Zenity Alternatives

Zenity has positioned itself as a specialized platform for AI agent governance and security posture management. Zenity announced that Gartner published an April 2026 report titled AI Vendor Race: Zenity Is the Company to Beat in AI Agent Governance (dated April 17, 2026); Gartner's standard disclaimer states that its publications are not endorsements of any vendor, product, or service. The company has raised $55M+ in funding, including a $38M Series B. However, organizations evaluating AI data security solutions often require capabilities that extend beyond agent-specific governance to address the full spectrum of data movement risks. A comprehensive AI data security platform must govern how data is accessed, moved, and exposed across both human activity and AI agent workflows. This guide examines seven alternatives to Zenity for 2026, starting with Nightfall AI, a unified platform combining enterprise DLP, Shadow AI protection, AI agent security, and data lineage tracking in one solution.

Key Takeaways

• Comprehensive data protection requires unified platforms: Solutions that cover SaaS, endpoints, email, browsers, and AI applications in one platform eliminate security gaps that emerge when using multiple point solutions
• AI-native detection dramatically improves accuracy: Platforms using machine learning and LLM-based detection can achieve 95% precision out of the box, while Nightfall reports that legacy DLP tools remain stuck at 5-25% accuracy
• Data lineage tracking catches sophisticated exfiltration: AI-powered lineage that traces data from source to destination across transformations identifies multi-step exfiltration attempts that appear benign in isolation
• Shadow AI security requires endpoint and browser coverage: Blocking sensitive data leakage to ChatGPT, Copilot, Claude, and other AI tools demands protection across endpoint and browser surfaces, including typing, clipboard paste, and file uploads
• Deployment speed affects time to value: API-based platforms that deploy in under one hour deliver faster protection compared to legacy solutions requiring weeks or months of configuration
• Real-time control separates leaders from laggards: Visibility without the ability to block, coach, redact, or remediate in real time is just a dashboard that documents failures after they occur

1. Nightfall AI

Nightfall AI delivers an AI data security platform that provides enterprises real-time visibility and control over data movement by humans and AI agents across SaaS, endpoints, email, browsers, and AI applications. Co-founded by Rohan Sathe (a founding engineer at Uber Eats) and launched in 2019 (emerging from stealth in November 2019), Nightfall is backed by Bain Capital Ventures, Venrock, WestBridge Capital, Webb Investment Network, and Pear VC, along with cybersecurity leaders Kevin Mandia, Freddy Kerrest, and Doug Merritt. The platform offers a unified AI-native approach that combines comprehensive DLP with AI agent security and data lineage tracking. More than 100 organizations run on Nightfall, including Gusto, DraftKings, Grafana Labs, Grab, Nubank, and Decagon.

How Does Nightfall AI Work?

Nightfall's platform uses a unified AI-native detection approach with 100+ AI-based models, LLM file classifiers, and computer-vision models, applying one policy across every surface where sensitive data moves. Key capabilities include:

• AI-Native Detection: ML detectors for PII, PHI, secrets, credentials, and financial data, plus 20+ AI file classifiers for sensitive document types with 95% precision out of the box
• Data Lineage Tracking: AI-powered tracing from source to destination that catches transformed content exfiltration
• Real-Time Control: Block, coach, redact, delete, revoke, quarantine, encrypt, and automate remediation actions
• Shadow AI Security: Protection across ChatGPT, Copilot, Claude, Gemini, Perplexity, and DeepSeek via typing, clipboard paste, and file upload

Deployment and Results

Nightfall emphasizes rapid deployment and measurable outcomes:

• SaaS integrations deploy in under one hour via API-based OAuth integration with no network architecture changes
• Endpoint agents and browser plugins are lightweight and deployable in minutes via MDM, with under 1% CPU and memory usage
• Customers report 6x average ROI with 80% automated remediation or self-resolution
• Four in five incidents resolved through automation or employee self-remediation

Customer Evidence

Nightfall's enterprise deployments demonstrate consistent results:

• Snyk: "Nightfall is reliable. When it says there's a detection, we trust that detection. You don't want to waste time chasing ghosts."
• Unit21: "We want to allow our folks to use the power of generative AI but in a safe and approved way. Nightfall gently redirects our people to the safe gen AI sites."

What Makes Nightfall Unique

• Comprehensive Coverage: Unified platform covering SaaS, endpoints, email, browsers, and AI apps in one solution
• Data Movement Focus: Governs runtime data movement, not just static data classification
• MCP Security: Covers local stdio and remote HTTP/SSE MCP workflows, IDE hooks, per-server risk scoring, and tool-call monitoring
• Agent Traffic Protection: Nightfall MCP Security monitors AI-agent prompts, MCP tool calls, API calls, and responses for sensitive data and risk signals
• LLM-Powered File Classification: Identifies sensitive document types by structure and meaning, not just keywords

Best For: Enterprises seeking a unified control platform that consolidates DLP, insider risk, and AI governance into one stack while delivering industry-leading detection accuracy and rapid deployment.

2. Lakera AI

Lakera AI focuses specifically on protecting LLM applications from prompt injection, jailbreaks, and other LLM-specific threats. Acquired by Check Point in Q4 2025 for approximately $190 million net cash consideration, Lakera originated in Zurich, Switzerland and now operates as part of Check Point's AI security strategy. The company built its threat intelligence from the Gandalf platform, which more than one million users have played to learn AI security concepts.

Core Capabilities

• LLM application protection with Lakera Guard API
• Prompt injection and jailbreak defense
• Support for production deployments
• Coverage for high-volume workloads
• Lakera Red for automated adversarial testing
• Support for 100+ languages

Focus Area

Lakera AI specializes in runtime protection for customer-facing LLM applications. The company publicly states that it is cited in the OWASP LLM and GenAI Security Landscape Guide 2025 and references Gartner GenAI TRiSM-related recognition.

Customer Feedback

Enterprise customers have shared positive experiences:

• Dropbox: "The Lakera team has accelerated our GenAI journey."
• Nu (banking): "We've chosen Lakera to secure our enterprise GenAI deployment across our regulated banking environment."

Limitations to Consider

• Focus is narrow to LLM application protection only
• Does not provide comprehensive data loss prevention
• No endpoint or browser agents for Shadow AI monitoring
• No data lineage tracking capabilities

Best For: Developers building customer-facing LLM applications who need runtime protection and adversarial testing capabilities.

3. Prompt Security

Prompt Security, acquired by SentinelOne in September 2025, is now positioned as part of SentinelOne's Singularity Platform and targets employee GenAI governance with capabilities spanning developers, employees, and AI applications. Its founding executives have OWASP LLM security involvement: CEO Itamar Golan was on the core team of the OWASP Top 10 for LLM Applications project, and CTO and co-founder Lior Drihem contributed to it.

Platform Capabilities

• Employee GenAI usage control across the organization
• MCP Gateway for agentic AI security
• AI red teaming capabilities
• Holistic coverage across employees, developers, and applications
• Policy-based controls for GenAI tool usage

Technical Approach

Prompt Security emphasizes governance at the organizational level, enabling security teams to establish policies that control how employees interact with AI tools while maintaining productivity.

OWASP Leadership

The company's founders have OWASP LLM security involvement: its CEO was on the OWASP Top 10 for LLM Applications core team and its CTO contributed to the project, providing credibility in the enterprise security community.

Limitations to Consider

• Does not function as a comprehensive DLP platform
• Historically focused on AI usage governance rather than endpoint-native DLP; it now operates as part of SentinelOne's broader Singularity platform
• Limited SaaS application coverage compared to dedicated DLP solutions
• Newer platform with less standalone enterprise track record

Best For: Organizations prioritizing policy-based employee GenAI governance where controlling sanctioned and unsanctioned AI tool usage is the primary concern.

4. HiddenLayer

HiddenLayer specializes in ML model security, protecting machine learning models from adversarial attacks, model theft, and tampering. The company holds public federal procurement credentials, including Missile Defense Agency SHIELD IDIQ awardee status, Tradewinds Solutions Marketplace "Awardable" status, and an AWS Intelligence Community Marketplace listing.

Security Focus

• ML model protection from adversarial attacks
• Model theft prevention
• Tampering detection and defense
• AI Attack Simulation and automated red teaming (model-agnostic)
• No training data required for red teaming capabilities

Federal Procurement Credentials

HiddenLayer's federal procurement credentials, including MDA SHIELD IDIQ awardee status, Tradewinds Awardable status, and AWS Intelligence Community Marketplace listing, reflect access to demanding compliance environments.

Technical Approach

The platform focuses specifically on protecting the models themselves rather than the data flowing through applications, addressing a different layer of the AI security stack.

Limitations to Consider

• Model-centric focus does not cover data loss prevention
• Not designed for employee AI usage monitoring
• Provides AI asset discovery and shadow-AI inventory, but is not primarily positioned as an employee browser or endpoint Shadow AI DLP control platform
• Different problem space than comprehensive data security

Best For: Organizations with custom ML models requiring protection from adversarial attacks and model theft, particularly in regulated federal environments.

5. ProtectAI

ProtectAI focuses on AI/ML supply chain security, providing tools for scanning models and securing MLOps pipelines. Palo Alto Networks completed its acquisition of ProtectAI in July 2025, providing enterprise backing and integration into Prisma AIRS.

Product Offerings

• ModelScan for model vulnerability scanning
• NB Defense for notebook security
• LLM Guard and other open-source tools (the earlier Rebuff prompt-injection project is now archived)
• Open-source tools available to developers
• MLOps pipeline security

Acquisition Context

ProtectAI's integration into Palo Alto Networks, completed in July 2025, provides access to enterprise sales channels and integration with the broader Prisma AIRS security platform.

Technical Focus

ProtectAI originated in AI/ML supply-chain and development lifecycle security. Following its July 2025 acquisition by Palo Alto Networks, its technology is positioned inside Prisma AIRS, which Palo Alto markets as covering development-to-runtime AI security.

Limitations to Consider

• Heritage is in AI/ML supply-chain and pipeline security; runtime and agentic coverage now comes via Palo Alto's Prisma AIRS rather than as standalone ProtectAI capabilities
• Development-lifecycle security emphasis in its original product line
• Standalone tooling was not designed for data exfiltration prevention
• Different security layer than comprehensive data security platforms

Best For: Organizations building custom AI models who need supply chain security and model scanning during the development lifecycle.

6. Microsoft Security

Microsoft offers AI security capabilities across its security portfolio, including Microsoft Defender, Microsoft Purview, and Security Copilot. These tools integrate natively with the Microsoft 365 ecosystem.

Product Components

• Microsoft Defender for cloud and endpoint protection
• Microsoft Purview for data governance and compliance
• Security Copilot for AI-assisted security operations
• Native integration with Microsoft 365, Azure, and Copilot

Ecosystem Integration

Organizations heavily invested in Microsoft infrastructure benefit from native integration between security tools and productivity applications.

Enterprise Scale

As a public company with extensive enterprise presence, Microsoft provides stability and long-term product roadmaps for security investments.

Limitations to Consider

• In Nightfall's comparison, Microsoft Purview shows 25% lower precision and 50% lower recall than Nightfall, and Purview deployments often require significant false-positive tuning
• Coverage for non-Microsoft AI applications is delivered across Microsoft's own products and channels (Microsoft Learn)
• Complex licensing and configuration requirements
• Not purpose-built for modern AI data security challenges

Best For: Organizations with deep Microsoft ecosystem investments seeking native integration, though supplementary solutions may be needed for comprehensive AI data security.

7. Palo Alto Networks

Palo Alto Networks provides AI security through Prisma AIRS and its broader security platform, leveraging its position as a leading enterprise security vendor.

AI Security Capabilities

• Prisma AIRS for AI application security, introduced in April 2025, expanded with Prisma AIRS 2.0 in October 2025, and announced as Prisma AIRS 3.0 for agentic AI security in March 2026 (Palo Alto Networks)
• Integration with broader Palo Alto security portfolio
• Enterprise-scale deployment capabilities
• Unified platform approach across security domains

Enterprise Presence

As a public company and established security leader, Palo Alto Networks offers extensive enterprise sales and support infrastructure.

Platform Integration

Organizations using Palo Alto for network security, cloud security, or endpoint protection benefit from integrated dashboards and policy management.

Limitations to Consider

• AI security capabilities are recent additions, with Prisma AIRS introduced in April 2025 and expanded through 2025 and 2026
• May require multiple products for comprehensive coverage
• Coverage for AI data movement control across endpoints, browsers, SaaS, and AI apps may vary
• Complex deployment for organizations not already using Palo Alto infrastructure

Best For: Organizations with existing Palo Alto Networks infrastructure seeking to add AI security capabilities within their current vendor relationship.

Why Nightfall AI Stands Out for AI Data Security

A Unified Platform Combining DLP, AI Security, and Data Lineage

Nightfall AI addresses the full spectrum of modern data security challenges in a single unified platform. While many competitors focus narrowly on one layer of the problem, whether AI agent governance without data-level enforcement, LLM application protection, or model security, Nightfall delivers coverage across every surface where sensitive data moves. This unified approach helps eliminate the security gaps that emerge when organizations deploy multiple point solutions.

Industry-Leading Detection Accuracy

The gap between legacy and AI-native detection is substantial. Nightfall achieves 95% precision out of the box using ML detectors and LLM classifiers, while Nightfall reports that legacy DLP tools remain at just 5-25% accuracy. This difference translates directly to security team productivity. High accuracy means teams can trust detections rather than spending time investigating false positives.

Real-Time Control, Not Just Visibility

Nightfall's core message is that visibility without control is just a dashboard. The platform provides real-time remediation capabilities including blocking, coaching, redaction, deletion, revocation, quarantine, and encryption. Security teams can govern sensitive data movement while still enabling AI adoption and business productivity.

Shadow AI Protection Across All Interaction Methods

Employee use of AI tools represents one of the fastest-growing data exfiltration vectors. Nightfall blocks sensitive data leakage to ChatGPT, Copilot, Claude, Gemini, Perplexity, and DeepSeek across all interaction methods: typing, clipboard paste, and file upload. This protection extends through browser extensions and endpoint agents, covering the complete path data takes to AI applications.

AI-Powered Data Lineage

Sophisticated exfiltration often involves multiple steps that appear benign in isolation. Data copied to a personal drive, renamed, synced to another service, and then uploaded to an AI tool evades traditional rules-based detection. Nightfall's AI-powered lineage tracking traces data from source to destination across transformations, identifying exfiltration attempts that legacy tools miss entirely.

MCP Security for Agentic Workflows

As AI agents gain autonomous capabilities through Model Context Protocol (MCP), new security requirements emerge. Nightfall covers local stdio and remote HTTP/SSE MCP workflows, providing Shadow MCP detection, per-server risk scoring, IDE hooks, and monitoring of prompts, MCP tool calls, responses, and shell commands. Many legacy DLP and gateway tools cannot see or govern local agentic workflows at all.

Rapid Deployment and Proven ROI

Time to value matters for security investments. Nightfall's SaaS integrations deploy in under one hour via API-based OAuth, and endpoint agents and browser plugins deploy in minutes via MDM. Customers report 6x average ROI. These results come from 80% automated remediation or self-resolution that reduces security team workload while improving protection.

Consolidating Security Tool Sprawl

Organizations typically deploy three to five separate tools for DLP, insider risk, CASB, and AI governance. Nightfall consolidates these capabilities into one unified platform operating across every surface. This consolidation reduces vendor management overhead, eliminates integration complexity, and provides unified visibility across all data movement.

For security teams evaluating alternatives to specialized AI agent platforms, Nightfall AI delivers the comprehensive coverage, accuracy, and real-time control required to govern sensitive data in the AI era. Legacy DLP was built for human-driven data movement. AI agents now move data autonomously at machine speed. Nightfall was built for both.

Frequently Asked Questions

What is the difference between AI agent governance and AI data security?

AI agent governance focuses specifically on discovering, inventorying, and controlling AI agents like Microsoft Copilot Studio or Salesforce Agentforce. AI data security is a broader category that governs how all sensitive data is accessed, moved, and exposed across both human activity and AI agent workflows. Organizations often need both capabilities, with AI data security platforms like Nightfall providing comprehensive coverage that includes agent-related risks alongside SaaS, endpoint, email, and browser protection.

Why do legacy DLP tools struggle with AI-era data risks?

Legacy DLP was architected for human-driven data movement using pattern-matching and static rules. Nightfall reports that these tools typically achieve only 5-25% detection accuracy and cannot see data flowing through AI applications, browser-based AI interactions, or autonomous agent workflows. AI-native platforms use machine learning and LLM-based detection to achieve 95% precision while covering modern data movement paths that legacy tools miss entirely.

How quickly can organizations deploy AI data security platforms?

Deployment timelines vary significantly across vendors. Nightfall AI deploys SaaS integrations in under one hour via API-based OAuth connections with no network architecture changes. Endpoint agents and browser plugins deploy in minutes via MDM. In contrast, legacy DLP implementations often require weeks or months of configuration and tuning before delivering value.

What should security teams prioritize when evaluating Zenity alternatives?

The primary considerations include coverage breadth across data movement surfaces, detection accuracy that reduces false positive burden, real-time control capabilities beyond passive monitoring, deployment speed affecting time to value, and proven enterprise results with documented ROI. Organizations should also evaluate whether solutions address both human and AI agent data movement risks in a unified platform.

Can organizations use multiple AI security tools together?

Some organizations deploy complementary tools addressing different security layers. For example, using Nightfall AI for comprehensive data protection while adding specialized model security tools for custom ML deployments. However, tool sprawl creates integration complexity and potential gaps. Unified platforms that address multiple use cases typically deliver better coverage with lower operational overhead than assembling point solutions.