Top 10 Nightfall Alternatives and Competitors

Nightfall AI has established itself as a modern leader in the data loss prevention (DLP) market, offering a cloud-native, API-driven platform built to protect sensitive data across SaaS, cloud, email, and generative AI applications. 

Nightfall makes it easy for organizations to get started quickly and secure their digital environments without deploying heavy agents or proxies.

This strength in accessibility and breadth has helped Nightfall gain traction among security teams looking for a lightweight way to monitor data exposure. However, as with any solution, there are natural trade-offs. 

Many teams report challenges with high false-positive rates, which can overwhelm analysts and disrupt workflows. Nightfall also lacks the ability to remediate historical data at scale, limiting organizations’ ability to efficiently clean up past exposures. And while it integrates with a wide range of applications, its coverage proves to be broad rather than deep - offering visibility across many apps, but limited granularity within each.

For some organizations, these gaps are manageable. For others, they open the door to exploring complementary or alternative solutions that address these challenges more directly - whether that’s reducing noise with richer context on users, enabling large-scale remediation of historical files, or delivering deeper integrations into specific SaaS platforms.

In this article, we’ll examine the top 10 Nightfall competitors and alternatives, their focus areas, strengths, and limitations, so you can evaluate which solution - or combination of solutions - best fits your organization’s data security strategy.

1. DoControl

Focus Areas(s): Data Access Governance, DLP, Shadow AI, Shadow Apps, ITDR 

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

Pros:

• Scalable API event-driven architecture that reacts in real-time
• Deep contextualized user data for accurate detection of events and risks
• AI classification and lineage engine to accurately detect sensitive content 
• Flexible policies that are easy to align to existing business processes
• Ability to build in approval processes to scalable engine 
• Ability to remediate historical data exposure on top of automated workflows 
• Coverage across 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. Microsoft Purview DLP

Focus Areas(s): 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 comes in 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 - high false-positives

4. Google Cloud DLP 

Focus Areas(s): DLP

Top Customers: Not available to the public 

Pros:

• Strong AI classification and labeling engine for Google Data
• Cost effective as comes in Google Enterprise package
• Easy to implement if within the Google ecosystem

Cons:

• Data access rules are too rigid
• No coverage beyond Google - requires additional tooling 
• No ability to remediate historical data exposure 
• No user context for HRIS / IdP tools, resulting in high false-positive rates

5. Netskope DLP

Focus Areas(s): DLP, Security Service Edge (SSE) / SASE

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 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 for scaling organizations
• Remediation options within SaaS are limited
• Limited user context for HRIS / IdP tools - high false-positives

6. Zscaler DLP

Focus Areas(s): 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 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
• Limited to no capabilities to take remediation actions within SaaS
• No user context for HRIS / IdP tools - high false-positives

7. Forcepoint DLP

Focus Areas(s): DLP 

Top Customers: Mariner Finance, Medicover Group, Gebauer & Griller, 

Pros:

• Agent-based and coverage across endpoint, cloud, SaaS, and email 
• Advanced content inspection and classifier engine that be customized to specific industries
• Integrate seamless into the Forcepoint ecosystem

Cons:

• Heavy-weight agent that has a long and complex deployment
• High-false positive rate that blocks business workflows and hinders user productivity
• Limited on the cloud side, and has a lot of overheard because of how customized each deployment is

8. Trellix (formerly McAfee) DLP

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 

9. BetterCloud

Focus Areas: CASB, DLP, User Management 

Key Customers: Bullhorn, Sprout Social, Bark, Classpass

Pros:

• Well known brand for overall SaaS Security
• Strong user management capabilities
• Unify CASB, DLP, and SaaS Management into one platform 

Cons:

• No SASE and ZTNA capabilities
• DLP engine is known to have inaccurate data - in some cases missing more than 30%
• Very limited remediation capabilities 

10. Egnyte

Focus Areas: DLP, DSPM 

Key Customers: Steve Madden, Redbull, Sargent

Pros:

• Strong capabilities for cloud content classification and governance
• Remediation capabilities available across various file sharing scenarios
• Great for SMBs, and easy to set up

Cons:

• Limited capabilities to support the scale of enterprise companies
• Classification engine can be limited depending on the organization

Conclusion

Nightfall AI has earned its place as a recognized leader in cloud-native DLP, particularly for organizations that want fast deployment, broad SaaS coverage, and machine learning-driven detection. 

For many, it provides a solid foundation. Yet, as data security challenges grow more complex, its limitations - such as higher false-positive rates, lack of large-scale historical remediation, and surface-level integrations - often prompt teams to look for additional or alternative solutions.

That’s why understanding the broader market of DLP competitors is so valuable. The reality is that:

1. No single tool fits EVERY data security need: It's unrealistic to think one DLP solution is the silver bullet to solving for DLP. Nightfall is strong, but limited - and companies need multiple layers of DLP.
   
   
2. Different tools for different ecosystems: SaaS-heavy firms may lean on DoControl or Cyberhaven; Microsoft shops may go native; and large, legacy enterprises may prefer Forcepoint, Trellix, or Netskope.
   
   
3. The future of DLP lies in multiple, integrated best of breed solutions: Security is changing, and so are customer expectations. The new trend in 2025 for DLP vendors is toward context-aware, SaaS-integrated, and AI-powered detection models that reduce noise and enable smarter, scalable remediation.
   

The final takeaway? Choosing the right DLP mix depends on your data flows, current priorities, existing workflows, compliance needs, and existing tech stack.

Ultimately, Nightfall has proven the value of modern, API-first data protection, but the next wave of solutions is going deeper - delivering contextual risk insights, historical and automated remediation, and rich integrations into core SaaS applications. 

By evaluating the strengths of Nightfall alongside its competitors, you can design a layered, future-ready DLP strategy that protects sensitive data without slowing down your business.

Want to Learn More?

• DoControl vs. Bettercloud – click here
• DoControl vs. Netskope – click here
• DoControl vs. Nudge - click here
• See a demo – click here
• Get a FREE Google Workspace Risk Assessment – click here
• See our product in action – click here