No solution approach in the current research connects this vendor to this use case.
Vendor research
Cyberhaven
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
No related approach foundBack 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
Scaled?
ScaledA private provider with at least $100M in known funding or at least 250 employees.This is a company-scale signal, not a product-quality rating.
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ScaledA private provider with at least $100M in known funding or at least 250 employees.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.
- $100M known funding
- 50-250 employees
Company context
Private independent company; reviewed Cyberhaven-controlled pages list Cyberhaven, Inc. without an acquisition or parent-company claim
Cyberhaven positions its AI and data security platform across data security posture management (DSPM), data loss prevention (DLP), insider risk, and AI security for endpoints, cloud, on-prem, software as a service (SaaS), and AI tools
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
- 4
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.
Founded2018
HeadquartersMountain View, California
OwnershipPrivate independent company; reviewed Cyberhaven-controlled pages list Cyberhaven, Inc. without an acquisition or parent-company claim
Employees50-250
Capital and scaleIndependent company
Cyberhaven
- Known funding
- $100M
- Operating scale
- Cyberhaven positions its AI and data security platform across DSPM, DLP, insider risk, and AI security for endpoints, cloud, on-prem, SaaS, and AI tools
- Backing context
- Vendor-controlled pages reviewed did not provide investor or funding ownership details
SERIES_C_PLUS · $100M · 2025-04
Founders and leadership1 person listed
- Dr. Volodymyr KuznetsovCurrent role listed
Co-Founder & Chief Technology Officer
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
- 2018
- Workforce scale
- 50-250
- 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 sourceCyberhaven AI Security product pageCyberhaven describes enterprise AI security controls for autonomous agents, AI apps, prompt/response data flow, MCP servers, and AI connectors.Company sourceCyberhaven homepageCyberhaven says its AI and data security platform unifies DSPM, DLP, Insider Risk, and AI Security across endpoints, cloud, on-prem, SaaS, and AI tools.Company information sourceStored company websiteSupports the company facts shown in this profile.Company information sourceCyberhaven current leadership pageSupports the company facts shown in this profile.Company information sourceCyberhaven co-founder product briefingSupports 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.
Sensitive-data discovery and accessCore product focusAI data protectionCore product focusEmployee AI access and usage controlsCore product focusBrowser and extension controlsRelated coverageAction-taking agent safeguardsRelated coverageAI asset and configuration securityRelated coverageEndpoint AI application controlsRelated coverageCoding-agent and developer workstation securityRelated coverageAI gateway and tool-connection controlsRelated coverage
Buyer context
- Treat Cyberhaven as a data-lineage, data loss prevention (DLP), insider-risk, and AI data-security platform extension with strong data-flow controls into generative and agentic AI.
- Public evidence supports approved and unapproved AI app inventory, prompt/response data-flow controls, browser/endpoint/CLI/integrated development environment (IDE) agent inventory, Model Context Protocol (MCP) monitoring, and agent interaction lineage.
- Public pages reviewed did not expose pricing, AI FinOps, or non-human identity (NHI)/service-account lifecycle claims.
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
- 14
- Related requirements
- 11
- References
- 58
- Requirements with public support
- 14
- Related requirements
- 14
- References
- 71
- Requirements with public support
- 14
- Related requirements
- 12
- References
- 35
- Requirements with public support
- 14
- Related requirements
- 12
- References
- 71
- Requirements with public support
- 14
- Related requirements
- 13
- References
- 39
- Requirements with public support
- 14
- Related requirements
- 12
- References
- 39
- Requirements with public support
- 14
- Related requirements
- 12
- References
- 31
- Requirements with public support
- 14
- Related requirements
- 11
- References
- 27
- Requirements with public support
- 14
- Related requirements
- 13
- References
- 31
- Requirements with public support
- 14
- Related requirements
- 13
- References
- 25
- Requirements with public support
- 2
- 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
Strong 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
Strong 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
Limited 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
Limited 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
No supporting claim foundInventory, 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.
Cyberhaven claims continuous inventory of AI agents, applications, and Model Context Protocol (MCP) servers, multidimensional AI risk scoring, data-lineage chain of custody, behavioral telemetry, and policy enforcement for autonomous systems.
Continuous, automatically maintained inventory of every AI agent, GenAI application, and MCP server across your environment.
Cyberhaven AI Security materials reviewed did not provide a public 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.
Cyberhaven claims continuous inventory of agents, local models, plugins, Model Context Protocol (MCP) servers, and tools plus AI Risk IQ scoring that includes model integrity and tracks component and version changes through endpoint and data lineage context.
Cyberhaven assigns an AI Risk IQ score across five dimensions: data sensitivity, model integrity, compliance adherence, user access, and security infrastructure.
Cyberhaven claims endpoint-resident runtime observability and policy enforcement across agent files, application programming interfaces (APIs), Model Context Protocol (MCP) servers, tools, outputs, and sensitive-data movement using behavioral context and data lineage.
The third pillar is runtime policy enforcement.
Cyberhaven claims user and device attribution, user-access risk scoring, agent activity chain of custody, and visibility into agents operating with user permissions.
Security teams can see which files were accessed, how data moved, and whether sensitive content traveled to an unexpected destination.
Cyberhaven claims endpoint inventory and full execution-lifecycle reconstruction for local coding agents across browsers, CLIs, integrated development environments (IDEs), files, Model Context Protocol (MCP) servers, application programming interfaces (APIs), generated outputs, and sensitive-data movement.
Continuously inventories AI agents running across endpoints, browsers, command-line interfaces, and IDEs.
Show 13 additional evidence records
Cyberhaven claims it automatically inventories approved and unapproved AI apps as they appear across an organization.
Cyberhaven automatically inventories sanctioned and unsanctioned AI apps as they appear across the organization, from mainstream SaaS generative AI applications to endpoint coding assistants, open-source agent frameworks, and MCP servers.
Cyberhaven claims it inventories mainstream software as a service (SaaS) generative AI applications as part of AI app discovery.
Cyberhaven automatically inventories sanctioned and unsanctioned AI apps as they appear across the organization, from mainstream SaaS generative AI applications to endpoint coding assistants, open-source agent frameworks, and MCP servers.
Cyberhaven claims usage and adoption insights categorize AI applications as approved, unapproved, tolerated, or restricted.
Usage and Adoption Insights Surfaces AI adoption trends across the enterprise, categorizing applications as Sanctioned, Unsanctioned, Tolerated, or Restricted to support governance decisions.
Cyberhaven claims runtime guardrails block high-risk data movement, redirect users to approved tools, and coach employees.
Enforces runtime guardrails at the prompt and response level, blocking high-risk data movement, redirecting users to sanctioned tools, and coaching employees with plain-English policy explanations.
Cyberhaven claims prompt- and response-level guardrails block high-risk data movement.
Enforces runtime guardrails at the prompt and response level, blocking high-risk data movement, redirecting users to sanctioned tools, and coaching employees with plain-English policy explanations.
Cyberhaven claims Shadow AI Discovery inventories AI agents across endpoints, browsers, CLIs, and integrated development environments (IDEs).
Continuously inventories AI agents running across endpoints, browsers, CLIs, and IDEs, including tools that cloud-only security solutions cannot see.
Cyberhaven claims prompt- and response-level runtime guardrails with block, redirect, and coaching controls.
Enforces runtime guardrails at the prompt and response level, blocking high-risk data movement, redirecting users to sanctioned tools, and coaching employees with plain-English policy explanations.
Cyberhaven claims Agentic AI Visibility reconstructs agent interaction lifecycles with tool calls, data access, and multi-turn conversation context.
Reconstructs the full execution lifecycle of every agent interaction, capturing tool calls, data access, and multi-turn conversation context in a single view.
Cyberhaven claims it discovers and monitors Model Context Protocol (MCP) servers and AI connectors across the enterprise.
Discovers and monitors Model Context Protocol servers and AI connectors across the enterprise, surfacing risk from integrations that operate outside traditional security controls.
Cyberhaven materials reviewed did not provide a public claim for non-human identity (NHI), service-account, credential lifecycle, or AI-agent identity governance.
No quoted source text is recorded for this claim.
Cyberhaven materials reviewed did not provide a public claim for AI spend attribution, model cost routing, budget enforcement, rate limits, or token spend controls.
No quoted source text is recorded for this claim.
Cyberhaven materials reviewed did not provide a public per-user, per-seat, platform, usage-based, or enterprise pricing model.
No quoted source text is recorded for this claim.
Cyberhaven claims its AI and data security platform protects data across endpoints, cloud, on-prem, software as a service (SaaS), and AI tools.
Cyberhaven’s AI & data security platform unifies DSPM, DLP, Insider Risk, and AI Security to protect data wherever it lives and goes across endpoints, cloud, on-prem, SaaS, and AI tools.