify risks and challenges around secure data handling.
• Shadow IT and shadow data: Growing employee adoption of unsanctioned personal devices, apps, and cloud platforms—known as shadow IT—has become a significant source of unmanaged organizational data. Similarly, shadow data, which is data stored or transferred within company networks without the knowledge or oversight of official IT teams, adds substantial risk. Another problem is shadow data, underscoring the necessity of stronger detection, visibility, and mitigation measures within DLP solutions.

Complementing DLP with next-gen SIEM

DLP solutions are great at monitoring data flows and securing against known threat patterns. However, malicious insiders and sophisticated attackers can act in ways that are unpredictable, or that evade DLP security rules. A category of security tools called user and entity behavior analytics (UEBA) can help.

UEBA tools establish a behavioral baseline for individual users, applications, network devices, IoT devices, or peer groupings of any of these. Using machine learning, they can identify abnormal activity for a specific entity or group of entities, even if it doesn’t match any known threat or pattern. This can complement traditional DLP solutions, alerting security teams of data-related incidents that have slipped past DLP rules.

Exabeam Advanced Analytics is an example of a UEBA system that can help prevent data breaches due to unknown threats.

See how Exabeam’s advanced behavioral analytics can help identify data breaches faster and prevent data loss.

See Additional Guides on Key Data Security Topics

Together with our content partners, we have authored in-depth guides on several other topics that can also be useful as you explore the world of data security.

Object Storage

Authored by Cloudian

• [Guide] What is Object Storage: Definition, How It Works and Use Cases
• [Guide] Best Object Storage Solutions: Top 5 Products to Know in 2025
• [Product] Cloudian | Enterprise-Class, S3-Compatible Object Storage Software

Exposure Management

Authored by CyCognito

• [Guide] What Is Continuous Threat Exposure Management (CTEM)? 
• [Guide] Understanding Adversarial Exposure Validation (AEV): 2025 Guide 
• [White Paper] Exposure Management: The Definitive Guidebook for the Security Practitioner 
• [Product] CyCognito Attack Surface Management | Comprehensive Attack Surface Management Solutions

Clickhouse

Authored by Instaclustr

• [Product] NetApp Instaclustr Data Platform | Open-Source Data Infrastructure Platform
• [Guide] Understanding ClickHouse^®: Products, architecture, tutorial and alternatives
• [Guide] ClickHouse vs. Postgres: 5 key differences and how to choose
• [Blog] Maximising Availability with Apache Cassandra