SentinelOne Alternatives

SentinelOne provides endpoint, cloud, identity, and AI security capabilities, including threat detection, AI usage governance, and data leakage prevention. Organizations researching SentinelOne alternatives may also be exploring complementary data loss prevention capabilities for sensitive information moving across human and AI-agent workflows. As AI agents access enterprise data through copilots, coding tools, SaaS applications, endpoints, and Model Context Protocol (MCP) connections, controlling that movement has become an important security priority.

This guide examines eight platforms with different approaches to data security, including Nightfall AI, a unified AI data security platform that provides visibility, detection, and real-time policy enforcement across supported human and agentic workflows.

Key Takeaways

• Endpoint protection and data loss prevention solve different problems: SentinelOne combines AI security and threat response with real-time data leakage prevention and agent security controls. Organizations often deploy both endpoint protection and specialized data security capabilities together.
• AI agents create new data movement risks: AI agents can access, transform, and transmit sensitive information through copilots, MCP servers, and IDE-embedded tools. Nightfall provides AI data security capabilities for supported human and agentic workflows, including local and remote MCP connections.
• AI-native detection improves data security visibility: Nightfall uses machine learning and contextual classification to identify sensitive information across supported workflows. The platform reports 95% detection precision from limited-period proof-of-value results, helping security teams identify relevant data risks and prioritize investigations.
• Rapid initial deployment supports time to value: Nightfall reports that an initial supported SaaS application can be connected in approximately 10 minutes and endpoint agents can be distributed through mobile device management (MDM) in approximately 30 minutes. Full implementation timelines depend on deployment scope, policy configuration, testing, and integrations.
• MCP introduces additional data security requirements: The Model Context Protocol enables AI agents to interact with enterprise applications and data sources through local and remote connections. Nightfall provides discovery, policy enforcement, and monitoring across supported local stdio, remote HTTP, and MCP gateway workflows.

1. Nightfall AI

Nightfall AI is a unified AI data security platform that governs sensitive data movement across human activity and AI-agent workflows. It provides real-time visibility, detection, and policy enforcement across supported SaaS applications, endpoints, browsers, email, AI tools, and MCP workflows. Backed by Bain Capital Ventures, Venrock, and cybersecurity leaders including Kevin Mandia, Freddy Kerrest, and Doug Merritt, Nightfall helps security teams govern how sensitive information is accessed, moved, and exposed. AI moves your data. Nightfall controls it.

How Does Nightfall AI Work?

Nightfall's AI data security platform provides real-time detection, classification, and policy enforcement across supported SaaS, endpoint, browser, email, and AI-agent workflows. Available controls depend on the integration and deployment configuration. Key capabilities include:

• Deployment: Nightfall reports that an initial supported SaaS application can be connected through native APIs in approximately 10 minutes. Endpoint agents can be distributed through MDM in approximately 30 minutes. Nightfall reports an endpoint footprint of approximately 1% CPU utilization and 50 MB of RAM, with actual resource usage depending on the device and configuration.
• Detection: Nightfall uses AI-native detection powered by supervised fine-tuned models, including machine learning detectors for PII, PHI, secrets, credentials, and financial data, along with LLM-based classifiers spanning more than 20 categories. Nightfall reports 95% precision from limited-period proof-of-value results.
• Control: Nightfall supports real-time blocking, end-user coaching, approval workflows, and remediation actions such as redaction, deletion, access revocation, quarantine, and encryption. Available actions vary by integration, data movement channel, and policy configuration.
• AI Agent Coverage: Native MCP security covering local stdio, remote HTTP, and gateway MCP paths plus IDE hooks for Cursor, Claude Code, and VS Code.

Capabilities and Reported Outcomes

Nightfall reports the following platform capabilities, customer adoption information, and operational benefits:

• Nightfall offers a unified platform with pricing and packaging options for SaaS, endpoint, and AI-agent security, helping organizations consolidate data security management across supported workflows.
• AI-assisted incident analysis, policy recommendations, and data-movement telemetry help security teams prioritize investigations and response activities.
• 100+ organizations run on Nightfall, including Gusto, DraftKings, Grafana Labs, Grab, Nubank, and Decagon.
• Native integrations with 12+ SaaS and email applications, including Slack, Google Drive, GitHub, Jira, Confluence, Salesforce, and Microsoft 365, alongside supported ChatGPT security controls.

Privacy and Compliance

Nightfall reports a SOC 2 Type II attestation and ISO 27001 certification. Its AI data security platform provides sensitive-data detection, policy enforcement, and investigation capabilities that can support organizations' efforts to address applicable HIPAA, PCI DSS, GDPR, and CCPA requirements. Compliance depends on each organization's implementation, policies, processes, and regulatory obligations.

What Makes Nightfall Unique

• Purpose-Built for Data Movement: Nightfall applies sensitive-data detection and policy enforcement across supported human and AI-agent workflows, including SaaS applications, endpoints, browsers, email, and MCP connections.
• Native MCP Security: Nightfall supports discovery, monitoring, and policy enforcement for local stdio, remote HTTP, and MCP gateway workflows. These capabilities help security teams govern sensitive data accessed and transferred through supported agentic connections.
• One Detection Brain: Nightfall applies a shared detection engine and policy framework across supported SaaS applications, endpoints, browsers, email, AI applications, and AI-agent workflows. This unified architecture supports centralized visibility, investigation, and management, with enforcement actions determined by the relevant integration and licensed capabilities.
• AI-Native Investigation: Nightfall's AI-native investigation capabilities help identify risky users, recommend policies, analyze incidents, and provide contextual telemetry for security investigations across supported data movement channels.

Best For: Organizations seeking a unified AI data security platform with AI-native detection, real-time enforcement, and centralized investigation across supported human and AI-agent workflows, including SaaS applications, endpoints, and MCP connections.

2. CrowdStrike Falcon

CrowdStrike Falcon combines endpoint detection and response, extended detection and response, threat intelligence, and incident response capabilities. Its Falcon Data Security offering extends the platform into sensitive-data discovery, classification, and protection across endpoints, browsers, SaaS applications, GenAI workflows, and cloud environments.

Key Features

• Next-generation antivirus and endpoint detection
• XDR capabilities for correlation across endpoint, cloud, and identity telemetry
• Falcon Data Security for sensitive-data discovery, classification, and real-time data movement controls
• Managed detection and response through Falcon Complete
• Threat intelligence informed by global telemetry

Primary Focus

CrowdStrike's Falcon Data Security module adds cross-channel data protection capabilities to the core endpoint platform. Falcon Data Security covers endpoints, browsers, SaaS, GenAI, and cloud data movement, while CrowdStrike AIDR provides MCP traffic inspection and policy enforcement. Falcon Data Security is managed within the Falcon platform.

Considerations

CrowdStrike combines endpoint threat protection, Falcon Data Security, and AI Detection and Response within the Falcon platform. Falcon Data Security provides classification and data movement controls across supported channels, while CrowdStrike AIDR supports AI-application, agent, and MCP security through its supported collection and enforcement methods.

Primary Use Case: Organizations with existing CrowdStrike deployments seeking to add cross-channel DLP capabilities without introducing another vendor, particularly those prioritizing endpoint threat detection.

3. Microsoft Purview DLP

Microsoft Purview DLP provides native data loss prevention within the Microsoft 365 ecosystem. The platform integrates natively with Exchange, SharePoint, OneDrive, and Teams, offering unified compliance management for Microsoft-centric environments.

Key Features

• Native integration with Microsoft 365 applications
• Sensitivity labels for document classification
• Policy enforcement across the Microsoft ecosystem
• Unified compliance dashboard
• Integration with Microsoft Defender for Endpoint

Primary Focus

Microsoft Purview combines sensitivity labels, sensitive information types, exact data matching, and machine learning-based trainable classifiers to identify and protect sensitive content. It supports Microsoft 365 workloads, endpoint DLP, browser-based controls, and selected AI application scenarios. Available functionality depends on the workload, applicable licensing, and configuration.

Considerations

Microsoft Purview provides native Microsoft 365 DLP controls alongside endpoint and browser protection, Microsoft 365 Copilot governance, and supported third-party application integrations. It also offers DLP capabilities for selected non-Microsoft connected applications, including features available in preview. Its policies and enforcement options are configured according to the supported application, workload, and licensing model.

For organizations evaluating alternatives, review the Microsoft Purview DLP alternatives guide for detailed capability comparisons.

Primary Use Case: Organizations using Microsoft 365 that want integrated data classification, DLP, endpoint protection, and compliance management, with additional controls available for supported third-party applications and AI workflows.

4. Cyberhaven

Cyberhaven combines data lineage tracking with DLP, data security posture management, insider risk management, and AI security capabilities. The platform provides visibility into how data moves through transformations across enterprise workflows, with emphasis on tracing data from origin through various file formats and destinations.

Key Features

• Data lineage tracking from origin through transformations
• Endpoint DLP capabilities
• Visibility into data flow patterns
• User behavior context
• Integration with existing security infrastructure

Primary Focus

Cyberhaven's architecture prioritizes understanding data provenance and transformation history. Cyberhaven has expanded its lineage-first platform with AI-agent discovery, observability, and runtime controls for supported workflows. Organizations should validate enforcement depth across local MCP connections, IDE-embedded agents, and tool interactions. This approach helps organizations understand complex data flows where a Salesforce CSV becomes an Excel file, then a Word document, then a PDF before reaching its final destination.

Considerations

Lineage-first architectures create comprehensive documentation of data transformation paths. Cyberhaven uses data lineage to connect events with their data origin and transformation history. Cyberhaven's combination of lineage tracking with AI-agent visibility addresses both historical data flow documentation and modern AI-agent data governance requirements.

For organizations comparing options, the Cyberhaven alternatives guide provides additional context on capability differences.

Primary Use Case: Organizations prioritizing exhaustive data lineage documentation for compliance purposes, particularly those with complex data transformation workflows and AI-agent data governance requirements.

5. Cyera

Cyera provides DSPM, Omni DLP, and AI Runtime Protection capabilities, combining sensitive-data discovery with data-in-motion security and AI runtime controls. The platform focuses on discovering and classifying sensitive data at rest across cloud environments and databases while also addressing real-time data movement.

Key Features

• Data discovery across cloud environments
• Automated data classification
• Security posture assessment
• Risk prioritization based on data sensitivity
• Cloud-native data security architecture
• Omni DLP controls for supported data movement channels
• AI Runtime Protection for supported AI applications and agentic workflows

Primary Focus

Cyera helps organizations understand what sensitive data exists and where it resides. Cyera combines data-at-rest discovery with Omni DLP and AI Runtime Protection, including real-time policy controls for AI applications, prompts, and agents. This visibility supports compliance initiatives and risk assessments by cataloging data assets across cloud infrastructure while enforcing runtime data security policies.

Considerations

Cyera connects DSPM-based discovery with runtime protection, supporting both data inventory and policy enforcement for supported data movement workflows.

The Cyera alternatives guide offers additional perspective on how DSPM fits within broader data security strategies.

Primary Use Case: Organizations seeking visibility into sensitive data locations for compliance and risk assessment purposes, including organizations combining posture management with runtime data-loss controls.

6. Netskope

Netskope provides a cloud access security broker (CASB) and secure access service edge (SASE) platform with DLP capabilities integrated into its network security architecture.

Key Features

• Cloud access security broker functionality
• Secure web gateway
• Zero trust network access
• Integrated DLP for cloud applications
• Inline and API-based deployment options

Primary Focus

Netskope combines network-based security inspection, cloud application protection, and DLP within its broader SASE architecture. Its capabilities include inline and API-based controls, endpoint AI asset discovery, and Agentic Broker functionality for supported MCP communications. These components support policy enforcement and visibility across configured web, cloud, endpoint discovery, and agentic workflows.

Considerations

Netskope provides complementary deployment options through network security inspection, cloud application integrations, endpoint AI discovery, and Agentic Broker. These capabilities form part of its broader security architecture for enterprise application access, sensitive-data movement, and supported AI workflows.

Organizations evaluating network-based approaches can review the Netskope DLP alternatives for capability comparisons.

Primary Use Case: Organizations seeking integrated SASE, cloud access security, DLP, endpoint AI discovery, and policy controls for supported enterprise web, SaaS, and agentic workflows.

7. Forcepoint

Forcepoint delivers enterprise DLP with coverage across endpoints, network, and cloud applications. The platform has evolved from traditional DLP roots to address modern cloud environments and AI security challenges.

Key Features

• Endpoint DLP agent
• Network DLP for email and web traffic
• Cloud application coverage
• Policy management across channels
• Incident management workflows

Primary Focus

Forcepoint provides enterprise DLP capabilities combining data classification, contextual policy enforcement, and protection across endpoint, network, cloud, and AI environments. Its AI Data Security offering, announced in July 2026, includes AI visibility and governance, sensitive-data protection, and an AI Agent Gateway with a phased availability schedule. These capabilities extend Forcepoint's enterprise data security portfolio into supported autonomous agent workflows.

Considerations

Forcepoint combines enterprise DLP, risk-adaptive data protection, classification, and AI security capabilities. Its AI Data Security offering includes agent visibility, policy management, and AI Agent Gateway functionality, with availability dependent on the relevant release and deployment configuration.

For organizations comparing legacy and modern approaches, the Forcepoint DLP alternatives guide provides detailed analysis.

Primary Use Case: Enterprises seeking integrated data classification, cross-channel DLP, risk-adaptive protection, and AI data security controls within an established enterprise security program.

8. Proofpoint

Proofpoint provides DLP across email, endpoints, cloud applications, and GenAI, alongside AI-agent and MCP security capabilities. The company emphasizes human-centric security addressing email-based data loss vectors while extending coverage to modern data movement surfaces.

Key Features

• Email DLP and encryption
• Outbound email content inspection
• Cloud application coverage
• Archive and compliance capabilities
• User awareness training integration
• AI-agent and MCP security capabilities within its broader security portfolio

Primary Focus

Proofpoint provides email security, data loss prevention, and human-centric security capabilities across enterprise communication and data movement workflows. Its broader data security portfolio includes endpoint and cloud protection, GenAI controls, and dedicated AI-agent and MCP security offerings. These capabilities support organizations managing sensitive data across both human activity and supported AI-driven workflows.

Considerations

Email represents one data movement channel among many in modern enterprise environments. Proofpoint combines email DLP, cross-channel data protection, and AI-agent security capabilities.

The Proofpoint DLP alternatives guide offers additional comparison points for organizations evaluating options.

Primary Use Case: Organizations seeking integrated email security, cross-channel DLP, insider risk context, and AI data protection capabilities, including supported AI-agent and MCP security workflows.

Comparison Summary

The eight platforms examined in this guide represent different approaches to enterprise data security. Nightfall focuses on sensitive data movement across human and AI-agent workflows. CrowdStrike combines endpoint security with cross-channel data security and AI detection and response. Microsoft Purview integrates DLP with Microsoft 365 and supported external environments. Cyberhaven emphasizes data lineage alongside DLP and AI security, while Cyera combines DSPM with Omni DLP and AI Runtime Protection. Netskope integrates DLP within its SASE and AI security architecture, while Forcepoint and Proofpoint offer enterprise data security portfolios that include AI-related capabilities.

Each approach delivers value for specific use cases and environments. The key differences lie in detection architecture, deployment model, AI agent coverage, and MCP security capabilities.

Why Nightfall AI Stands Out for Data Security

Purpose-Built for the AI Era

AI has changed both how enterprise data moves and who moves it. Human users now work alongside AI assistants and autonomous agents that access information through applications, local files, MCP servers, and chained workflows. Nightfall addresses this environment with an AI data security platform that applies detection, policy enforcement, and investigation capabilities across supported human and agentic data movement channels.

Native MCP and AI Agent Security

Nightfall provides native MCP security capabilities across supported local stdio, remote HTTP, and MCP gateway workflows. These capabilities help govern sensitive data moving through AI agents and connected tools. Nightfall also provides IDE hooks for Cursor, Claude Code, and VS Code, supporting policy enforcement within configured AI-assisted development workflows.

Detection Precision That Reduces Operational Burden

Nightfall uses transformer-based models and contextual classification to identify sensitive information across supported workflows. The company reports approximately 95% detection precision from limited-period proof-of-value results. Its detection and investigation capabilities are designed to help security teams prioritize relevant incidents, with actual performance depending on data types, policies, and deployment configuration.

Rapid Initial Deployment

Nightfall's API-based integrations allow organizations to connect an initial supported SaaS application in approximately 10 minutes without routing that application's traffic through a network proxy. Its endpoint DLP agent can be distributed through MDM in approximately 30 minutes, according to Nightfall's published deployment guidance. These milestones describe initial setup; broader production rollout depends on endpoint fleet size, integrations, policy configuration, and testing.

Unified Platform for Consolidated Security

Nightfall brings data loss prevention, insider risk context, and AI data security capabilities into a unified platform. Its shared detection engine and policy framework support consistent classification and centralized management across supported SaaS applications, endpoints, browsers, email, and AI-agent workflows. This architecture can help organizations simplify data security operations and coordinate enforcement across multiple data movement channels.

Real-Time Control, Not Just Visibility

Visibility alone is not enough to govern sensitive data movement. Nightfall supports real-time enforcement through blocking, coaching, policy overrides, and manual or automated approval workflows. Supported integrations also provide remediation actions such as redaction, deletion, access revocation, quarantine, and encryption. Available actions depend on the protected surface and policy configuration, enabling security teams to respond to risky data movement with controls appropriate to each workflow.

For security professionals evaluating alternatives to endpoint-focused platforms like SentinelOne, Nightfall's combination of AI-native detection, native MCP security, and comprehensive data movement control makes it a strong option for organizations securing sensitive data across growing AI workflows. Schedule a demo to see how Nightfall protects data movement across your environment.

Frequently Asked Questions

What is the fundamental difference between SentinelOne and data loss prevention platforms?

SentinelOne provides endpoint threat protection, detection and response, and AI security capabilities, including controls for AI-related data leakage. Nightfall is an AI data security platform focused on governing sensitive data movement across supported human and AI-agent workflows. These capabilities address overlapping but distinct security requirements and can complement one another within an enterprise security architecture. Data security controls can also support applicable compliance obligations, but deploying either platform does not automatically establish regulatory compliance.

Can SentinelOne replace a dedicated DLP solution?

SentinelOne completed its acquisition of Prompt Security in September 2025, expanding its AI usage governance, data leakage prevention, and agent security capabilities. Whether SentinelOne serves as an organization's primary DLP platform depends on its security architecture, required applications, enforcement channels, and licensing. Nightfall provides a unified AI data security platform focused on sensitive-data detection and policy enforcement across supported SaaS, endpoint, browser, email, and agentic workflows, and can operate alongside endpoint detection and response platforms.

Why is MCP security important when evaluating data security platforms?

The Model Context Protocol (MCP) enables AI agents to interact with enterprise applications, tools, and data sources through standardized connections. These interactions can involve sensitive information, making visibility and policy enforcement important for agentic workflows. Nightfall provides MCP security capabilities across supported local stdio, remote HTTP, and gateway connections, along with IDE-level controls for supported development environments.

How does AI-native detection differ from traditional DLP approaches?

DLP platforms use different combinations of rules, pattern matching, machine learning, and contextual classification to identify sensitive information. Nightfall uses AI-native models trained on labeled sensitive-data examples and reports approximately 95% detection precision in limited-period proof-of-value results. Its contextual detection and investigation capabilities are designed to help security teams prioritize relevant data movement incidents.

What deployment timeline should organizations expect for modern DLP platforms?

DLP deployment timelines vary according to the applications, endpoints, policies, and enforcement controls involved. Nightfall reports that an initial supported SaaS application can be connected in approximately 10 minutes, and endpoint agents can be distributed through MDM in approximately 30 minutes. These figures represent initial deployment milestones. Full production rollout depends on organizational scale, policy testing, integrations, and operational requirements.