12 Best Data Loss Prevention Tools of 2024

This year, the average cost of a data breach is $4.88 million. Many organizations can’t afford the financial burdens or reputational damage associated with poor data security. Needless to say, in today’s sprawling, cloud-based digital landscape, the risks have never been higher.

But have no fear: Data Loss Prevention (DLP) tools can provide CISOs and security analysts with some peace of mind. Read on to learn more about how you can use DLP tools to create an airtight data protection strategy.

What is Data Loss Prevention (DLP)?

DLP solutions integrate seamlessly with cloud, network, and endpoint environments to scan for sensitive data. If a DLP tool detects sensitive data, it'll surface that data so that security teams can investigate. Typically, DLP tools also offer a menu of remediation actions, such as redaction, deletion, encryption, and more. These remediation actions help to make sure that sensitive data isn't accidentally sprawled or leaked.

At a glance, DLP solutions:

• Monitor data at rest and in motion to to detect any unauthorized data sharing.
• Classify sensitive data like personally identifiable information (PII), payment card industry data (PCI), protected health information (PHI), or intellectual property (IP).
• Remediate sensitive data in real time by redacting, deleting, or encrypting it.
• Ensure regulatory compliance with GDPR, HIPAA, PCI-DSS, and other regulatory standards.

In short, businesses can use DLP to safeguard data, secure file sharing, and reduce the risk of external threats (like data breaches) as well as internal threats (like data exfiltration).

How can you use data loss prevention software? 

DLP software comes in handy for preventing any unauthorized data access or transmission. DLP tools typically offer features such as: 

• Visibility into data sharing across cloud, network, and endpoint environments
• Content inspection for identifying sensitive data
• Policy enforcement for ensuring compliance with security regulations
• Encryption for data at rest and in motion
• Incident response to address security incidents promptly
• Reporting to demonstrate compliance

These features create a strong defense against data leakage, data breaches, and noncompliance.

What are the different types of data loss prevention solutions? 

In today’s remote and hybrid work environments, it’s a good idea to cover all your bases. This is where the three major types of DLP come into play: 

• Network DLP: Monitors data in motion across the network, and intercepts traffic to prevent unauthorized data transfers.
• Endpoint DLP: Protects data at rest on individual devices like laptops, smartphones, and tablets.‍
• Cloud DLP: Secures data stored in cloud applications and SaaS platforms, ensuring protection in multi-cloud environments.

What are key features to look for in data loss prevention tools?

When evaluating DLP tools, it’s best to prioritize the following features:

• Comprehensive coverage: Choose tools that can protect data across SaaS apps, GenAI tools, email, and endpoints.
• AI-powered detection: Find a solution that leverages AI for more accurate findings.
• Precise data classification: Opt for a DLP tool that can reliably categorize sensitive information like PII, PCI, PHI, and IP.
• User-friendly interface: Go for a tool that has intuitive policy management.
• Automated workflows: Seek out a solution that offer automated alerts, policy enforcement, and remediation actions for efficient incident response.
• Seamless integrations: Ensure that your DLP software integrates smoothly with your existing security infrastructure.
• Detailed reporting and analytics: Choose a tool with strong reporting features to track security incidents, monitor user activities, and demonstrate regulatory compliance.
• Scalable features: Pick a solution that can scale with your organization and adapt to evolving threats.

When equipped with the above features, DLP tools can run in the background with minimal input from your security team.

What are the top data loss prevention vendors in 2024? 

Nightfall AI DLP

Nightfall AI is an AI-native data security platform that discovers and protects sensitive data across SaaS apps, GenAI apps, email, and endpoints. Whether you’re looking to prevent secret sprawl, stop data exfiltration, or encrypt sensitive data, Nightfall’s industry-leading detection engine and flexible APIs cover a wide variety of use cases. Get a demo of Nightfall’s industry-leading platform here.

Key features:

• AI-powered detection engine with 2x greater precision and 4x fewer false positives than legacy DLP solutions
• Hi-res visibility and granular controls (including customizable policies)
• Real-time alerts via Slack, Teams, Jira, email, or any SIEM of your choice
• Automated end-user remediation via the "Human Firewall" feature

Microsoft Purview DLP

Microsoft Purview stands out by offering comprehensive data protection across cloud, on-premises, and hybrid environments. Its integration with Microsoft 365 provides seamless coverage for Microsoft Teams, SharePoint, OneDrive, Exchange, and more.  

Pros:

• Seamless integration with the Microsoft ecosystem
• Advanced machine learning (ML) for data classification
• Comprehensive policy enforcement for files shared within the ecosystem

Cons:

• Complex initial setup with high total cost of ownership
• DLP support is not available with E1/A1/G1 or business premium licenses, and requires additional subscription to E3/A3/G3 licenses
• Delays in alerting
• May require additional tools for non-Microsoft environments

Google Cloud DLP 

Google Cloud DLP integrates seamlessly with Google Cloud services for effective content inspection. With this in mind, Google Cloud DLP is particularly suited for businesses that already use the Google Cloud ecosystem.

Pros:

• Powerful ML-based detection for sensitive data protection
• Effective for Google Workspace environments

Cons:

• Limited coverage for non-Google SaaS applications
• Does not offer data exfiltration prevention
• Inflexible policies and limited alerting options
• May require additional solutions for comprehensive protection across all platforms

Netskope DLP

Netskope is recognized for its cloud-native framework, as well as its integrations with SaaS, IaaS, and web environments.

Pros:

• In-depth visibility and control over cloud data
• Granular policy controls and user behavior analytics 

Cons:

• Clunky UI with complex policy management
• Potential performance issues with the Netskope Agent on endpoints
• Limited reporting dashboard

Code42 DLP

Code42 monitors file movements to prevent data loss and data exfiltration, particularly in endpoint and cloud environments.

Pros:

• Effective protection against data loss and data exfiltration
• Rapid recovery of lost or compromised data
• Smooth integrations with various existing IT environments

Cons:

• Limited features compared to more comprehensive DLP solutions
• Basic detection capabilities may result in higher false positive rates
• Setup process requires browser plug-ins
• Interface is not intuitive

Zscaler DLP

Zscaler focuses on monitoring data traffic in the cloud to prevent data loss and ensure regulatory compliance.

Pros:

• Strong cloud-based security architecture for data traffic monitoring
• Integrates with existing IT environments for enhanced data protection
• Supports regulatory compliance across multiple standards

Cons:

• Limited detection capabilities with regex-based pattern matching
• Lacks advanced features like image classification and GenAI integration
• Steep learning curve due to complex workflows and manual remediation options

Symantec (Broadcom) DLP

Symantec, now integrated into Broadcom's portfolio, offers extensive data protection across endpoints, networks, and cloud environments. 

Pros:

• Wide range of deployment options for diverse IT environments
• Strong integration capabilities with other security tools
• User risk scoring and automated incident response for security teams

Cons:

• Resource-intensive implementation
• Can be complex to manage in large organizations

Forcepoint DLP

Forcepoint focuses on data security and compliance across cloud and on-premises environments.

Pros:

• Comprehensive coverage across various environments
• Strong capabilities in user behavior analytics and insider threat detection
• Integrates with existing security infrastructures for enhanced protection

Cons:

• Primarily based on legacy systems, which may lead to integration issues in cloud-first environments
• Limited use of advanced AI, leading to a higher rate of false positives
• Complex policy management with a steeper learning curve for users

Proofpoint DLP

Proofpoint is known for its strong email security capabilities and comprehensive approach to information protection.

Pros:

• Advanced content inspection techniques for detecting sensitive data
• Robust email security features to prevent data exfiltration
• Strong regulatory compliance features, including for personally identifiable information (PII)

Cons:

• Complex setup process for full feature implementation
• Can be expensive for extensive coverage across all channels

Trellix DLP

Trellix (formerly McAfee) employs behavior analytics for data protection across cloud services, email, and endpoints.

Pros:

• Strong behavior analytics for detecting anomalies
• Focuses on extended detection and response (XDR)

Cons:

• Frequent false positives require constant adjustments
• Unintuitive interface complicates policy management
• Disruptive updates and patches may interrupt operations
• Limited flexibility in customizing rules for unique business needs
• Challenging product integrations because of multiple acquisitions over the years

Fortra’s Digital Guardian DLP

Digital Guardian offers a robust DLP solution that secures data across endpoints, networks, and cloud environments.

Pros:

• Strong endpoint protection capabilities
• Comprehensive coverage across networks and cloud environments

Cons:

• Complex deployment and management processes
• Frustrating user interface (UI) 
• Steep learning curve for new users

Palo Alto Networks Enterprise DLP

Palo Alto Networks (PANW) offers Enterprise DLP as part of its broader security platform.

Pros:

• Strong integration with other Palo Alto security services
• Comprehensive content inspection and policy management capabilities
• ML-powered data classification

Cons:

• Complex and resource-intensive implementation process
• Challenging policy configuration may lead to misconfigurations
• Better fit for organizations who already use the PANW ecosystem

What are best practices for implementing data loss prevention tools? 

Maximize the effectiveness of your DLP strategy by following these best practices:

1. Develop clear DLP policies: Outline protocols for handling and storing sensitive data like PII, PCI, PHI, and IP. These protocols should also consider requirements for regulatory standards like GDPR, HIPAA, PCI-DSS, and more.
2. Start with a pilot program: Identify potential challenges or shortcomings with your DLP strategy before scaling to your entire business.
3. Train IT staff and end users: Educate IT staff and end users about best practices for handling sensitive data. While annual trainings can be useful, coaching is most effective with real-time notifications.
4. Regularly review and update your DLP strategy: Data protection is not a one-time effort; it requires ongoing commitment. Be sure that you review and update your DLP strategy to adapt to new threats and regulatory changes.
5. Monitor and analyze DLP reports: Utilize your DLP tool to generate reports on user activities and data handling. Analyze these reports to identify patterns, trends, and potential vulnerabilities.

By following these best practices, you can optimize your DLP strategy while defending your data against leaks and breaches.

What are the challenges associated with data loss prevention tools? 

It's no secret that legacy DLP tools can be frustrating, especially with their false positive alerts and clunky workflows. However, AI can often help with addressing these challenges, such as by:

1. Maintaining hi-res visibility: AI tools can analyze vast quantities of data in real time.‍
2. Scanning for unusual behavior: AI analyzes user behavior to flag any potential risks.
3. ‍Securing data in real time: AI tools can enforce DLP policies automatically.‍
4. Refining DLP strategies: AI analytics can help identify weaknesses in your security framework.‍
5. Enhancing incident response: AI-powered DLP solutions can minimize the impact of a breach.

By embracing AI, it's possible to mitigate the challenges posed by legacy DLP tools. For example, learn more about how Nightfall leverages AI here.

What’s the TL;DR on data loss prevention tools? 

Data loss prevention (DLP) tools are essential for safeguarding your sensitive information and ensuring regulatory compliance. The right DLP solution will scale with your business, and will serve as an investment in the future of your security program. 

Amidst a sea of DLP tools, Nightfall AI stands out for its innovative detection engine and automated security workflows. Get in touch for a free demo of Nightfall’s industry-leading data protection solutions.