Zenity Reviews 2026

Key Takeaways

• Gartner named Zenity the "Company to Beat" in AI Agent Governance in a report dated April 17, 2026. Gartner states that it does not endorse vendors and does not advise selecting vendors based on such designations, so the recognition reads as analyst opinion rather than procurement guidance
• Coverage extends beyond a Microsoft, Salesforce, and ServiceNow story to Microsoft Foundry, AWS Bedrock, Google Vertex AI, ChatGPT Enterprise, Claude Enterprise, cloud and homegrown agents, coding and personal agents, endpoints, and MCP. LangChain, CrewAI, AutoGen, and Databricks are not publicly enumerated
• Runtime enforcement extends beyond the Microsoft, Salesforce, and ServiceNow ecosystems, with general availability of agent runtime security for Microsoft Foundry on March 17, 2026, blocking for custom-built agents through AIDR, and an MCP gateway for coding and personal agents. Enforcement depth varies by platform
• The headline enterprise metrics are vendor-published and anonymized, covering 90% of vulnerabilities remediated within four months at a Fortune 20 technology company, 95% of high-risk violations automatically remediated at a Fortune 200 consulting firm, and 80% risk reduction across a Fortune 50 financial services tenant of more than 150,000 resources
• Zenity is not a purely reactive platform, documenting validation of which agent attack paths are exploitable and sandbox execution of risky skills, files, and code, with public materials centering on exposure validation and sandboxing
• The buying decision is scope rather than capability gaps, since Zenity secures agentic activity across SaaS, cloud, endpoint, and MCP environments, and its public materials position the platform around agent governance rather than a general-purpose program for human-initiated email, browser, endpoint, and SaaS data movement. That is where Nightfall's coverage across supported surfaces applies: one AI data security platform running a single detection brain across humans and AI agents, with full inline blocking on agentic surfaces

The AI agent security landscape has matured rapidly since 2024. Organizations deploying Microsoft Copilot, Salesforce Agentforce, and custom AI agents now face legitimate questions about how to govern autonomous systems that access sensitive data. AI has not just changed how data moves; it has changed who moves it. Data now flows through copilots, agents, and MCP servers at machine speed, with no human in the loop. Zenity has positioned itself at the center of this conversation, and the decision to adopt any AI agent security platform requires understanding what each solution actually covers, where scope boundaries exist, and how specialized governance tools compare to unified data security approaches.

This review examines Zenity's capabilities, market positioning, published customer outcomes, and scope boundaries based on documented product features as of July 2026. The goal is not to dismiss Zenity's strengths but to provide security leaders with accurate information for vendor evaluation in a category where marketing claims often outpace technical reality.

Zenity: A Player in the AI Data Security Space

Zenity launched in 2021 as a security and governance platform for low-code and no-code development. It later expanded into enterprise copilots and AI agent security, with its own timeline documenting support for securing enterprise AI copilots beginning in November 2023.

In October 2024, Zenity raised a $38 million Series B. The company has publicly disclosed a $5 million seed round, a $16.5 million Series A, and the $38 million Series B, alongside a strategic investment from M12 whose size was not disclosed. Zenity was founded in Tel Aviv and maintains substantial operations there alongside a growing US presence, with recent corporate announcements datelined New York. The company focuses on what it calls AI Security Posture Management (AISPM) and AI Detection and Response (AIDR).

Current named product capabilities include:

• AI Observability for continuous agent inventory and discovery across SaaS platforms, cloud environments, and endpoints, including visibility into the data agents access
• AI Security Posture Management for pre-deployment posture assessment covering agent configurations, permissions, integrations, and tool access
• AI Exposure Management for correlating identity, data, and behavior into validated, exploitable attack paths
• AI Detection and Response for runtime detection, blocking, and automated response with intent-aware analysis
• Boundaries, Agentic Identity, and MCP Security as additional named capabilities within the platform

Zenity also describes Surface, Enforce, and Protect as architectural layers. Response functionality is documented within AISPM and AIDR playbooks and remediation rather than as a standalone product module.

The platform targets a specific problem: enterprises are deploying agents faster than they can inventory them. Some large organizations report agent and application estates numbering in the thousands. Zenity cites an anonymous Fortune 50 pharmaceutical customer with 2,000 instances of agents and applications shared across the organization. Zenity addresses this by documenting full-lifecycle coverage from agent discovery through posture and exposure management to detection and response. Discovery is a real starting point, and it is also why Nightfall treats inventory as a byproduct of prevention rather than a prerequisite for it, so protection begins on day one instead of after a cataloging cycle. For background on the discovery problem itself, see Nightfall on finding AI agents first.

How Zenity Compares to Other AI Security Startups

For evaluation purposes, AI agent security offerings can be grouped into four overlapping approaches: lifecycle governance and observability platforms, runtime guardrails providers such as Lakera, broad enterprise security suites from vendors like Palo Alto Networks, and data security platforms that address both human and AI agent workflows. This is an analytical framework for buyers rather than a taxonomy attributable to a named analyst report. Gartner's own public terminology includes categories such as AI Agent Governance, Agentic AI Security, Guardian Agents, AI TRiSM for Agents, and runtime inspection and enforcement.

Zenity and Microsoft Agent 365 overlap in agent discovery, governance, security posture, and runtime protection, although Agent 365 functions as a broader Microsoft control plane spanning inventory, governance, identity, threat protection, data protection, compliance, and administration through Microsoft 365, Entra, Defender, and Purview. Microsoft also states that Agent 365 can support agents created through Microsoft platforms, open-source frameworks, and third-party platforms.

Zenity's analyst recognition stands out. In a report dated April 17, 2026, Gartner named Zenity the "Company to Beat" in AI Agent Governance, using criteria that include technical capabilities, customer implementations, potential customer base, business model, partnerships, and ecosystem. Gartner explicitly states that it does not endorse vendors and does not advise users to select vendors based on ratings or designations, so the designation should be read as analyst opinion rather than procurement guidance. Zenity also holds September 2025 Gartner Cool Vendor recognition in Agentic AI TRiSM.

A structural point matters more than any single recognition. Agent governance, prompt-time inspection, and posture cataloging each cover one slice of the problem, while the actual data movement crosses surfaces: the same employee runs a local stdio MCP server in Cursor, sends prompts to a remote model, and pulls a file off the endpoint. Nightfall runs one detection brain across all of it, which is the difference between governing a category of tools and governing the data itself. Nightfall's overview of AI agent security explains how agents, MCP, and the AI harness fit together.

Understanding AI Governance and Why it Matters in 2026

AI governance has moved from abstract concern to operational necessity. The challenge is straightforward: AI agents make autonomous decisions about data access, tool usage, and action execution. Without governance frameworks, organizations cannot answer basic questions about what their AI systems can do, what data they can access, or whether their configurations align with security policies.

The governance challenge breaks down into several dimensions:

• Discovery involves knowing which AI agents exist across your environment, including shadow AI deployments
• Posture management requires understanding agent configurations, permissions, and potential vulnerabilities before deployment
• Runtime monitoring means detecting anomalous behavior, policy violations, and potential threats during agent execution
• Response covers both automated remediation and human-in-the-loop workflows for high-risk incidents

Security and risk-management resources increasingly address AI-specific risks. These include OWASP's LLM and generative AI risk lists, MITRE ATLAS's knowledge base of adversary tactics and techniques against AI systems, and NIST's voluntary AI Risk Management Framework. These are materially different instruments: risk guidance, an adversary-technique knowledge base, and a voluntary risk-management framework respectively. None of them automatically imposes legal compliance obligations, and NIST expressly describes the AI RMF as voluntary. Separately, organizations in regulated industries do face increasing pressure to demonstrate governance over AI systems that handle customer data, financial information, or protected health information. Nightfall's primer on model governance covers how these frameworks map to operational controls.

The Evolving Landscape of AI Governance Standards

Zenity positions its platform around alignment with these emerging resources. The company publicly documents that AIDR findings are mapped to OWASP LLM and MITRE ATLAS, and that Copilot Studio guardrails align with those two resources. Formal NIST AI RMF alignment is a distinct claim from mapping, since mapping, alignment, certification, and compliance are not equivalent terms.

However, standards alignment and operational security remain distinct challenges. Documented mappings differ from actually preventing data exfiltration or detecting prompt injection attacks in real time. Governance platforms deliver security value in proportion to the enforcement they can apply at runtime rather than the reporting they can produce afterward.

How Zenity and Others Approach AI Governance Software

AI governance software varies significantly in depth and approach. Some platforms focus on inventory and visibility, others on policy enforcement, and still others on runtime protection. Understanding these distinctions matters for vendor selection.

Key features differentiate governance platforms:

• Agent discovery mechanisms range from passive monitoring to active scanning across cloud environments, SaaS platforms, and endpoints
• Posture assessment depth varies from basic configuration checks to comprehensive analysis of permissions, integrations, MCP server connections, and tool access patterns
• Runtime detection capabilities differ between signature-based approaches, behavioral analysis, and intent-aware systems that understand context
• Remediation options span from alerting only to automated blocking and policy enforcement

Zenity's posture management receives recognition for depth in pre-deployment configuration review. Before deployment, Zenity AISPM evaluates agent configurations, permissions, integrations, and tool access. Zenity's observability and AIDR capabilities separately inspect memory activity, RAG retrievals, tool invocations, execution paths, and data use during operation. Distinguishing these two layers matters, because build-time review and runtime inspection solve different problems.

On coverage, Zenity documents named integrations and use cases across Microsoft, Salesforce, ServiceNow, AWS, Google Cloud, ChatGPT Enterprise, Claude Enterprise, custom and homegrown frameworks, and coding or personal agents. Its public pages do not enumerate LangChain, CrewAI, AutoGen, or Databricks by name, and the absence of a named product page does not establish lack of support. Note also that Databricks is a commercial data and AI platform, not an open-source agent framework comparable to LangChain or CrewAI. Nightfall extends protection to homegrown and third-party AI applications through its developer APIs, so custom frameworks are covered by the same detection engine that runs on every other surface.

Integrating AI Governance into Existing Workflows

Enterprise security programs rarely operate in isolation. Governance platforms must integrate with existing SIEM, SOAR, and incident management systems to deliver practical value. Zenity has invested in this area, having been selected for AWS Security Hub Extended and transmitting agent security findings using OCSF format. A March 2026 partnership with ServiceNow makes Zenity agent risk context and controls available in ServiceNow SecOps.

These integrations allow security teams to correlate AI agent threats with findings from other security tools. A credential exposure in an endpoint security system combined with unusual agent behavior in Zenity creates context that neither system provides alone. Nightfall approaches the same requirement from the data side, delivering incidents through Slack, Teams, email, Jira, and on-device notifications, with an API and MCP server for SOAR and ITSM workflows.

Beyond Visibility: The Need for Control in AI Data Security

Visibility without control is just a dashboard. This principle applies directly to AI agent security, where knowing that an agent accessed sensitive data matters far less than preventing unauthorized data movement in the first place. Seeing the leak is not the win. Stopping it is.

The visibility versus control distinction shapes vendor evaluation:

• Visibility-first platforms show you what happened after the fact, useful for compliance reporting and forensics but limited for prevention
• Control-first platforms enforce policies in real time, blocking unauthorized actions before data leaves your environment
• Hybrid approaches combine monitoring with selective enforcement based on risk scoring and policy configuration

Zenity has moved substantially toward inline prevention. The company described inline prevention capabilities for Azure AI Foundry, now Microsoft Foundry, in November 2025, then announced general availability of agent runtime security for agents built on Microsoft Foundry on March 17, 2026, building on the November capabilities. Zenity separately documents native prevention and automated response for Copilot Studio.

Enforcement is also documented beyond the Microsoft, Salesforce, and ServiceNow ecosystems. Zenity describes blocking for custom-built agents through AIDR, runtime boundaries across custom, SaaS, and endpoint deployments, and native hooks plus an MCP gateway for coding and personal agents, including detection and prevention of secret exfiltration and unsafe tool chaining on developer devices. Enforcement depth varies across supported platforms. Organizations weighing that footprint alongside comprehensive data exfiltration prevention for non-agent surfaces will find that Nightfall applies block, coach, and override actions consistently across SaaS, endpoint, browser, email, and agentic workflows from a single policy engine.

Implementing Real-time Controls for AI Data Movement

Real-time control requires understanding both the content being accessed and the context of that access. An AI agent retrieving customer records for a legitimate support case differs fundamentally from an agent exfiltrating the same data to an unauthorized external service.

Content inspection at runtime demands high-accuracy detection that minimizes false positives. Nightfall's detection engine delivers 95% precision out of the box, against a 5 to 25% baseline typical of legacy pattern-matching DLP, using ML detectors for PII, PHI, secrets, credentials, and financial data plus LLM classifiers across more than 20 categories. Detectors are customer-trainable and auto-retraining, and teams can build custom detectors without regex for context-aware classification. The practical result is signal instead of noise: false positives drop by 95%, and SecOps shifts from triage to oversight.

Context awareness requires understanding user identity, data sensitivity, destination risk, and historical patterns. A sales representative accessing CRM data during business hours through approved applications presents different risk than the same access occurring at 3 AM from an unfamiliar device uploading to personal cloud storage. Nightfall attaches that context to every incident, including role, data lineage, prior behavior, and HRIS or IdP metadata, so legitimate business activity is separated from real exfiltration without slowing teams down.

Comparing Zenity with Legacy DLP and Emerging DLP 2.0 Solutions

The data loss prevention market has evolved through distinct generations. Traditional DLP products historically relied heavily on rules, dictionaries, patterns, and fingerprints, and the major platforms also support exact matching, document fingerprinting, machine learning, OCR, behavioral context, cloud applications, email, and endpoints. Forcepoint, Proofpoint, and Symantec each document classification techniques spanning exact data matching, indexed or fingerprinted documents, machine-learning classifiers, and content matching across email, web, cloud, SaaS, and endpoints. Their specific strengths differ by product, and they extend beyond regex-based perimeter monitoring.

That generation was designed for an era of pattern matching on files and email. It was built for one actor, the human, and for static content, and it has no model for workflows made up of chains of agents, tools, and data sources acting together. Newer AI data security platforms extend coverage to agent workflows, MCP servers, and autonomous systems. Nightfall was built the other way around from the start: content- and context-aware detection that produces signal on the surfaces that matter now. Nightfall covers browser AI plugins, agentic AI, and MCP in more detail in its write-up on the blind spots legacy DLP cannot see.

The DLP 2.0 generation added data lineage, which is genuinely useful context. Lineage on its own describes where a file has been rather than stopping it from leaving, and lineage-first architectures are oriented toward SaaS and endpoint file movement rather than the agentic runtime, where a local stdio MCP server, a Cursor or Claude Code session, or an agent run touches data directly. Nightfall inverts the design: AI-native detection decides what is risky first, so the lineage teams act on is the lineage that matters, and the same detection brain runs on every surface including the agentic ones, with full inline blocking. AI capability is native to the platform and included in every tier rather than licensed as a separate product line. Nightfall's Cyberhaven comparison and its look at agentic AI data risk walk through what that difference looks like in practice.

A related distinction applies to AI gateways such as Harmonic, LiteLLM, MintMCP, Runlayer, and Palo Alto AI Access Security. Gateways proxy remote MCP traffic, which is useful, and Nightfall supports remote MCP as well. What a gateway sits outside of is the laptop itself: the local stdio server, the IDE agent session, and the file an agent just touched, and it routes traffic rather than classifying and enforcing on the sensitive content flowing through it. Gateway is a feature. AI data security is a platform. Nightfall explains the mechanics in its piece on how MCP can bypass traditional security tools.

Data security posture management sits in a different place again. Posture and cataloging have real value, and prevention does not require posture as a prerequisite. Cataloging data at rest for six to twelve months while exfiltration goes unprevented is the wrong order of operations. Nightfall begins preventing on day one and delivers data discovery and classification as a byproduct, which means an existing DSPM investment can stay in place without delaying prevention.

Zenity positions itself within the AI data security category with a specific center of gravity:

• Agent-centric security and governance rather than general-purpose enterprise data protection
• Posture and exposure management addressing configurations and exploitable attack paths before runtime
• Platform coverage spanning Microsoft, Salesforce, ServiceNow, AWS, Google Cloud, ChatGPT Enterprise, Claude Enterprise, custom agents, endpoints, and MCP, with depth varying by platform
• Content- and behavior-aware data-leakage controls inside supported agent workflows, rather than a general-purpose enterprise DLP suite

Organizations evaluating Zenity should understand this positioning. If the primary concern is governing AI agents across enterprise and custom platforms, Zenity's specialized approach provides depth in that lane. If concerns extend to human-initiated email activity, browser-based data leakage unrelated to agents, general endpoint protection, or broad SaaS data loss prevention, a platform that governs both actors in one stack addresses more of the requirement. Human risk and AI risk are not two problems. They are one, and solving either alone leaves the other side exposed.

Zenity's Place in the DLP Ecosystem

The fundamental question is whether agent-centric governance or unified data security better serves organizational needs. It is important to distinguish agentic activity on a surface from general-purpose DLP for all activity on that surface. Zenity now covers agentic activity across SaaS, cloud and homegrown, browser-adjacent, device-based, and endpoint environments, including sensitive-data leakage through agent conversations, tool calls, encoded payloads, local tools, and model-provider interactions.

What Zenity's public documentation does not establish is comprehensive general-purpose DLP for all human-initiated data movement unrelated to AI agents, including:

• Email security covering Gmail, Outlook, and Exchange for ordinary human correspondence
• Browser-based data leakage through human copy-paste, uploads, and non-agent web activity
• General endpoint data movement outside agent-mediated activity
• SaaS application content scanning across Slack, Jira, Confluence, Google Drive, and similar platforms for human-generated content

Organizations already operating comprehensive data security programs may view Zenity as a specialized addition for agent-specific governance. Organizations lacking foundational DLP will find that a unified platform covering both human and agent data movement addresses more of their requirements in a single procurement, consolidating DLP, insider risk, and AI governance into one stack instead of three contracts and three budget lines.

Security for Copilots and AI Assistants

Microsoft Copilot, Salesforce Agentforce, and similar enterprise AI assistants have become primary channels for sensitive data interaction. Employees ask questions containing customer information, share documents through AI interfaces, and rely on copilots to process confidential materials. Securing these interactions requires understanding both the AI platform and the data flowing through it.

Zenity's copilot security strengths include:

• Native Copilot Studio integration with documented prevention and automated response, plus observability into data access
• Salesforce Agentforce governance and posture management
• ServiceNow SecOps integration for incident management workflows
• Configuration review for permissions, integrations, and tool access before deployment

Zenity's deepest documented runtime integration is with Microsoft Foundry, where the company announced general availability of agent runtime security on March 17, 2026, building on inline prevention capabilities described in November 2025. That Foundry timeline applies to Foundry rather than to Copilot Studio, which has its own separately documented prevention controls.

Nightfall covers the assistant layer from the data side, with AI application coverage spanning ChatGPT, Claude, Copilot, Gemini, and other generative AI tools, and with browser and endpoint enforcement applied to prompts, uploads, and pastes before content leaves the device.

Innovations in AI Agent Security

Zenity correlates posture, runtime, identity, data, and behavioral signals into shared context and validated attack paths. Its Clarity Agent performs intent-aware runtime analysis of execution, tool calls, memory access, and data-usage patterns, while AI Exposure Management correlates identity, data, and behavior into reachable end-to-end attack chains, and AIDR correlates build-time posture risk with runtime execution. Correlation through OCSF is also available via AWS Security Hub Extended. The intent is to reduce alert fatigue by presenting security teams with contextualized incidents rather than isolated alerts.

Correlation quality depends on the breadth of data sources feeding the system. Platforms with visibility into agent-mediated events draw on a narrower signal set than platforms that also observe human email, endpoint, browser, and SaaS interactions, and that wider context is often what clarifies incident severity. Security programs benefit from applying consistent detection and policy controls across SaaS, endpoint, browser, email, and AI workflows rather than maintaining agent-specific silos. Nightfall's approach to securing AI agents applies the same brain to every one of those surfaces, and its AI agent risk report documents where the exposure is concentrating in 2026.

Deployment and Operational Advantages: Zenity vs. Unified Platforms

Deployment complexity and time to value significantly impact security outcomes. Tools that take months to configure provide limited protection during implementation periods. Quick deployment allows organizations to achieve baseline visibility and control early, then refine policies based on observed behavior.

Zenity deployment characteristics:

• Publicly presented as a SaaS platform delivered through a hosted, region-specific application portal; the public materials reviewed do not document a self-hosted, on-premises, or air-gapped edition
• Documented integrations for Microsoft, Salesforce, ServiceNow, AWS, Google Cloud, and other agent environments, with implementation time varying by platform and deployment scope
• A layered architecture in which inventory and posture context inform enforcement, alongside documented runtime blocking, boundaries, automated playbooks, and inline controls
• Vendor-reported enterprise scale, including 80% risk reduction across an anonymous Fortune 50 financial services tenant containing more than 150,000 resources

Unified data security platforms offer a different deployment profile. Nightfall connects API-based SaaS integrations in minutes, with organizations going live across supported SaaS applications in under an hour (data detection and response). Its single endpoint agent covers human and AI or MCP traffic across more than 10 exfiltration vectors, runs at roughly 1% CPU and 50MB RAM with macOS and Windows parity, and deploys in about 30 minutes through standard MDM tooling such as Intune or SCCM (endpoint and browser DLP).

Reducing Operational Overhead in Data Security

Operational burden extends beyond initial deployment. Security teams must maintain policies, investigate alerts, respond to incidents, and demonstrate compliance over time. Platforms that automate routine tasks and reduce false positives allow teams to focus on genuine security threats rather than alert triage.

Zenity publishes an anonymized customer-reported figure of 95% of high-risk violations automatically remediated at a Fortune 200 consulting company. Zenity also reports 90% of existing vulnerabilities remediated within four months with two FTEs at an anonymous Fortune 20 technology company. These are vendor-published, anonymized outcomes rather than independently audited results.

The broader question is total operational burden across the security program. Running separate tools for DLP, CASB, insider risk, and AI governance creates vendor relationships, integration complexity, and training requirements that compound operational costs. Consolidating those functions reduces that burden directly. Nightfall's autonomous DLP analyst surfaces the highest-risk users before exfiltration happens, builds and tunes policies to the environment, and packages every incident with a complete forensic story, which moves SecOps from triage to oversight and governance.

Why Nightfall AI Stands Out for AI Data Security

Nightfall AI approaches the AI data security challenge from a different position than agent-centric governance platforms. AI moves your data. Nightfall controls it. Rather than focusing on AI agents alone, Nightfall is the AI data security platform that governs data movement across both actors, humans and AI agents, in real time and across every surface: supported SaaS applications, endpoints, browsers, email integrations, generative AI applications, and AI agent or MCP workflows. More than 100 organizations run on Nightfall, including Gusto, DraftKings, Grafana Labs, Grab, Nubank, and Decagon.

Key differentiators include:

• Unified platform consolidation that replaces separate SaaS DLP, endpoint DLP, insider-risk, generative AI governance, and MCP security tools with a single stack and a single detection brain
• AI-native detection quality of 95% precision out of the box, against a 5 to 25% baseline attributed to legacy pattern-matching DLP, using ML and LLM-based classifiers across more than 20 sensitive-data categories
• Coverage across every surface including real-time and historical scanning across 13 SaaS applications, email integrations, browser protection, and endpoint channels such as browser uploads, clipboard activity, cloud sync, USB, printing, screen capture, and AI prompts (data exfiltration prevention)
• AI-powered data lineage that tracks sensitive assets from corporate sources to external destinations across endpoint and application workflows, including after downloading, renaming, compressing, and transferring, with AI agent audit trails that distinguish agent actions from human actions
• Session-aware identity controls that distinguish corporate and personal account activity on supported web domains through the Nightfall browser extension and endpoint integration
• Extensible protection for homegrown and third-party AI applications through Nightfall APIs, alongside coverage for MCP-connected workflows and AI coding assistants including Claude Code, Cursor, and GitHub Copilot in VS Code, with documented prompt-sanitization use cases for frameworks such as LangChain

Nightfall's MCP security capabilities address both local stdio and remote HTTP/SSE workflows with Shadow MCP detection, user and device attribution, tool-call monitoring, and per-server risk scoring, with tools classified by what they can actually do: read, read/write, or destructive. Enforcement is full inline blocking rather than visibility or alerting alone, and prompt-injection detection runs on agent traffic. That combination gives the CISO a defensible answer to the question of whether AI agent risk is governed, backed by control rather than discovery.

For security leaders evaluating AI data security investments, the choice between agent-centric governance and unified data protection depends on organizational context. Organizations with mature, comprehensive data security programs that need deep agent governance may find Zenity's specialization valuable. Organizations seeking to consolidate security tooling while extending coverage to AI workflows can weigh Nightfall's consolidated platform approach against point solutions across licensing, implementation, staffing, coverage, and remediation. Nightfall's unified architecture reduces total cost of ownership by collapsing DLP, insider risk, and AI governance into one contract and one control plane. Nightfall publishes pricing and platform comparisons, and teams can see the platform in a demo.

Frequently Asked Questions

What specific enterprise platforms does Zenity support?

Zenity documents named integrations and use cases for Microsoft Copilot Studio, Microsoft Power Platform, Microsoft 365 Copilot, Microsoft Foundry, Salesforce and Agentforce, ServiceNow, AWS Bedrock and Bedrock AgentCore, Google Vertex AI, ChatGPT Enterprise, and Claude Enterprise. It also identifies agentic SaaS, cloud and homegrown agents, coding and personal agents, device-based agents, and MCP-connected systems as supported agent types. Zenity's public pages do not enumerate LangChain, CrewAI, AutoGen, or Databricks by name, though its documentation does reference custom and homegrown frameworks, and a missing product page is not evidence of non-support. Organizations relying on custom frameworks can cover them with Nightfall's developer APIs and MCP security, which apply the same detection and enforcement used on every other surface.

How does Zenity's pricing compare to unified data security platforms?

Zenity does not publish standard pricing on its public pages, and licensing units in the agent governance category vary, with models that can be based on users, agents, resources, integrations, or data volume. Total cost in this category also turns on whether an agent-centric platform sits alongside a general-purpose program for human-initiated email, endpoint, browser, and SaaS activity. Organizations already operating comprehensive data security programs may view agent governance as an incremental line item, while organizations building programs from scratch generally get more coverage per contract from a consolidated platform. Nightfall publishes pricing and includes AI-native detection and agent coverage in every tier rather than as a separate add-on.

Can Zenity operate in air-gapped or on-premises environments?

Zenity is publicly presented as a SaaS platform delivered through a hosted application portal, and the public materials reviewed do not document a self-hosted, on-premises, or air-gapped edition. Absence from public marketing documentation is not proof that no private, managed, or sovereign deployment arrangement exists. Zenity also markets use cases across government, healthcare, and financial services and announced a June 2026 Carahsoft partnership targeting government agencies, so blanket assumptions about exclusion from regulated sectors are not warranted. Organizations with residency or sovereignty mandates in regulated industries such as financial services and digital health can review Nightfall's security and trust documentation for its own architecture.

What proactive security testing capabilities does Zenity offer?

Zenity documents proactive exposure validation rather than posture management and reactive detection alone. Its AI Exposure Management capability validates which agent attack paths are exploitable, correlates signals into complete attack paths, and surfaces reachable end-to-end chains. Zenity also runs risky skills, files, and code in a sandbox to determine actual behavior for coding and personal agents, operates a Security Assessment Hub, and publishes hands-on exploit research such as its AgentFlayer findings. Public materials center on exposure validation and sandboxing rather than a packaged adversarial-probe library, and organizations requiring model-level adversarial testing may run dedicated tooling alongside it. Nightfall complements that testing posture with runtime enforcement, including prompt injection detection on agent traffic and inline blocking on the surfaces where data actually moves.

How does Zenity handle data from customer service conversations and support tickets?

Zenity is agent-centric, and it does inspect data access and use within agent workflows. Its AI Observability capability tracks the data agents access, including RAG queries, file access, user attachments, and retrievals, while AIDR analyzes data access and usage and documents monitoring and blocking of sensitive-data leakage through agent conversations, tool calls, and encoded payloads. Copilot Studio observability likewise captures data access steps. The content protection described in the public materials is scoped to agent workflows, so organizations concerned about sensitive customer data in human-generated tickets, messages, and files will also want general-purpose data detection and response across Slack, Jira, Confluence, Zendesk, Google Drive, and email, applied by the same detection brain that governs their agents.