Action-taking agent safeguardsDirectly addresses · Observe and govern agent plans, memory, tool use, delegated tasks, and actions while the agent runs.AI application runtime protectionDirectly addresses · Protect custom AI applications, information-retrieval systems, model calls, prompts, and outputs while they run.AI gateway and tool-connection controlsAdditional if: Model or tool traffic uses a security gateway · Model, tool, connector, API, or Model Context Protocol (MCP) traffic is routed through a security gateway.Coding-agent and developer workstation securityAdditional if: Coding agents reach developer systems · Coding agents can reach code editors, command-line tools, repositories, build systems, software packages, or developer workstations.
Vendor research
Zenity
Review what this vendor says publicly, the security topics those statements may support, what remains unverified, and factual company context. This is not an assessment of product effectiveness or fit.
Use-case context
How this vendor relates to the selected use case
Related research availableBack to use case →
These links show approaches associated with this vendor. The relationship label describes how the approach maps to the use case—not product effectiveness, complete requirement coverage, or fit.
Company scale
Growth stage?
Growth stageA provider with at least $25M in known funding or at least 51 employees that has not reached the scaled threshold.This is a company-scale signal, not a product-quality rating.
?
Growth stageA provider with at least $25M in known funding or at least 51 employees that has not reached the scaled threshold.This is a company-scale signal, not a product-quality rating.A descriptive band derived from retained public revenue, workforce, ownership, or funding signals.
Company scale is separate from product features, effectiveness, and suitability.
- $55M known funding
- <50 employees
- Founded 2021
Company context
Private, VC-backed
Started in low-code/no-code security and expanded into agentic AI, copilots, and business-built apps
Research coverageCounts describe available public research, not product quality.View details
- Vendor statements
- 19 records
- Source-checked records
- 15
- Evaluation requirements
- 19 in this research model
- Unresolved requirements
- 3
Company intelligence
Who is behind the product
Company facts provide evaluation context. Each signal is kept separate because tenure, workforce, funding, and hiring answer different questions.
Founded2021
HeadquartersTel Aviv-Yafo, Israel
OwnershipPrivate, VC-backed
Employees<50
Capital and scaleIndependent company
Zenity
- Known funding
- $55M
- Operating scale
- Started in low-code/no-code security and expanded into agentic AI, copilots, and business-built apps
- Backing context
- $38M Series B co-led by Third Point Ventures and DTCP; total capital raised over $55M
Series B · $38M · 2024-10-29
Named investors
Third Point Ventures · DTCP · M12 · Intel Capital · Vertex Ventures
Founders and leadership2 people listed
- Ben KligerCurrent role listed
Co-Founder & CEO
- Michael BarguryCurrent role listed
Co-Founder & CTO
Operating signalsRead each signal separately
There is no combined company rating. The company-scale label uses stated size thresholds; product features and effectiveness require separate evidence.
- Company tenure
- 2021
- Workforce scale
- <50
- Hiring activity
- Not displayed
A current count requires a retained, clickable source URL.
Open research questions
- A current hiring source is not available, so the count is not shown.
Company sources and research limits5 linked public sources
Only company facts supported by retained public sources are shown. Missing values remain unknown, and company scale does not establish product effectiveness.
Company sourceZenity Series B announcementZenity announced a $38M Series B and more than $55M total capital raised.Company sourceZenity seed announcementZenity announced its early low-code/no-code security funding in 2021.Company information sourcecompany_intelSupports the company facts shown in this profile.Company information sourceStored company websiteSupports the company facts shown in this profile.Company information sourceZenity company and leadership pageSupports the company facts shown in this profile.
Solution areas
These areas describe how the vendor approaches enterprise AI security. They do not establish product quality or fit.
Action-taking agent safeguardsCore product focusAI application runtime protectionCore product focusAI asset and configuration securityRelated coverageAI gateway and tool-connection controlsRelated coverageEndpoint AI application controlsRelated coverageCoding-agent and developer workstation securityRelated coverage
Buyer context
- Relevant if the client has heavy Microsoft Copilot, low-code, automation, or business-user-built agent exposure.
- Buyer diligence should distinguish application-builder governance from generalized employee generative AI data loss prevention (DLP).
Related frameworks
Where public vendor statements relate to framework requirements
11 related frameworks · expand when needed
Related frameworks
Where public vendor statements relate to framework requirements
These links show related requirements for further review. They do not establish framework compliance or control implementation. Open the full framework crosswalk →
Current referenceCIS Critical Security Controls
- Requirements with public support
- 15
- Related requirements
- 11
- References
- 58
- Requirements with public support
- 15
- Related requirements
- 14
- References
- 71
- Requirements with public support
- 15
- Related requirements
- 12
- References
- 35
- Requirements with public support
- 15
- Related requirements
- 12
- References
- 71
- Requirements with public support
- 15
- Related requirements
- 13
- References
- 39
- Requirements with public support
- 15
- Related requirements
- 12
- References
- 39
- Requirements with public support
- 15
- Related requirements
- 12
- References
- 31
- Requirements with public support
- 15
- Related requirements
- 11
- References
- 27
- Requirements with public support
- 15
- Related requirements
- 13
- References
- 31
- Requirements with public support
- 15
- Related requirements
- 13
- References
- 25
- Requirements with public support
- 3
- Related requirements
- 3
- References
- 3
Evaluation questions
What to verify beyond public claims
These questions come from security requirements with some public support. Use them as starting points for demonstrations, documentation review, customer references, or a buyer-observed pilot.
- 01Unapproved AI use discovery
An unmanaged AI app used by a test user appears in discovery inventory with user, app or domain, and timestamp.
- 02Unapproved AI use discovery
The test user's AI usage activity can be filtered or exported with AI-specific context.
- 03AI-feature discovery in business applications
A software as a service (SaaS) app with an embedded AI feature appears in the software as a service (SaaS) AI inventory with app, provider, and feature context.
- 04AI-feature discovery in business applications
The inventory shows which users, data classes, integrations, or providers are associated with the AI-enabled software as a service (SaaS) app.
- 05Approved AI usage monitoring
Approved AI workspace activity appears with user, workspace or tenant, model or provider, and timestamp.
- 06Approved AI usage monitoring
Prompt, model, or admin activity can be exported or correlated for the selected approved AI platform.
- 07Controls for unapproved AI use
A policy blocks, coaches, redirects, or contains a test interaction with an unapproved AI destination.
- 08Controls for unapproved AI use
The control event records policy reason, user, destination, action, and timestamp.
Detailed security-requirement research18 evaluation items · supporting evidence and open research are shown separatelyExpand
Security requirementPublic supportRelated frameworksWhat to verify
Unapproved AI use discovery
Strong public supportDiscover and monitor workforce AI tools, accounts, prompts, domains, models, users, and usage outside approved controls.
NIST AI RMF PlaybookNIST Cybersecurity Framework 2.0CIS Critical Security ControlsISO/IEC 42001OWASP GenAI Security Solutions Landscape
An unmanaged AI app used by a test user appears in discovery inventory with user, app or domain, and timestamp.
AI-feature discovery in business applications
Limited public supportInventory software as a service (SaaS) applications that embed AI features, expose enterprise data to AI capabilities, or create AI-driven data movement.
NIST AI RMF PlaybookNIST Cybersecurity Framework 2.0CIS Critical Security ControlsISO/IEC 42001OWASP GenAI Security Solutions Landscape
A software as a service (SaaS) app with an embedded AI feature appears in the software as a service (SaaS) AI inventory with app, provider, and feature context.
Approved AI usage monitoring
Strong public supportMonitor approved AI workspaces, tenants, gateways, and model platforms such as ChatGPT Enterprise, Claude Enterprise, Gemini, Microsoft Copilot, Vertex AI, Elvex, or internal AI gateways.
NIST AI RMF PlaybookNIST Cybersecurity Framework 2.0CIS Critical Security ControlsISO/IEC 42001OWASP GenAI Security Solutions Landscape
Approved AI workspace activity appears with user, workspace or tenant, model or provider, and timestamp.
Controls for unapproved AI use
Limited public supportBlock, coach, redirect, or contain non-approved AI use and policy-violating AI interactions.
NIST AI RMF PlaybookNIST Cybersecurity Framework 2.0CIS Critical Security ControlsISO/IEC 42001OWASP GenAI Security Solutions Landscape
A policy blocks, coaches, redirects, or contains a test interaction with an unapproved AI destination.
Sensitive-data protection for generative AI
Strong public supportDetect, classify, redact, or block sensitive data in prompts, responses, files, retrieval, memory, and AI-connected workflows.
NIST AI RMF PlaybookNIST Cybersecurity Framework 2.0CIS Critical Security ControlsISO/IEC 42001OWASP GenAI Security Solutions Landscape
Sensitive prompt, response, or file test data is detected and classified during an AI interaction.
Browser and business-application controls
Strong public supportApply session-level controls in browser and software as a service (SaaS) workflows, including uploads, downloads, copy/paste, sharing, and identity-aware access decisions.
NIST AI RMF PlaybookNIST Cybersecurity Framework 2.0CIS Critical Security ControlsISO/IEC 42001OWASP GenAI Security Solutions Landscape
A session-level policy controls upload, download, copy, paste, sharing, or form submission in a browser or software as a service (SaaS) workflow.
Generative AI application security
Limited public supportProtect enterprise-built large language model (LLM) applications, retrieval-augmented generation (RAG) systems, prompts, application programming interfaces (APIs), model calls, tools, and production runtime behavior.
NIST AI RMF PlaybookNIST Cybersecurity Framework 2.0CIS Critical Security ControlsISO/IEC 42001OWASP GenAI Security Solutions Landscape
A test large language model (LLM) application event records prompt, application programming interface (API), model, retrieval, or tool interaction context.
AI governance, risk, and compliance
Strong public supportInventory AI systems and owners, translate policy and regulatory obligations into governed workflows, assess risk, manage approvals and exceptions, and retain audit evidence across the AI lifecycle.
NIST AI RMF PlaybookNIST Cybersecurity Framework 2.0CIS Critical Security ControlsISO/IEC 42001OWASP GenAI Security Solutions Landscape
A test AI system is registered with owner, intended use, risk tier, lifecycle state, and applicable obligations.
AI assurance and adversarial testing
No supporting claim foundTest models, applications, retrieval-augmented generation (RAG) systems, and agents before release and continuously thereafter using adversarial probes, evaluation suites, attack simulation, and security release gates.
NIST AI RMF PlaybookNIST Cybersecurity Framework 2.0CIS Critical Security ControlsISO/IEC 42001OWASP GenAI Security Solutions Landscape
A controlled test campaign exercises an AI model, application, or agent against named AI attack classes.
AI model and supply-chain security
Limited public supportDiscover, inventory, scan, validate, and monitor models, datasets, model artifacts, registries, dependencies, and AI development assets for tampering, unsafe serialization, provenance gaps, or malicious content.
NIST AI RMF PlaybookNIST Cybersecurity Framework 2.0CIS Critical Security ControlsISO/IEC 42001OWASP GenAI Security Solutions Landscape
A test model or AI artifact appears in inventory with origin, version, hash or provenance, and deployment context.
AI gateway, tool-connection, and runtime controls
Strong public supportMediate model, agent, tool, application programming interface (API), connector, and Model Context Protocol (MCP) traffic through an enforcement point that applies identity-aware policy, content controls, routing, rate limits, and auditable allow or deny decisions.
NIST AI RMF PlaybookNIST Cybersecurity Framework 2.0CIS Critical Security ControlsISO/IEC 42001OWASP GenAI Security Solutions Landscape
A model, agent, tool, or Model Context Protocol (MCP) request passes through a named policy enforcement point.
Action-taking agent monitoring
Strong public supportObserve and govern agent plans, memory, tool calls, delegated tasks, autonomy, runtime decisions, and outcomes.
NIST AI RMF PlaybookNIST Cybersecurity Framework 2.0CIS Critical Security ControlsISO/IEC 42001OWASP GenAI Security Solutions Landscape
A test agent run captures plan, steps, tool calls, outcome, and timestamps.
Agent-to-agent communication security
Limited public supportAuthorize, log, and control agent-to-agent, agent-to-tool, Model Context Protocol (MCP), connector, and tool-chain handoffs.
NIST AI RMF PlaybookNIST Cybersecurity Framework 2.0CIS Critical Security ControlsISO/IEC 42001OWASP GenAI Security Solutions Landscape
An agent, tool, connector, or Model Context Protocol (MCP) handoff logs source identity, destination, and authorization decision.
Non-human identity and service-account security
Limited public supportInventory, least privilege, credential hygiene, monitoring, and lifecycle management for non-human identities, workloads, service accounts, application programming interface (API) keys, and machine credentials.
NIST AI RMF PlaybookNIST Cybersecurity Framework 2.0CIS Critical Security ControlsISO/IEC 42001OWASP GenAI Security Solutions Landscape
A test service account, agent identity, or non-human identity appears in inventory with owner and privileges.
AI agent identity and permissions
Limited public supportRegister AI agents as accountable identities, bind them to owners and delegating users, authorize task- and tool-level access, issue short-lived credentials, review access, and revoke or suspend agent authority.
NIST AI RMF PlaybookNIST Cybersecurity Framework 2.0CIS Critical Security ControlsISO/IEC 42001OWASP GenAI Security Solutions Landscape
A test agent is registered with a unique identity, accountable owner, purpose, and permitted resources.
AI coding-agent and workstation security
Strong public supportDiscover and govern AI coding agents, integrated development environment (IDE) assistants, command-line agents, skills, hooks, extensions, Model Context Protocol (MCP) tools, filesystem access, commands, network activity, secrets, and software-supply-chain actions on developer workstations and build environments.
NIST AI RMF PlaybookNIST Cybersecurity Framework 2.0CIS Critical Security ControlsISO/IEC 42001OWASP GenAI Security Solutions Landscape
A test coding agent and its skills, hooks, extensions, or Model Context Protocol (MCP) tools appear in an attributable inventory.
AI cost and usage controls
No supporting claim foundVisibility, attribution, budgeting, rate limiting, anomaly detection, and optimization for AI usage and spend across models, agents, workflows, and owners.
Commercial MetadataNIST AI RMF PlaybookNIST Cybersecurity Framework 2.0ISO/IEC 42001OWASP Agentic AI Security Solutions Landscape
A controlled AI usage event is attributed to user, team, model, workflow, or owner with cost or token metrics.
Licensing model
No supporting claim foundPublicly discoverable commercial model such as per user, per seat, per app, per token, per integration, or enterprise platform license.
The vendor can map the sourced commercial model to per-user, per-seat, per-app, per-token, per-integration, or platform packaging.
Public sources
Vendor statements and quoted evidence
Showing the first 6 of 19 source records. Open additional records only when needed.
Zenity claims continuous discovery, inventory, ownership, configuration, permission, dependency, behavior, policy, posture, graph, incident, and audit governance across software as a service (SaaS)-managed, home-grown, and device-based AI agents.
Security teams gain clear visibility into agent ownership, configurations, permissions, dependencies, and runtime behavior.
Zenity platform materials reviewed did not provide a public customer-facing product claim for automated adversarial testing, repeatable attack suites, model or agent red teaming, or release-gate evaluation.
No quoted source text is recorded for this claim.
Zenity claims pre-session evaluation of Claude configurations, Model Context Protocol (MCP) servers, skills, plugins, and agent extensions plus dependency and attack-path mapping across agents, tools, knowledge, automations, triggers, and actions.
Zenity evaluates Claude configuration, MCP servers, skills, plugins, and other agent extensions before sessions begin.
Zenity claims real-time inline protection and policy enforcement over agent execution, application programming interface (API) calls, tools, prompts, memory, sensitive data, credentials, commands, and multi-step behavior across software as a service (SaaS), cloud, and endpoints.
Run-time security capabilities provide real-time, inline protection against runtime threats.
Zenity claims ownership, identity relationship, permission, tool-access, and execution-path mapping with policy enforcement against over-privileged agents, unauthorized application programming interface (API) calls, restricted-data access, and privilege escalation.
Know which agents exist, who owns them, what they can access, and how they behave across your environment.
Zenity claims lifecycle visibility, posture evaluation, and runtime enforcement for Claude Code and device-based coding agents across code changes, commits, pull requests, configurations, Model Context Protocol (MCP) servers, skills, plugins, commands, credentials, files, tools, and memory.
Zenity's agent security platform deliver[s] full-lifecycle visibility, posture management, and runtime enforcement across Claude Code, Cowork, and Chat.
Show 13 additional evidence records
Zenity platform materials reviewed did not provide a public product claim for AI usage-cost attribution, token or spend metrics, budgets, chargeback, or cost-aware model routing.
No quoted source text is recorded for this claim.
Zenity claims full inventory and attribution of AI agents across platforms, including creators, tools, accessed systems, roles, permissions, runtime activity, and shadow AI.
Zenity provides full inventory and attribution of AI agents across platforms including who created them, what tools they use, and what systems they access. You can drill into user roles, permissions, and runtime activity to track usage and uncover shadow AI.
Zenity claims coverage spans software as a service (SaaS), home-grown agentic platforms, and end-user devices.
Security and governance across all environments - SaaS, home-grown agentic platforms (Cloud), and end-user devices (Endpoint) - with unified visibility, policy control, and threat prevention.
Zenity claims full-stack observability for approved and shadow AI, including inventory, owners, prompts, actions, and runtime activity.
Know which agents exist, who owns them, what they can access, and how they behave across your environment.
Zenity claims intent-based detection examines execution paths, including tool calls, memory access, data usage, and control flow, to identify malicious or unintended outcomes.
By examining the full execution path - including tool calls, memory access, data usage, and control flow - Zenity identifies malicious or unintended outcomes even when inputs look harmless. This intent-focused approach exposes attacks that prompt-based firewalls miss.
Zenity claims it identifies when AI agents access or expose sensitive data and lets teams flag, redact, or block unsafe behavior.
Zenity identifies when AI agents access or expose PHI, PII, PCI, or hardcoded secrets
Zenity claims it monitors step-level agent execution and enforces inline controls to stop unsafe actions.
Monitor step-level agent execution, correlate behavior with context, and enforce inline controls to stop unsafe actions before they impact the business
Zenity claims Model Context Protocol (MCP) server and tool-call governance with runtime visibility, policy enforcement, and protection against risky agent activity.
See and Stop Agent Risk at Runtime
Zenity claims visibility into AI-agent ownership, permissions, integrations, access paths, and least-privilege posture.
ownership, permissions, integrations, and runtime behavior
Zenity claims AI security coverage across user devices, software as a service (SaaS)-managed copilots, and enterprise AI environments.
Security and governance across all environments - SaaS, home-grown agentic platforms (Cloud), and end-user devices (Endpoint) - with unified visibility, policy control, and threat prevention.
Zenity claims it monitors step-level agent execution, correlates behavior with context, and enforces inline controls to stop unsafe actions.
Monitor step-level agent execution, correlate behavior with context, and enforce inline controls to stop unsafe actions
No public claim found for this capability.
No quoted source text is recorded for this claim.
No public claim found for this capability.
No quoted source text is recorded for this claim.