Top 10 DLP Solutions in 2025

In 2025, data is no longer confined to secure, on-premises servers – it moves constantly across Cloud platforms, mobile devices, SaaS apps, and hybrid environments. 

With this increased mobility comes increased risk. A single misplaced file, misconfigured permission, or insider threat can trigger a costly data breach, regulatory penalty, or reputational hit. That’s why Data Loss Prevention (DLP) has evolved from a “nice-to-have” into a core security capability for modern enterprises.

But here’s the challenge: not all DLP solutions are created equal. Some tools are rigid and reactive, others are difficult to scale, and too many rely on outdated detection models that can’t keep up with the fluid nature of today’s data flows. 

In this piece, we will break down the top 10 DLP solutions available in the market today, and break down their strengths, weaknesses, key focus areas, and more.

Selection Criteria 

When evaluating the top DLP platforms of 2025, we looked beyond the basic “checklist” features to identify the tools that actually help organizations protect sensitive information without slowing down productivity.

Our selection process focused on four key criteria:

1. Flexibility: A top-tier DLP solution must adapt to different architectures, workflows, and compliance needs – whether you’re fully in the Cloud, on-prem, or somewhere in between. Flexibility means integration with a wide range of SaaS tools and systems, along with the ability to enforce policies without disrupting legitimate work.

2. Contextual Awareness: The best DLP isn’t just looking for keywords – it understands context. That means recognizing whether a file contains PII, IP, or financial data, and then applying the right policy in real time. This can include pulling additional data and context from other systems, such as HRIS platforms, to identify user roles, employment status, or sensitivity levels tied to specific records. Contextual DLP drastically reduces false positives and ensures sensitive data is protected without creating alert fatigue.

3. Scalability – DLP has to work as well for 500 employees as it does for 50,000. Scalability means handling growing data volumes, supporting a distributed/remote workforce, and maintaining consistent policy enforcement without performance and operations slowdowns. The right solution protects the data while also enabling the business – not just making sure it runs smoothly, but that it runs better.

4. Remediation Capabilities – Detecting a policy violation is only half the job; what happens next matters just as much. Leading solutions provide automated remediation – quarantining a file, blocking a share, revoking permissions, or alerting security teams instantly. Remediation needs to be fast, flexible, and precise. It also needs to be done effectively at scale, ensuring teams can detect and respond in a way  that doesn't hinder business productivity.

No Single DLP Solution Covers It All…You Need a Best of Breed Approach

While these criteria give us a framework for comparison, it’s important to note that no single DLP solution can fully address every organization’s needs. Each platform / vendor has its strengths – some excel at SaaS app discovery, others are unbeatable for endpoint protection, and still others lead in insider threat detection or regulatory compliance automation.

For that reason, many companies end up using a combination of one, two, or even three DLP tools to achieve complete coverage. By layering different solutions – each with its own specialization – organizations can close gaps, reduce risk, and create a unified, multi-layered defense against data loss.

You’ll need a best of breed integrated approach, and these ranks are based on how well each vendor performs in their specific domain. 

With this in mind, our Top 10 DLP Solutions in 2025 list highlights where each vendor shines, where they have limitations, and how they can complement other tools in your security stack.

1. DoControl

Top customers: Colgate-Palmolive, Snap Inc., Databricks, Sanmina, Datadog

Pros:

• Scalable API event-driven architecture that reacts in real-time
• Deep contextualized user data from HRIS, IdP, & EDR for accurate detection and risk prioritization
• AI classification and lineage engine to accurately detect sensitive content within files
• Flexible policies that are easy to align and apply to existing business processes
• Ability to build in approval processes with end-user engagement to scalable engine 
• Ability to remediate historical data exposure on top of automated workflows 
• Coverage across tons of SaaS and Generative AI Apps 

Cons:

• No agent to take action on the Endpoint 

2. Cyberhaven 

Top customers: Motorola, Waxcare, Zoom, Upstart 

‍Pros:

• Strong workflow engine across SaaS, Cloud, and AI
• Agent in place to cover scenarios the Endpoint
• Strong AI classification and detection engine 
• Large list of apps to connect the dots between SaaS, Cloud, and Endpoint 

Cons:

• Agent-based – can often be too rigid for organizations, and drives a high false-positive rate
• Limited ability to engage end users in decision making and policies
• Limited contextual data from user data from HRIS / IdP systems
• No ability to remediate historical data exposure 

‍3. Nightfall 

Top customers: Synk, Aquia, Reltio, CapitalRx

Pros:

• Strong workflow engine for DLP across a broad set of SaaS apps
• Strong AI data classification engine with dynamic classifier engine 
• API engine that can respond to threats in real-time

Cons:

• No agent – therefore, can’t take action on Endpoint actions 
• Limited ability to engage end users 
• Limited contextual data from user data from HRIS / IdP 
• No ability to remediate historical data exposure – only can prevent future exposure 
• Limited user context for HRIS / IdP tools – leading to a high false-positive rate

4. Netskope DLP

Top customers: Triple A, Ross Stores, Yamaha, Sainsbury’s, JLL

Pros:

• Deep classifier engine that covers SaaS, Cloud, and Endpoint 
• Agent (inlined), and API options to cover both scenarios, and connect the dots between the two
• Can be bundled in with a broader Netskope offering

Cons:

• Very difficult to setup and maintain – high cost of ownership 
• API option is spotty, and data is often inaccurate
• Agent-based approach is often too rigid and black and white for scaling organizations
• Remediation options within SaaS are limited
• Limited user context for HRIS / IdP tools – leading to high false-positives

5. Google Cloud DLP 

Top Customers: Not available to the public 

‍Pros:

• Strong AI classification and labeling engine for Google Data
• Cost effective, as it’s included within the Google Enterprise package
• Easy to implement if being used within the Google ecosystem

Cons:

• Data access rules are very rigid – with black and white rules that hinder collaboration
• No coverage beyond Google – requires additional tooling 
• No ability to remediate historical data exposure 
• No user context for HRIS / IdP tools – leading to high false-positives

6. Microsoft Purview DLP 

Top Customers: Not available to the public 

Pros:

• Strong AI classification and labeling engine for MSFT data
• Decent workflow engine to enforce sharing policy controls
• Cost effective, as it comes included within the Microsoft E5 package

Cons:

• High cost of ownership, as it’s very difficult to setup and maintain
• No coverage beyond Microsoft – requires additional tooling 
• No ability to remediate historical data exposure 
• No user context for HRIS / IdP tools, leading to high false-positive rates

7. Zscaler DLP 

Top Customers: Protegrity, MGM Resorts International, Micron Technology, Amplifon

Pros:

• Strong inline DLP – inspecting traffic across Cloud, SaaS, and Endpoint
• Strong classifier engine across key verticals
• Can consolidate into a companies existing SSE stack 

Cons:

• Very difficult to setup and maintain – high cost of ownership
• Mostly focused on inline traffic (agent-based) approach – limited API capabilities – can’t catch BYOD
• Little to no capabilities to take remediation actions within SaaS
• No user context for HRIS / IdP tools – high false-positives

8. Symantec  

Top Customers: GoDaddy, EPAM Systems, SAP, Accenture, and Cognizant

Pros:

• Mature DLP platform that has advanced content inspection 
• Strong for organizations with regulatory needs – HIPAA, FEDRAMP
• Coverage across Endpoint, Cloud, Email, and some SaaS

Cons:

• Fully agent-based with very complex deployment and maintenance
• Limited SaaS abilities and almost no remediation – high-false positive rate because of agent 
• No contextual user data from HRIS / IdP tools 
• Best for Endpoint DLP, but struggles with Cloud and SaaS 

9. Trellix 

Top Customers: Not available to the public 

Pros:

• Broad DLP capabilities across Email, Cloud, and Network  
• Strong for highly regulated organizations who aren’t as worried about business/operations flow 
• Centralized threat intel from McAfee and FireEye connections 

Cons:

• Heavy agent, and very difficult to implement and maintain
• High false-positive rate, and almost no effective remediation capabilities for SaaS 
• No user context for HRIS / IdP tools – leading to high false-positives 

10. Code42 

Top Customers: Banked, Lyft, Okta, Snowflake 

Pros:

• Broad DLP capabilities across Email, Cloud, and Network 
• Strong on Endpoint with some capabilities via an API 
• Strong risk prioritization and insider risk analysis 

Cons:

• Classifier engine is lacking with certain capabilities 
• API capabilities are limited – struggling to effectively remediate and protect SaaS
• User feedback suggests that the solution is very difficult to implement 

The Takeaway?

Choosing the right Data Loss Prevention solution isn’t just about checking a box. Not every DLP solution is perfect – what matters is about finding the one that's perfect for your organization.

In evaluating the top players in the market, we looked beyond the marketing copy and focused on what truly matters for DLP in 2025: 

• Flexibility to adapt to your business processes quickly and easily
• Contextual awareness to accurately identify and prioritize sensitive data exposures, risks, and threats
• Scalability to protect information across the ever-expanding SaaS surface, ensuring your security grows alongside your company and your stack
• Remediation capabilities that resolve issues fast without hindering operations

Again, it’s important to recognize that there’s rarely a single “silver bullet” for DLP. Each vendor brings different strengths to the table – each solution has its own nuances and approach. That’s why many organizations take a layered approach, combining multiple solutions to achieve unified coverage and minimize gaps.

The vendors on this list all bring something valuable to the table, but the right choice will be the one that meets today’s needs while preparing you for tomorrow’s threats.