Prompt Security Alternatives

Prompt Security established itself as a notable player in AI security before its acquisition by SentinelOne. SentinelOne announced its agreement to acquire Prompt Security on August 5, 2025, and completed the acquisition on September 5, 2025. While the platform offered AI-native security capabilities for embedded AI, copilots, and agents, Prompt Security is now positioned as part of SentinelOne's Singularity Platform, with SentinelOne's materials describing it as built into Singularity and managed through the same console. For security teams seeking standalone AI data security solutions, this shift creates an opportunity to evaluate purpose-built alternatives that govern data movement across humans and AI agents. Choosing the right AI data security platform can help organizations protect sensitive data flows through copilots, coding tools, email, endpoints, and SaaS applications without being locked into a broader endpoint security suite. This guide examines seven alternatives that serve different AI security and data protection needs in 2026, starting with Nightfall AI, which delivers real-time visibility and control over data movement by both humans and AI agents.

Key Takeaways

• AI-native detection outperforms legacy pattern matching: Nightfall reports up to 95% precision out of the box from a platform built from the ground up with machine learning, compared with the 5-25% precision it attributes to legacy DLP solutions that rely on keywords and regex patterns
• Deployment speed varies widely: API-native architectures like Nightfall AI can deploy SaaS and API integrations in minutes to hours, while traditional enterprise DLP solutions can involve a longer rollout
• Prompt Security is now part of SentinelOne: Following the 2025 acquisition (completed September 5, 2025), SentinelOne positions Prompt Security as integrated into its Singularity Platform rather than as the same standalone offering it was before
• GenAI coverage is a critical differentiator: Solutions with browser-level monitoring across ChatGPT, Copilot, Claude, and other AI applications address the fastest-growing data loss vector
• Unified platforms reduce tool sprawl: Single solutions covering SaaS, endpoints, email, browsers, and GenAI tools eliminate the operational overhead of managing multiple point solutions
• Data lineage tracking adds investigation context: Platforms that track data origin and movement across surfaces enable root cause analysis beyond simple detection and blocking

1. Nightfall AI

Nightfall AI delivers an AI-native data security platform that governs how data is accessed, moved, and exposed across human activity and AI agent workflows. The platform provides real-time visibility and control over sensitive data flowing through copilots, coding tools, email, endpoints, SaaS applications, and MCP servers. Backed by Bain Capital Ventures, Venrock, WestBridge Capital, Webb Investment Network, and Pear VC, along with cybersecurity leaders including Kevin Mandia, Freddy Kerrest, and Doug Merritt, Nightfall positions itself as the control platform for AI data.

How Does Nightfall AI Work?

Nightfall's platform uses AI-native detection powered by supervised fine-tuned models to secure data flows across every surface where sensitive information moves. Key Highlights:

• Detection Engine: ML detectors for PII, PHI, secrets, credentials, and financial data, plus LLM classifiers spanning 20+ categories, all customer-trainable and auto-retraining, delivering 95% precision out of the box versus a 5-25% legacy DLP baseline
• Coverage: 13 supported SaaS apps including Slack, Google Drive, Gmail, GitHub, Jira, Confluence, Salesforce, Microsoft Teams, OneDrive, SharePoint, Notion, Zendesk, and Microsoft Exchange Online, plus endpoints, browsers, AI apps, email, and APIs for securing additional SaaS apps, GenAI apps, or data pipelines
• Endpoint: A single agent covers human and AI/MCP traffic across 10+ vectors with ML and LLM detection, roughly 1% CPU and 50MB RAM, macOS and Windows parity, and deployment in about 30 minutes via MDM
• GenAI Security: Browser plugins and endpoint monitoring across ChatGPT, Copilot, Claude, Gemini, Perplexity, DeepSeek, and all major AI applications
• Real-Time Control: Block, coach, redact, delete, revoke, quarantine, encrypt, and automate remediation workflows
• AI Agent & MCP Security: Coverage for local stdio and remote HTTP MCP workflows, IDE hooks, risk scoring, tool classification by what each tool can do (read, read/write, destructive), and prompt injection detection on agent traffic
• AI-Native Investigation: A SecOps Copilot surfaces risky users, recommends policies, and analyzes incidents, with continuous telemetry and rich context from HRIS/IdP metadata, session replay, and endpoint lineage

Documented Results

Nightfall's enterprise deployments demonstrate consistent, quantifiable outcomes:

• Organizations achieve 95% detection precision out of the box versus the 5-25% Nightfall attributes to legacy DLP, cutting false positives by up to 95% and reducing noise with 90% fewer alerts than legacy DLP baselines
• SaaS and API integrations deploy in minutes to hours via API-native architecture versus weeks or months with traditional solutions, with data detection and response live across 13 SaaS apps in under an hour
• Nightfall reports proof points such as 95% precision, 10x lower total cost of ownership, and 80% self-resolution of alerts through its automated and end-user remediation workflows
• 100+ organizations, from startups to global enterprises, run on Nightfall, with customer examples including Gusto, DraftKings, Grafana Labs, Grab, Nubank, and Decagon

Privacy and Control Architecture

Nightfall's platform enables security teams to govern sensitive data movement while still enabling AI adoption and business productivity. The platform supports admin-driven, automated, or end-user driven workflows with manual or automated approval processes. Alerts and remediation can flow through Slack, Microsoft Teams, email, Jira, webhooks, APIs, the Nightfall console, and SIEM/SOAR workflows, with employee coaching and business-justification workflows where applicable.

What Makes Nightfall AI Unique

• AI-Native From Day One: Built from the ground up with machine learning, not retrofitted pattern matching, achieving 95% precision out of the box
• Unified Platform: One detection brain runs across SaaS data security, endpoint protection, email, browsers, and GenAI tools, consolidating DLP, insider risk, and AI governance into a single stack and eliminating tool sprawl
• MCP and Agent Coverage: MCP security covers local stdio and remote HTTP MCP workflows that legacy DLP and AI gateways cannot see
• Data Lineage: AI-driven lineage tracking across SaaS, endpoints, and unmanaged devices provides investigation context beyond simple detection

Best For: Organizations seeking a unified AI data security platform that deploys SaaS and API integrations in minutes to hours, achieves high detection precision with low false positives, and governs data movement across both human actors and AI agents in real time.

2. Lakera Guard

Lakera, now part of Check Point's AI Security portfolio following the September 2025 acquisition announcement, provides runtime LLM security through an API-first architecture designed for organizations building and deploying custom AI applications. Current Lakera documentation is branded as Check Point AI Security. The platform focuses on protecting LLM interactions from prompt injection attacks, jailbreaks, and system prompt extraction.

Core Capabilities

• API-level guardrails for custom LLM applications and AI agents
• Detection accuracy for prompt injection attacks
• Coverage across OpenAI, Anthropic, Google, AWS Bedrock, Azure, and self-hosted models
• Multilingual support
• Free tier available with enterprise pricing for production workloads

Technical Architecture

Lakera centralizes guardrail evaluation through an API endpoint, but each application, gateway, or supported platform surface still needs to route AI interactions through Lakera's controls. Check Point states that Lakera provides detection for prompt injection and related attacks, multilingual support, and a threat-intelligence base of adversarial patterns.

Deployment Considerations

The API-based integration model requires custom development work for each application surface. Organizations seeking browser-level coverage for employee AI usage would need to build custom extensions or use Lakera alongside broader DLP solutions.

Best For: Development teams building custom LLM applications that require specialized prompt injection defense and API-level guardrails.

3. Cyberhaven

Cyberhaven focuses on data lineage-centric enterprise DLP, using what the company calls Large Lineage Models to track data provenance across organizational surfaces. The platform emphasizes understanding where data originated and how it moves rather than relying solely on content classification.

Key Features

• Continuous lineage graph tracking data origin and transformation
• Coverage across SaaS applications, cloud storage, and endpoints
• Support for all file types including images, CAD files, source code, and proprietary formats
• Insider risk management capabilities built on provenance tracking
• AI-native detection combined with lineage context

Enterprise Focus

Cyberhaven positions itself for organizations requiring the most granular data provenance tracking, particularly for intellectual property protection and insider threat investigations. The platform provides investigation context that goes beyond content-based detection.

Deployment Considerations

Cyberhaven deployments are typically enterprise engagements with implementation support. The platform suits organizations with dedicated security teams and complex data governance requirements. Because its architecture centers on lineage in SaaS and endpoint environments, agentic workflows such as local stdio MCP, IDE-embedded agents, and AI assistants sit outside its native monitoring and enforcement, and its AI capabilities are offered as a separate SKU on top of the endpoint platform rather than included in every tier the way Nightfall packages them.

Best For: Large enterprises requiring the deepest data lineage implementation for provenance tracking, insider risk management, and protection of proprietary file formats.

4. Microsoft Purview DLP

Microsoft Purview DLP provides native data loss prevention for organizations standardized on Microsoft 365. Microsoft 365 E3 includes basic Purview DLP capabilities for Microsoft 365 workloads such as Exchange, SharePoint, and OneDrive, while more advanced Purview capabilities generally require E5, the Purview Suite, or add-on licensing. The platform offers centralized labeling and policy management across the Microsoft ecosystem.

Native M365 Capabilities

• Deep integration with SharePoint, OneDrive, Teams, and Exchange
• Basic DLP included in existing E3 licensing, with advanced capabilities in E5, the Purview Suite, or add-ons for Microsoft-centric organizations
• Centralized sensitivity labeling across Microsoft applications
• Compliance templates for GDPR, HIPAA, and other regulatory frameworks
• Copilot coverage for Microsoft's AI assistant

Coverage Boundaries

Purview's strengths center on Microsoft 365, but Microsoft has expanded DLP controls into some web, network, cloud-app, and GenAI use cases, including inline traffic to tools such as ChatGPT, Gemini, and DeepSeek, and cloud apps through the Defender for Cloud Apps catalog. Some of this coverage is preview-dependent and architecture-dependent, and coverage gaps can remain for non-Microsoft SaaS and unmanaged AI tools. Purview includes traditional pattern-based detection such as sensitive information types and regex, but it also supports machine-learning trainable classifiers, exact data match, document fingerprinting, and sensitivity-label-based controls.

Deployment Characteristics

Some Microsoft Purview policy and label changes can take time to propagate depending on the workload, client, and policy type, which may affect organizations needing real-time policy updates. Microsoft supports Endpoint DLP on macOS, though specific macOS controls, deployment paths, and feature coverage can differ from Windows.

Best For: Organizations running entirely on Microsoft 365 with no significant non-Microsoft SaaS applications, seeking basic DLP coverage included in existing licensing.

5. Forcepoint DLP

Forcepoint DLP delivers enterprise data loss prevention with behavioral analytics through its Risk-Adaptive Protection capabilities. The platform provides unified policy management across cloud and on-premises environments with extensive compliance templates.

Enterprise Capabilities

• Behavioral analytics providing contextual risk scoring
• Pre-built compliance templates for GDPR, HIPAA, and other frameworks
• Hybrid deployment options for SaaS and on-premises environments
• OCR and ML-enhanced detection capabilities
• Integration with existing security infrastructure

Operational Considerations

Forcepoint implementations are enterprise deployments that typically involve configuration, tuning, and implementation support, and Forcepoint uses customized enterprise pricing.

Compliance Focus

The platform's extensive compliance template library supports organizations in heavily regulated industries, with behavioral analytics adding context beyond content classification for risk-based enforcement.

Best For: Regulated enterprises with compliance requirements and the resources to manage complex DLP implementations, particularly those needing hybrid cloud and on-premises coverage.

6. Proofpoint DLP

Proofpoint DLP focuses on email and human-centric risk, with people-centric telemetry for understanding who poses the greatest data loss risk, and current Proofpoint DLP also provides integrated protection across endpoints, cloud, and email.

People-Centric Approach

• Deep email scanning and protection capabilities
• People-centric risk scoring based on user behavior patterns
• Integrated DLP coverage across endpoint, cloud, and email in current Proofpoint materials
• Endpoint DLP controls for a range of GenAI tools, including blocking uploads and redacting sensitive prompt inputs
• ML-based detection for sensitive content
• Compliance support for regulated industries

Platform Integration

Proofpoint is a natural fit for organizations already using its email security products, where DLP capabilities extend from existing deployments. The platform provides bundled value for email-heavy enterprises while extending controls across endpoint, cloud, and GenAI use cases.

Coverage Scope

Proofpoint continues to focus on email and human-centric risk, and its current DLP also covers endpoint, cloud, and GenAI use cases, including endpoint controls for a range of GenAI tools. The depth and architecture of that coverage can vary across surfaces.

Best For: Organizations already invested in Proofpoint's email security ecosystem seeking to extend data protection across email, endpoint, cloud, and GenAI workflows.

7. Symantec DLP (Broadcom)

Symantec DLP, now under Broadcom ownership following Broadcom's completion of its acquisition of Symantec's enterprise security business on November 4, 2019, is a mature enterprise DLP platform with extensive detection technologies and granular policy controls. The platform has been a long-standing option for complex intellectual property protection scenarios.

Enterprise Depth

• Extensive detection technologies, including data identifiers, keywords, regex, file attributes, fingerprinting, exact data match, indexed document matching, and machine learning, with extensive policy customization
• Mature enterprise workflows for large-scale deployments
• Modular licensing for specific capability requirements
• Extensive policy controls for complex IP protection
• Strong presence in Fortune 500 organizations

Implementation Requirements

Symantec DLP involves planning, tuning, and operational expertise, especially for detection methods such as exact data match. The platform often calls for dedicated DLP teams for ongoing operations.

Legacy Architecture

While powerful, Symantec DLP's architecture predates the cloud-native era, and its depth comes with implementation complexity relative to modern alternatives.

Best For: Large enterprises with dedicated DLP teams, complex intellectual property protection requirements, and the resources to manage long implementation cycles and ongoing maintenance.

Why Nightfall AI Stands Out for AI Data Security

AI-Native Architecture Built for Modern Data Movement

Nightfall's platform was built from the ground up with machine learning, not retrofitted with AI features added to legacy pattern-matching systems. The detection engine achieves 95% precision out of the box compared to the 5-25% baseline Nightfall attributes to traditional DLP solutions. This difference translates to dramatically fewer false positives, reducing the alert fatigue that plagues security teams using legacy tools.

Complete Coverage Across Every Data Surface

Where point solutions focus on specific vectors, Nightfall provides a unified control platform spanning SaaS applications, endpoints, email, browsers, and AI tools. The platform's 13 supported SaaS apps cover the collaboration and productivity tools where sensitive data actually moves, including Slack, Google Drive, Gmail, GitHub, Salesforce, Jira, Confluence, Microsoft Teams, OneDrive, SharePoint, Notion, Zendesk, and Microsoft Exchange Online, with endpoints, browsers, email, AI apps, and APIs extending coverage further. This unified approach eliminates the tool sprawl and integration complexity of managing separate solutions for each surface.

GenAI and Shadow AI Leadership

Nightfall addresses the fastest-growing data loss vector with comprehensive GenAI protection across ChatGPT, Copilot, Claude, Gemini, Perplexity, DeepSeek, and all major AI applications. Browser plugins and endpoint monitoring catch AI usage regardless of whether employees access these tools through corporate or personal accounts. Real-time prompt sanitization prevents sensitive data exposure before it reaches external AI systems.

MCP and AI Agent Security

Legacy DLP was built for human-driven data movement, but AI agents now move data autonomously at machine speed. Nightfall's MCP security capabilities cover local stdio and remote HTTP MCP workflows, IDE hooks, and agent chains that other solutions cannot see. Risk scoring and tool classification by what each tool can actually do, whether read, read/write, or destructive, distinguish legitimate business activity from dangerous exfiltration across agentic workflows.

Deployment in Hours, Not Months

Nightfall's API-native architecture enables SaaS and API integrations to deploy in minutes to hours rather than the weeks or months required by traditional enterprise DLP, with data detection and response live across 13 SaaS apps in under an hour. AI agent and MCP security is positioned as deployable in days, with production in two weeks according to Nightfall's MCP comparison. Organizations gain protection quickly without lengthy professional services engagements or complex infrastructure changes.

Proven Customer Success

Nightfall reports proof points such as 95% precision, 10x lower total cost of ownership, and 80% self-resolution of alerts. 100+ organizations, from startups to global enterprises, run on Nightfall, with customer examples including Gusto, DraftKings, Grafana Labs, Grab, Nubank, and Decagon, demonstrating enterprise scalability across industries.

Real-Time Control, Not Just Visibility

Visibility without control is just a dashboard. Nightfall enables security teams to block, coach, redact, delete, revoke, quarantine, encrypt, and automate remediation in real time. The platform supports block, coach, and override workflows with manual or automated approval, delivering the control necessary to stop data loss before it happens. Where point tools and gateways watch, Nightfall enforces: it is a platform, not a feature.

For security teams seeking alternatives now that Prompt Security is integrated into SentinelOne's Singularity Platform, Nightfall AI provides the comprehensive AI data security platform needed to govern sensitive data movement across both humans and AI agents. Where prompt-time controls focus on the AI interaction itself, Nightfall governs the data as it moves across every surface, from SaaS and endpoints to email, browsers, and MCP and agent workflows, in real time. Schedule a demo to see how Nightfall can protect your organization's data in the AI era.

Frequently Asked Questions

Why are organizations seeking Prompt Security alternatives in 2026?

SentinelOne announced its agreement to acquire Prompt Security on August 5, 2025, and completed the acquisition on September 5, 2025. SentinelOne now positions Prompt Security as integrated into its Singularity Platform and managed through the same console. This shift creates an opportunity for security teams to evaluate purpose-built AI data security solutions that can be deployed independently and provide broader coverage across SaaS applications, endpoints, and AI workflows.

What is the difference between AI-native detection and legacy DLP pattern matching?

AI-native detection uses machine learning models trained on real-world data to classify sensitive content, achieving detection precision around 95% out of the box for platforms like Nightfall AI. Legacy DLP relies on keywords, regular expressions, and predefined patterns, which Nightfall associates with a 5-25% precision range and high false positive rates. This difference dramatically impacts security team productivity and the effectiveness of data protection programs.

How do prompt security alternatives address GenAI and Shadow AI risks?

Solutions like Nightfall AI provide browser-level monitoring and endpoint protection across all major AI applications including ChatGPT, Copilot, Claude, Gemini, and Perplexity. This coverage catches sensitive data exposure regardless of whether employees use corporate or personal accounts. Real-time prompt sanitization prevents data from reaching external AI systems, while data lineage tracking helps organizations understand AI usage patterns across the enterprise.

Can a single platform replace multiple point solutions for AI data security?

Yes. Unified platforms like Nightfall AI consolidate AI-native DLP, insider risk management, GenAI protection, endpoint and browser controls, SaaS data security, and MCP/AI-agent security into a single solution covering SaaS applications, endpoints, email, browsers, and AI tools. This approach eliminates tool sprawl, reduces vendor management overhead, and provides unified visibility and policy management. The platform's 13 supported SaaS apps and comprehensive GenAI coverage mean organizations can simplify their security stack while expanding protection.

What should organizations prioritize when evaluating prompt security alternatives?

Key evaluation criteria include detection accuracy (AI-native vs. pattern-based), deployment speed (hours vs. months), coverage breadth (SaaS, endpoints, email, GenAI), real-time control capabilities (block, redact, quarantine), and proven customer success metrics. Organizations should also consider whether solutions can govern AI agent and MCP workflows, which represent a growing blind spot for legacy security tools that were designed only for human-driven data movement.