DLP 101: 12 Best Data Loss Prevention Tools of 2024

Did you know that data breaches can cost companies upward of $4 million apiece? That's a price tag many businesses can't afford—not to mention the reputational damage that comes with poor data security. 

But don't panic! Data loss prevention tools are here to come to the rescue, offering CISOs and security analysts some much-needed peace of mind. Let's dive into how you can use these tools to build an ironclad data protection strategy.

What is data loss prevention?

Data loss prevention tools (a.k.a. DLP tools) act like vigilant guardians for your cloud, network, or endpoint environments. They constantly scan for sensitive data, and when they find it, they immediately alert your security team. But that's not all—these tools also offer a variety of remediation actions to redact, delete, or encrypt sensitive information before an accidental data leak or breach occurs. Advanced solutions with a collaborative approach to mitigating security threats to your data also give users the option to provide a business justification for exceptions.

Here's what DLP tools do in a nutshell:

1. Keep a watchful eye on data, whether it's at rest or in motion, alerting on suspicious activity.
2. Sort out sensitive data by identifying personally identifiable information (PII), payment card industry data (PCI), protected health information (PHI), or intellectual property (IP).
3. Take action on policy violations in real time to protect sensitive data with options to redact, delete, quarantine, encrypt, or invite users to provide a business justification.
4. Make sure you're meeting data security requirements under compliance frameworks like GDPR, HIPAA, PCI-DSS, and other regulatory standards.

In other words, businesses use DLP to lock down their data, secure file sharing, and reduce the risk of both external threats (like data breaches) and internal threats (like data exfiltration).

Core Functions of a Modern Data Loss Prevention Solution

Under the banner of preventing data loss modern, unified platforms that are focused on providing advanced security for your data also offer capabilities like data detection and response (DDR), data security posture management (DSPM), access control, data lineage, exfiltration prevention, and automated security workflows. Ideally, these DLP providers use advanced AI models to help speed up security teams, reduce their overall response workload, and improve detection accuracy. The latter point is a crucial outcome, as most DLP has become known for its historical "noisiness" and low signaling on complex datasets that are unstructured and can't be identified with regular expressions (regex) or matching technologies.

How can you use DLP tools? 

Think of DLP software as your personal bouncer for data: it stops any unauthorized access or transmission in its tracks. Here's what you can expect from a good DLP tool:

1. X-ray vision into data sharing across your entire digital landscape
2. Real-time policy enforcement 
3. Military-grade email encryption 
4. Rapid response to security incidents
5. Detailed reporting to prove your compliance 

With these features, you're building a strong defense against data leaks, breaches, and noncompliance.

What are the different types of data loss prevention tools?

In today's world of remote and hybrid work, you need to cover all your bases. That's where the three types of DLP come in:

1. Network DLP: Monitor data in motion and stop unauthorized transfers.
2. Endpoint DLP: Protect data at rest on laptops, smartphones, and tablets.‍
3. Cloud DLP: Safeguard data in cloud apps like Slack, ChatGPT, and more.

You may need a patchwork of multiple DLP solutions to cover all three of these areas. However, more advanced DLP tools cover multiple areas in one. With that in mind, let's move on to what you should look for in your ideal DLP tool.

Major Differences in Scope of Cloud DLP Providers

It's important to segment cloud DLP into solutions focused on a single application or portion of your cloud environment, known as native DLP, from those covering your entire cloud ecosystem–including all your mission-critical SaaS applications. Native DLP, like Microsoft DLP (Purview), Slack DLP, or Google DLP are not interested in creating a security ecosystem for users. Rather, their aim is to exclude any application outside of their own brand. This can cause disruptions to productivity for your security teams, forcing them to manage multiple vendors for one function. This means portal switching, dual systems for the same goal, lack of universal visibility, and nuanced capabilities or nomenclature your teams have to learn. For improved productivity and an efficient ecosystem, security teams need a centralized platform that does it all–cloud, SaaS, and even endpoint monitoring.

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

When you're shopping for DLP tools, keep an eye out for these must-have features:

1. All-encompassing coverage: Choose a tool that's got your back across SaaS apps, AI tools, email, and endpoints.
2. AI-powered detection: Look for a solution that uses AI to spot issues more quickly and accurately.
3. Precise data classification: Opt for a DLP tool that can reliably categorize sensitive information like PII, PCI, PHI, and IP.
4. User-friendly interface: Go for a tool that makes policy management a breeze.
5. Automated workflows: Seek out a solution that offers automated alerts, policy enforcement, and remediation actions for efficient incident response.
6. Seamless integrations: Ensure your DLP software plays nice with your existing security setup.
7. Detailed reporting and analytics: Choose a tool with strong reporting features to track security incidents, monitor user activities, and demonstrate regulatory compliance.
8. Scalable features: Pick a solution that can grow with your organization and adapt to evolving threats.

With these features in place, your DLP tools can work their magic in the background, requiring minimal input from your security team.

What are the best data loss prevention vendors in 2024?

Nightfall AI was built with generative AI (GenAI) at the core to discover and protect sensitive data across SaaS apps, AI apps, email, and endpoints. Whether you're trying to prevent secret sprawl, stop data exfiltration, or encrypt sensitive data, Nightfall's cutting-edge detection engine and flexible APIs have got you covered. 

Key features:

• AI-powered detection engine with 2x better precision and 4x fewer false positives than legacy DLP solutions
• Fine-tuned controls, including easily customizable policies and detection rules
• Real-time alerts via Slack, Teams, Jira, email, or your SIEM of choice
• Automated end-user remediation via the "Human Firewall" feature

Want to see Nightfall’s industry-leading detection engine in action? Sign up for a custom demo here.

#12 - Microsoft Purview DLP

Microsoft Purview offers comprehensive data protection across cloud, on-premises, and hybrid environments. It integrates seamlessly with Microsoft 365 to provide smooth 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
• Requires additional subscription to E3/A3/G3 licenses
• Delays in alerting
• Requires additional tools for non-Microsoft environments

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#11 - Google Cloud DLP 

Google Cloud DLP integrates smoothly with Google Cloud services for effective content inspection. It's particularly suited for businesses that already use the Google Cloud ecosystem.

Pros:

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

Cons:

• Limited coverage for SaaS apps outside of the Google Workspace
• Poor signaling on complex data types
• Does not prevent data exfiltration prevention
• Inflexible policies and limited alerting options
• Requires additional tools for non-Google environments

#10 - Netskope DLP

Netskope stands out for its cloud-native framework and 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

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#9 - Code42 DLP

Code42 keeps an eye on 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

#8 - 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

#7 - Symantec (Broadcom) DLP

Symantec, now part of 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
• Complex to manage in large organizations

#6 - Forcepoint DLP

Forcepoint concentrates 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:

• Integration issues in cloud-first environments
• High rate of false positives
• Complex policy management with a steeper learning curve for users

#5 - 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

Cons:

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

#4 - 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

#3 - 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

#2 - 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
• Better fit for organizations who already use the PANW ecosystem

#1 - Nightfall AI

Overview of Nightfall DLP

Nightfall AI is the market leader in Next-Gen DLP. From a single pane of glass, Nightfall gives you visibility into sensitive data in all your mission-critical SaaS apps, cloud workspaces, GenAI tools, and now endpoints. With Nightfall, you can put remediation tasks on autopilot across cloud data loss prevention. No portal switching, no lengthy manual processes, just comprehensive coverage.

With powerful reporting tools and advanced threat detection for your data, Nightfall AI is the perfect tool for a layered approach to data security.

Pros:

• Automatically finds and remediates data sprawl that can put you at risk of experiencing severe data breaches or violating regulatory requirements
• Advanced AI-powered detection goes far beyond just credit card data or social security numbers (SSN)
• Easily identifies even the most complex, unstructured data sets, including personal data, protected health information, and even custom fields in your SaaS apps
• Universal coverage for Google Cloud Platform, Gmail, and all your mission critical SaaS (Jira, Slack, GitHub, ZenDesk, and much more)
• Provides robust DLP at the endpoint level, as well
• Use templates or create custom security policies to ensure compliance with regulations and a strong data security posture

Cons:

• You'll definitely want to replace your current DLP after a POV with Nightfall AI.
• You won't be able to get mad at employees anymore, because the collaborative nature of our platform is proven to vastly improve internal data hygiene over time.
• You'll have to engage in conversations at family meals, because you won't be called out of the room to deal with a slew of false positives.

What are best practices for implementing data loss prevention tools?

To get the most out of your DLP products, follow 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? 

Let's face it—legacy DLP tools can be a real headache, especially with their false positive alerts and clunky workflows. But here's the good news: AI can often solve these challenges. Here's how:

1. Maintaining hi-res visibility: AI tools can analyze vast quantities of data in real time, giving you a crystal-clear view of your data landscape.
2. Scanning for unusual behavior: AI analyzes user behavior to flag any potential risks, acting like a digital detective.
3. Securing data in real time: AI tools can enforce DLP policies automatically, providing instant protection.
4. Refining DLP strategies: AI analytics can help identify weaknesses in your security framework, allowing you to patch up vulnerabilities before they become problems.
5. Enhancing incident response: AI-powered DLP solutions can minimize the impact of a breach by responding quickly and efficiently.

By embracing AI, it's possible to overcome 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 tools, or DLP tools, are your secret weapon for safeguarding sensitive information and ensuring regulatory compliance. The right DLP solution will grow with your business and serve as an investment in the future of your security program. 

In a sea of DLP tools, Nightfall AI stands out for its innovative detection engine and automated security workflows. Ready to see how Nightfall can revolutionize your data protection strategy? Get in touch for a free demo of Nightfall's industry-leading data protection solutions.

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