DLP 101: 10 Best Data Loss Prevention Solutions in 2024

Data loss prevention solutions are a critical aspect of any data security strategy. Read on to learn why you need DLP—and how you can choose the best DLP solution for your business. 

What are data loss prevention solutions?

DLP  can detect and prevent the unauthorized use, access, or transmission of sensitive data. Different solutions specialize in protecting different environments, such as SaaS applications, generative AI (GenAI) tools, email services, and endpoint devices.

Why are data loss prevention solutions important in 2024? 

As cyber threats evolve, and data regulations become more stringent, DLP solutions play a vital role in:

• Protecting sensitive information from data breaches and data leaks
• Preventing insider threats and accidental data exposure
• Ensuring regulatory compliance with standards like GDPR, HIPAA, and SOC 2
• Maintaining customer trust and brand reputation

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

When evaluating DLP solutions, look out for the following features:

• Data discovery and classification: Scan and classify sensitive data across various repositories, as well as multiple data types and formats. 
• Real-time monitoring and alerts: Monitor data movement with instant alerts for any suspicious activity. 
• Policy enforcement across multiple channels: Manage and enforce security policies across business-critical channels like SaaS apps, AI tools, email, and endpoints.
• Integration with existing security infrastructure: Integrate seamlessly with your existing security and tech stacks. 
• Automated incident response: Automate security workflows to handle incidents quickly.‍
• Comprehensive reporting and analytics: Create custom dashboards, detailed audit logs, and advanced analytics for compliance reporting.

How can you choose the right data loss prevention solution for you?

There are dozens of DLP vendors out there. Here are a few things you should consider to zero in on the right one: 

• Business size and industry: Consider DLP solutions that cater to your business’ size, data volume, and sector (e.g. healthcare, finance).
• Specific data protection requirements: Identify DLP solutions that address your unique data types, compliance needs, and risk profile.
• Existing IT infrastructure and security stack: Look for DLP solutions that integrate seamlessly with your current systems.
• Budget and resource constraints: Evaluate both initial costs and long-term expenses, including licensing, implementation, and ongoing management.
• Scalability and growth potential: Choose a DLP solution that can adapt to your business’ future needs.‍
• Ease of use and management: Prioritize solutions with user-friendly interfaces to reduce operational overhead.

What are the best data loss prevention solutions in 2024?

Nightfall AI

Nightfall AI is an AI-native data security platform that discovers and protects sensitive data across SaaS apps, GenAI apps, email, and endpoints. Nightfall's flexible APIs cover a wide variety of use cases, including:

• Preventing secret sprawl
• Preventing data exfiltration
• Safeguarding personal information
• Encrypting sensitive data
• Securing AI usage

No matter the use case, Nightfall's industry-leading detection engine has 4x fewer false positives than legacy DLP solutions. This increased accuracy ensures that no sensitive data slips between the cracks. It also means that security teams have the option to automate remediation, which can save time and help to maintain compliance with leading regulations like HIPAA, PCI-DSS, SOC 2, and beyond.

Get a demo of Nightfall here.

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Microsoft Purview DLP

Microsoft Purview offers comprehensive data protection across cloud, on-premises, and hybrid environments. Its integration with Microsoft 365 provides seamless coverage for email, SharePoint, and OneDrive, while advanced machine learning (ML) algorithms enhance data discovery and classification. Purview's policy enforcement and automated response features help maintain compliance with various regulations. However, some users find the initial setup complex, and may experience limitations in flexibility compared to other DLP solutions.

Google Cloud DLP

Google Cloud DLP is known for its strong integration with Google Cloud services and its ML-based content inspection. It provides effective data discovery, classification, and de-identification across cloud storage, databases, and big data processing frameworks. It's worth noting that Google Cloud DLP may be less suitable for businesses with a SaaS ecosystem that extends outside of Google Workspace.

Netskope DLP

Netskope is known for its seamless integrations with SaaS, IaaS, and web environments. It offers in-depth visibility and control over data, alongside real-time threat protection. Netskope’s granular policy controls and user behavior analytics enhance its data protection capabilities. However, users often note that deployment can be quite burdensome.

Zscaler DLP

Zscaler offers content inspection, policy enforcement, and incident management across multiple channels. Its global cloud architecture provides scalable performance for large enterprises. However, some users find Zscaler’s DLP complex to configure and manage.

Symantec DLP

Symantec, now integrated into Broadcom's portfolio, offers extensive data protection across endpoints, networks, and cloud environments. It features content-aware detection, user risk scoring, and automated incident response. It's worth noting that some users find Symantec to be resource-intensive and unwieldy in large environments.

Code42 DLP

Code42 is known for its strong focus on endpoint data protection and insider threat detection. However, Code42 may not offer as extensive coverage across cloud and SaaS environments compared to other DLP solutions.

Palo Alto Networks (PANW) DLP

Palo Alto Networks’ Enterprise DLP is part of a broader security ecosystem. It offers comprehensive protection across networks, clouds, and endpoints. Its integration with other Palo Alto security services and ML-based classification enhances its effectiveness. Nonetheless, its implementation can be resource-intensive, making it more suited to larger enterprises with significant security budgets.

Forcepoint DLP

Forcepoint excels in user behavior analytics and context-aware security policies. It provides features such as optical character recognition (OCR) and ML-based classification across various channels. Despite its strengths, Forcepoint can have a steep learning curve and may require extensive tuning to reduce false positives.

Proofpoint DLP

Proofpoint is known for its strong email security capabilities and comprehensive information protection approach. It offers advanced content inspection and ML techniques for data protection. However, Proofpoint's setup can be complex, and some users report high false positive rates.

What’s the TL;DR on DLP? 

The data protection landscape is always changing. In turn, it's important to stay informed about the latest DLP software and best practices. Be sure to evaluate your data protection toolkit regularly, and remember: DLP isn't just a one-time solution, but an ongoing investment in your future security.

FAQs about DLP

What is the difference between cloud-based and on-premises DLP solutions?

On-premises DLP solutions must be managed within a business’ infrastructure. This means that security teams have more direct control over data.

On the other hand, cloud-based DLP solutions are hosted and managed by vendors. This frees up valuable time and resources for security teams down the line.

How do DLP solutions detect sensitive data? 

DLP solutions use various techniques to detect sensitive data, including:

• Pattern matching for known data types and formats (e.g., credit card numbers, SSNs)
• Keyword and phrase detection
• Fingerprinting of sensitive documents
• ML and AI algorithms for context-aware classification
• ML-based Optical Character Recognition (OCR) for image-based text

Can DLP solutions prevent data loss from insider threats? 

Yes. Many DLP solutions offer features to mitigate insider threats. They may offer features such as user activity monitoring and user behavior analytics. Certain solutions may also cover role-based access control, or RBAC.

How do DLP solutions integrate with other security solutions? 

DLP solutions often integrate with other security solutions through APIs, plugins, or native integrations. Common integrations include:

• SIEM (Security Information and Event Management) systems
• Identity and Access Management (IAM) solutions
• Cloud Access Security Brokers (CASBs)
• Email security gateways
• Endpoint Detection and Response (EDR) tools

Are DLP solutions effective for protecting data in cloud environments? 

Yes. DLP solutions can integrate via API with SaaS apps and GenAI tools for real-time data protection. They can monitor data at rest, as well as data in motion across the cloud.

How often should you update your DLP policies?

DLP policies should be reviewed and updated regularly, ideally every 3-6 months, or whenever there are significant changes in:

• Regulatory requirements
• Business structure or data handling processes
• Types of data being collected or processed
• New technologies or services adopted by the business

What are the potential drawbacks of implementing DLP solutions? 

While DLP solutions offer many significant benefits, some potential drawbacks of legacy solutions include:

• Complex setup and configuration
• False positive alerts, which can impede productivity
• User resistance if policies are overly restrictive
• Costly ongoing maintenance and fine-tuning

While these issues often afflict legacy solutions, they’re less common with AI-powered solutions. AI-powered solutions leverage enhanced detection and automation for faster onboarding, fewer false positive alerts, less intrusive remediation, and lower cost of ownership.

How does AI enhance DLP solutions? 

AI and ML can enhance DLP solutions by:

• Improving accuracy in data classification
• Reducing false positives through context-aware detection
• Adapting to new patterns and threats automatically
• Providing more sophisticated user behavior analytics
• Automating policy recommendations and enforcement

Are there any open-source DLP solutions available? 

Yes, there are certainly open-source DLP solutions available. Open-source DLP solutions may be good options for businesses with a limited budget. However, these solutions may also demand higher operational costs due to ongoing maintenance, fine-tuning, and more.