Best Agentless (API-Based) DLP Solutions in 2026

Legacy data loss prevention was designed primarily around human-initiated movement through files, email, web traffic, and endpoints. In 2026, sensitive data also moves through SaaS applications, copilots, AI coding tools, APIs, and Model Context Protocol workflows. That shift makes agentless DLP valuable, but it also changes what security teams should expect from an API-based architecture.

Agentless DLP generally connects directly to supported SaaS and cloud applications through APIs, allowing organizations to discover, classify, monitor, and remediate sensitive data without installing an endpoint agent for those specific integrations. API coverage is especially useful for sensitive data already stored in cloud applications. For broader data exfiltration prevention, modern architectures can combine SaaS APIs with endpoint, browser, email, and AI agent controls so policy follows data across more of the environment.

Nightfall AI leads this list because it treats API-based SaaS protection as one layer of a broader AI data security platform. Nightfall uses one AI-native detection and policy framework across SaaS, endpoints, browsers, email, AI applications, and MCP workflows, giving organizations a unified control plane for both human and agentic data movement.

Key Takeaways

• Agentless DLP is strongest as part of a layered architecture: SaaS APIs provide direct visibility into supported cloud data, while endpoint, browser, email, and agentic controls extend enforcement to additional data movement paths.
• AI-native detection matters more as data movement becomes contextual: Nightfall reports 95% detection precision out of the box and uses content and context to distinguish legitimate activity from risky exfiltration.
• AI agents create a new data control plane: Local stdio MCP servers, remote MCP connections, IDE-embedded agents, AI assistants, and autonomous workflows introduce data paths that traditional SaaS API scanning alone does not address.
• Prevention should accompany discovery: Data discovery and classification provide important context, but organizations also need controls that can block, coach, redact, revoke, quarantine, encrypt, or otherwise remediate sensitive data movement at supported enforcement points.
• Platform consolidation can reduce operational fragmentation: A common detection engine and policy framework across DLP, insider risk, AI governance, and agentic security can simplify investigation and response.

1. Nightfall AI

Nightfall is the AI data security platform built to control AI agents and all data they touch. It governs sensitive data movement across SaaS applications, endpoints, email, browsers, AI applications, and MCP servers with a shared detection and policy framework. In the agentless context, Nightfall uses direct API integrations for supported SaaS applications while extending protection through complementary endpoint, browser, and AI agent controls.

This architecture is designed around a simple principle: AI moves your data. Nightfall controls it.

How Does Nightfall AI Work?

Nightfall connects to supported SaaS and email applications through native DLP integrations, including Slack, Google Drive, Gmail, Microsoft Teams, OneDrive, SharePoint Online, Exchange Online, GitHub, Salesforce, Jira, Confluence, Notion, and Zendesk.

Across these surfaces, Nightfall applies AI-native detection to sensitive information such as PII, PHI, PCI data, secrets, credentials, source code, financial data, and sensitive document classes. Its API-based SaaS integrations support real-time and historical scanning, while its broader platform adds endpoint and browser DLP and protection for supported AI applications.

Key Capabilities:

• AI-powered detection: Nightfall reports 95% detection precision out of the box using AI-based models, LLM-powered file classifiers, and contextual detection rather than relying on pattern matching alone.
• MCP and AI agent security: MCP security covers local stdio and remote HTTP MCP workflows, IDE hooks, risk scoring, tool classification, prompt injection detection, and inline controls for supported agentic traffic.
• Real-time remediation: Nightfall supports surface-appropriate actions such as block, coach, redact, delete, revoke access, quarantine, encrypt, monitor, and notify.
• Autonomous investigation: Nyx, Nightfall's autonomous DLP analyst, helps investigate incidents, identify patterns, surface risky users, and recommend policy or response actions.
• Rapid deployment: Nightfall's API-based SaaS integrations deploy in minutes, and endpoint coverage can be distributed through standard MDM workflows. Pre-trained detection models support early protection without requiring teams to build a large regex library first.
• Lightweight endpoint coverage: Nightfall reports an endpoint footprint of approximately 1% CPU and 50MB of RAM, with macOS and Windows parity.

What Makes Nightfall Different?

Nightfall is built around one detection brain across human and AI-driven data movement. That means the same core classification and policy logic can follow risk from SaaS data to endpoints, browsers, AI applications, and MCP workflows instead of treating each surface as a separate security problem.

Its AI agent security is particularly important for organizations adopting Cursor, Claude Code, VS Code, copilots, and MCP-connected tools. These workflows can access local files, enterprise SaaS data, and remote services in ways that conventional API-only DLP does not cover on its own.

Nightfall also combines prevention with data discovery, so organizations can identify sensitive data while actively controlling how it moves. This prevention-first model gives security teams operational value from the beginning rather than making runtime control dependent on a separate posture project.

Best For: Organizations that want one AI data security platform for SaaS DLP, endpoint and browser protection, insider risk, AI governance, and MCP security, with AI-native detection and real-time enforcement across supported surfaces.

2. Strac

Strac provides DLP and data security capabilities across SaaS applications, cloud environments, endpoints, databases, and GenAI tools. It supports integrations across these environments and includes endpoint coverage for macOS, Windows, and Linux.

Core Capabilities

• Integration support: Strac supports SaaS, cloud, database, endpoint, and GenAI integrations.
• Endpoint coverage: The platform supports macOS, Windows, and Linux endpoints.
• Data handling controls: Strac supports actions such as redaction, masking, tokenization, and vaulting for supported workflows.
• MCP coverage: Strac supports MCP-related DLP controls for AI agent workflows.
• DSPM and DLP: The platform combines data discovery and posture capabilities with policy enforcement.

Platform Approach

Strac supports organizations that want integration coverage across SaaS, cloud, databases, endpoints, and GenAI, along with Linux endpoint support and data handling controls such as tokenization and vaulting. Its combination of DLP and DSPM brings discovery and enforcement into the same product family.

Nightfall differentiates through its AI-native control-plane architecture. The same detection and policy framework governs supported SaaS, endpoint, browser, email, and agentic surfaces, including local and remote MCP workflows and native IDE hooks. This gives organizations a consistent data-centric policy model across both human and AI-driven activity.

Typical Fit: Organizations seeking DLP and DSPM capabilities with Linux endpoint support, database coverage, and sensitive data transformation options.

3. Microsoft Purview DLP

Microsoft Purview DLP provides native data loss prevention across Microsoft 365 workloads. It integrates with SharePoint, OneDrive, Teams, Exchange, sensitivity labels, Microsoft Defender, and Microsoft 365 Copilot controls.

Core Capabilities

• Microsoft 365 integration: Purview provides native DLP policy coverage across supported Microsoft workloads.
• Sensitivity labels: Organizations can use sensitivity labels as conditions within supported DLP policies.
• Copilot governance: Purview includes controls for Microsoft 365 Copilot interactions within the Microsoft ecosystem.
• Defender integration: DLP alerts can feed into Microsoft security investigation workflows.
• Connected application support: Microsoft also documents preview DLP support for selected non-Microsoft SaaS applications through connected application capabilities, with feature availability depending on the workload and configuration.

Platform Approach

Purview supports organizations standardized on Microsoft 365 by using classifications, identities, labels, and administrative workflows already present in that environment. Its DLP model is closely integrated with Microsoft workloads across the collaboration and productivity stack.

Nightfall's differentiation is broader cross-surface AI data control. It extends one AI-native detection framework across SaaS, endpoints, browsers, AI applications, and MCP workflows, including agentic activity outside the Microsoft ecosystem. The Nightfall vs Microsoft Purview comparison places these approaches in the context of a wider data movement architecture.

Typical Fit: Microsoft-centric organizations that want native DLP and information protection capabilities across Microsoft 365 and related services.

4. Cyera

Cyera provides a data security platform that combines agentless data security posture management, data discovery and classification, DLP decisioning, and AI runtime protection. Its platform supports organizations that prioritize cloud data visibility and posture context.

Core Capabilities

• Agentless DSPM: Cyera discovers and classifies sensitive data across supported cloud and data environments.
• Contextual risk analysis: The platform combines sensitivity, identity, access, and exposure context to prioritize data risk.
• Omni DLP: Cyera can centralize analysis and decisioning across supported DLP enforcement points.
• AI runtime protection: Browser and API-layer controls extend protection into supported AI interactions and custom AI applications.
• AI agent security: Cyera's Agent Guardian provides visibility and control for supported AI agents from discovery through runtime enforcement.

Platform Approach

Cyera combines data posture, discovery, runtime protection, and AI agent security for organizations that want a data-centric inventory and risk model connected to DLP operations.

Nightfall differentiates by making data movement control the organizing layer across SaaS, endpoints, browsers, email, and MCP workflows. One AI-native detection framework applies policy to both human and agent activity, while discovery and classification remain part of the same prevention workflow.

Typical Fit: Organizations prioritizing cloud data discovery and posture management alongside centralized DLP analysis, AI runtime controls, and agent security.

5. Netskope DLP

Netskope DLP is part of the Netskope One Security Service Edge platform. It combines inline security controls with API-based protection for supported cloud applications and provides DLP capabilities within a broader SSE architecture.

Core Capabilities

• SSE integration: DLP operates within Netskope's broader web, cloud, and access security platform.
• Inline and API coverage: Netskope combines traffic inspection with API-based scanning for supported SaaS applications.
• Classification: The platform supports machine learning and file classification capabilities.
• Cloud application controls: Netskope provides policy controls across sanctioned and unsanctioned cloud usage through its supported enforcement modes.
• Agentic AI controls: Netskope Agentic Broker provides visibility and policy controls for supported MCP traffic and integrates with Netskope DLP, while Netskope also documents endpoint discovery for supported local AI assets.

Platform Approach

Netskope supports organizations that use SSE as a primary control plane for web and sanctioned SaaS traffic. API scanning complements that inline architecture by extending visibility to supported cloud data at rest, and its agentic security capabilities add controls for supported MCP workflows.

Nightfall can complement an SSE deployment with one AI-native data policy framework across SaaS, endpoint, browser, email, AI application, and MCP surfaces. Nightfall's documented local stdio MCP coverage and native IDE hooks operate under the same detection framework used for other protected data movement. The Nightfall vs Netskope comparison covers the two architectures in more detail.

Typical Fit: Organizations already invested in Netskope that want DLP integrated with their SSE, cloud security, and agentic security architecture.

6. Zscaler DLP

Zscaler DLP provides centralized data protection within the Zscaler platform across web, endpoint, email, SaaS, cloud, and GenAI use cases. It combines inline cloud security controls with endpoint capabilities and data-at-rest scanning for supported SaaS environments.

Core Capabilities

• Inline DLP: Zscaler provides policy enforcement for supported web, SaaS, email, and GenAI traffic routed through its platform.
• Endpoint DLP: Zscaler supports endpoint controls for device data and common exfiltration channels.
• Data-at-rest scanning: The platform can discover and protect sensitive data in supported sanctioned SaaS applications.
• Centralized classification and policy: Zscaler applies shared DLP policies and classification methods across supported channels.
• Agentic AI controls: Zscaler has announced AI Broker capabilities for MCP and A2A communications together with endpoint AI security for supported agentic use cases.

Platform Approach

Zscaler supports organizations that use its security cloud and want DLP integrated with a broader zero trust and SSE architecture. Its 2026 AI security portfolio also addresses supported agentic AI communications and endpoint AI activity.

Nightfall differentiates through a data-centric control plane that applies the same AI-native detection engine across SaaS, endpoint activity, browsers, email, AI applications, and local or remote MCP workflows. Its documented local stdio MCP coverage and native IDE hooks are governed by that same detection and policy framework. The Nightfall vs Zscaler comparison provides additional architectural context.

Typical Fit: Organizations standardized on Zscaler that want DLP integrated into their existing zero trust, SSE, and AI security architecture.

7. Forcepoint DLP

Forcepoint DLP provides enterprise data protection across cloud, web, email, endpoints, supported AI use cases, and related hybrid environments. It supports centralized policy management, endpoint controls, data discovery, reporting and forensics, and predefined compliance policies and classifiers.

Core Capabilities

• Cross-channel DLP: Forcepoint supports policy enforcement across cloud, web, email, and endpoint channels.
• Compliance templates: The platform includes predefined policies and classifiers for common regulatory and sensitive data requirements.
• Endpoint protection: Forcepoint supports Windows and macOS endpoint controls, including common file and device activity.
• Hybrid enterprise support: Forcepoint supports organizations with a mix of cloud and on-premises data security requirements.
• AI-related data protection: Forcepoint supports data protection for AI and GenAI use cases, including supported autonomous agent, Shadow AI, and sanctioned AI application workflows.

Platform Approach

Forcepoint has a long enterprise DLP history and supports regulated organizations with predefined policy libraries, hybrid deployment options, centralized controls across multiple enterprise data channels, and AI data security use cases.

Nightfall differentiates through an AI-native detection and policy framework that spans SaaS, endpoints, browsers, email, and documented AI agent and MCP controls. Its local stdio and remote MCP coverage plus native IDE hooks are part of the same data-security control plane used for human-driven data movement. The Nightfall vs Forcepoint comparison provides additional context.

Typical Fit: Regulated enterprises that want predefined DLP policy libraries, endpoint controls, hybrid data security support, and AI-related data protection.

Why Nightfall AI Stands Out for Agentless DLP

One Detection Brain Across Every Surface

Nightfall uses the same AI-native detection and risk framework across SaaS, endpoints, email, browsers, AI applications, and MCP workflows. This matters because modern data loss rarely stays inside one channel. A user or AI agent can access a file locally, move information into a browser, invoke an MCP tool, and send data into a SaaS or AI application as part of one workflow.

Instead of treating each stage as a separate security event, Nightfall is designed to preserve consistent policy and context across the movement. That unified model supports data detection and response as well as prevention.

AI-Native Detection Built for Precision

Pattern matching remains useful for deterministic data types, but it is not enough for every modern data classification problem. Nightfall combines ML detectors, LLM-powered file classifiers, contextual signals, and AI-based analysis to identify sensitive data and distinguish normal business activity from meaningful risk.

Nightfall reports 95% detection precision out of the box. The goal is to give SecOps teams higher-quality incidents that can be acted on rather than overwhelming analysts with low-value matches.

Purpose-Built MCP and AI Agent Security

AI agents can access and transform data without a human manually moving each file or message. That changes the DLP threat model. Local stdio MCP servers, remote MCP connections, IDE-embedded agents, copilots, AI assistants, and autonomous workflows all create additional paths for sensitive data movement.

Nightfall's MCP security platform is purpose-built for this environment. It supports discovery, risk scoring, tool classification, prompt injection detection, and inline controls for supported agentic workflows. This extends data security beyond remote gateway traffic to local agent activity and endpoint context.

Prevention First, Discovery Included

DSPM and discovery remain valuable, but runtime data movement does not pause while an organization builds a complete posture inventory. Nightfall starts with prevention across supported SaaS, endpoint, browser, email, and agentic channels while also providing data discovery.

This gives organizations a practical sequence: control risky movement now, improve visibility continuously, and use the resulting telemetry to strengthen policies and investigations over time.

Unified DLP, Insider Risk, and AI Governance

Nightfall consolidates DLP, insider risk, and AI governance into one AI data security platform. The same telemetry and policy model can support data protection, risky-user analysis, Shadow AI governance, and AI agent control.

For organizations addressing unauthorized AI use, Nightfall provides controls to protect data from Shadow AI while supporting approved adoption through secure AI usage.

Autonomous Investigation With Nyx

Nyx acts as an autonomous DLP analyst for incident investigation and response. It can help surface risky users, summarize incidents, identify patterns, correlate activity, and recommend response or policy changes.

That model shifts security operations from manually processing every alert toward higher-level oversight, investigation, and governance.

Real-Time Controls Beyond Visibility

Visibility is useful, but the operational value of DLP comes from controlling risky data movement. Nightfall supports surface-appropriate actions such as block, coach, redact, delete, revoke access, quarantine, encrypt, monitor, and notify.

This is why Nightfall is positioned as an AI data security platform rather than only an agentless SaaS scanner. API-based protection is one layer. The broader objective is to control sensitive data wherever humans or AI agents move it.

For organizations evaluating agentless and API-based DLP in 2026, Nightfall offers the strongest overall combination of AI-native detection, SaaS API coverage, endpoint and browser enforcement, AI application protection, MCP security, autonomous investigation, and real-time remediation.

Frequently Asked Questions

What Is Agentless DLP?

Agentless DLP uses APIs or native cloud integrations to inspect and govern data without installing endpoint software for those specific connections. It is commonly used for SaaS and cloud data discovery, classification, monitoring, and application-native remediation. Agentless does not mean every data flow can be controlled through APIs alone. Endpoint files, local applications, browser interactions, removable media, and local AI agent activity may require an endpoint, browser, gateway, or application-layer enforcement point.

What Is the Difference Between Agentless and Agent-Based DLP?

Agentless DLP connects directly to supported cloud services through APIs. Agent-based DLP runs software on endpoints to observe and control local data movement such as file operations, clipboard activity, uploads, downloads, printing, removable storage, or application interactions. Modern enterprise platforms can combine both models. Nightfall uses API-based SaaS integrations alongside lightweight endpoint and browser controls so organizations gain direct cloud visibility and local enforcement through one policy framework. For a broader architecture overview, see cloud, network, endpoint DLP.

How Does API-Based DLP Protect SaaS Data?

API-based DLP connects to supported SaaS applications using authorized integrations. It can inspect stored content, classify sensitive data, identify inappropriate sharing or exposure, and apply application-native remediation where supported. Because APIs operate according to each application's capabilities, API-based inspection is different from an inline proxy or endpoint control. Layered enterprise architectures can use each enforcement plane for the data flows it can observe and control most directly.

Can Agentless DLP Help With Insider Risk?

Yes. Agentless SaaS integrations can contribute valuable content, identity, sharing, and activity context for supported applications. That information can help identify unusual or risky data handling inside cloud services. A broader insider risk program benefits from endpoint and behavioral context as well. Nightfall combines SaaS signals with endpoint, browser, AI application, and agentic telemetry so investigations can follow sensitive data across more of the user's workflow.

Why Does AI-Native Detection Matter for DLP?

Sensitive data is not always recognizable through a fixed pattern. Source code, product plans, contracts, financial documents, credentials in context, and proprietary business information may require semantic or contextual classification. Nightfall uses AI-native detection to identify content and context across supported surfaces, helping security teams focus on meaningful risk rather than relying only on static pattern matches.

How Quickly Can Nightfall Be Deployed?

Nightfall's API-based SaaS integrations are designed to deploy in minutes, and endpoint coverage can be distributed through standard MDM tooling. Pre-trained detection models begin identifying sensitive data without requiring organizations to build a large regex library before receiving value. The result is a deployment model designed for early protection and continuous improvement rather than a long tuning phase before meaningful enforcement begins.

Is Nightfall Only an Agentless DLP Product?

No. Nightfall is an AI data security platform with an agentless API layer for supported SaaS applications and complementary enforcement across endpoints, browsers, email, AI applications, and MCP workflows. That hybrid design is central to Nightfall's value. API integrations provide direct SaaS visibility and remediation, while endpoint and agentic controls address local and runtime data movement that APIs alone do not cover.