No solution approach in the current research connects this vendor to this use case.
Vendor research
WitnessAI
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
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.
- $85.5M known funding
- 51-200 employees
- Founded 2023
- Private-company revenue and profitability not sourced
Company context
Private, VC-backed
Company reported more than 500% ARR growth and 5x employee growth over the prior 12 months in 2026 funding announcement
Research coverageCounts describe available public research, not product quality.View details
- Vendor statements
- 20 records
- Source-checked records
- 18
- Evaluation requirements
- 19 in this research model
- Unresolved requirements
- 1
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.
Founded2023
HeadquartersMountain View, California
OwnershipPrivate, VC-backed
Employees51-200
Capital and scaleIndependent company
WitnessAI
- Known funding
- $85.5M
- Operating scale
- Company reported more than 500% ARR growth and 5x employee growth over the prior 12 months in 2026 funding announcement
- Backing context
- $58M strategic round led by Sound Ventures with Fin Capital, Qualcomm Ventures, Samsung Ventures, and Forgepoint participation; prior backing includes GV and Ballistic Ventures
Strategic funding · $58M · 2026-01-13
Named investors
Sound Ventures · Fin Capital · Qualcomm Ventures · Samsung Ventures · Forgepoint Capital Partners · GV · Ballistic Ventures
Founders and leadership1 person listed
- Rick CacciaCurrent role listed
Co-Founder & CEO
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
- 2023
- Workforce scale
- 51-200
- Hiring activity
- 3 open positions · stable
A hiring count is shown only when a clickable source is available.
Ashby careers board ↗Core company facts have supporting public sources.
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 sourceWitnessAI funding announcementWitnessAI announced a $58M strategic funding round and agentic AI governance expansion.Company sourceCB Insights company profilePublic profiles list WitnessAI as founded in 2023 and headquartered in Mountain View.Company information sourcecompany_intel_seedSupports the company facts shown in this profile.Company information sourceAshby careers boardSupports the company facts shown in this profile.Company information sourceWitnessAI LinkedIn company profileSupports 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.
Buyer context
- Notable investor and board/advisor signal for enterprise AI security credibility.
- Buyer diligence should test deployment architecture, data path, and how network-level controls work with existing data loss prevention (DLP)/SSE architecture.
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 referenceCSA AI Controls Matrix
- Requirements with public support
- 17
- Related requirements
- 14
- References
- 71
- Requirements with public support
- 17
- Related requirements
- 12
- References
- 35
- Requirements with public support
- 17
- Related requirements
- 12
- References
- 71
- Requirements with public support
- 17
- Related requirements
- 13
- References
- 39
- Requirements with public support
- 17
- Related requirements
- 12
- References
- 39
- Requirements with public support
- 17
- Related requirements
- 12
- References
- 31
- Requirements with public support
- 17
- Related requirements
- 13
- References
- 31
- Requirements with public support
- 17
- Related requirements
- 13
- References
- 25
- Requirements with public support
- 16
- Related requirements
- 11
- References
- 58
- Requirements with public support
- 16
- Related requirements
- 11
- References
- 27
- Requirements with public support
- 4
- 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
Limited 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
Strong 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
Strong public supportTest 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
Strong 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
Limited 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
Limited public supportVisibility, 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 20 source records. Open additional records only when needed.
WitnessAI claims unified governance across employees and agents using AI inventory, contextual policies, audit trails, reporting, approved-tool control, and compliance-oriented interaction logging.
Apply governance consistently across employees and agents.
WitnessAI claims automated predeployment AI red teaming to identify weaknesses in model defenses before deployment.
Automate AI red-teaming to find vulnerabilities pre-deployment.
WitnessAI claims discovery of agents, Model Context Protocol (MCP) servers, tools, and downstream systems plus Model Context Protocol (MCP) Catalog scoring against OWASP and CVE risk classes before tools are approved.
Scores those tools against OWASP and CVE risk classes through a new MCP Catalog.
WitnessAI claims bidirectional runtime defense and organization-wide allow or block policy enforcement across prompts, responses, agent actions, tools, Model Context Protocol (MCP) servers, models, integrated development environments (IDEs), and applications.
A security team approves which MCP servers and tools agents may use, and enforces that policy organization-wide.
WitnessAI claims role and team-based AI access, human attribution for agent activity, and organization-wide authorization policies for agent access to approved Model Context Protocol (MCP) servers and tools.
Attribute AI agent activity to human identities.
WitnessAI claims controls for AI coding tools and agents, source code, intellectual property, secrets, integrated development environment (IDE) activity, and coding-agent interactions with Model Context Protocol (MCP) servers and tools.
Enforce control over AI coding agents’ interactions with MCP servers and tools.
Show 14 additional evidence records
WitnessAI claims AI-interaction visibility and attribution, intent-based policies that consider risk, cost, and purpose, model routing based on cost, and audit trails supporting financial accountability.
It applies intent-based machine learning engines and intelligent policies that account for risk, cost, and purpose together.
WitnessAI claims it uncovers shadow AI usage and catalogs AI applications, Model Context Protocol (MCP) servers, and agents while monitoring real-time interactions.
Uncover shadow AI usage, catalog your complete AI inventory—applications, MCP servers, and agents—and monitor real-time interactions
WitnessAI claims detection and governance coverage for thousands of AI applications and native AI-enabled apps.
4,000+ AI applications can be detected by the WitnessAI catalog, no endpoint client required.
WitnessAI claims it visualizes AI conversations including prompts and responses in real time.
Visualize all AI conversations, including prompts and responses, in real time
WitnessAI claims it can enforce control of approved Model Context Protocol (MCP) servers and tools across agents, integrated development environments (IDEs), and agentic apps.
Enforce control of approved MCP servers and tools across every agent, IDE, and agentic app
WitnessAI claims it protects sensitive data across employee and agent activity by redacting it in real time.
Protect sensitive data across employee and agent activity
WitnessAI claims it discovers running agents and the external Model Context Protocol (MCP) servers and tools they connect to, and governs agent actions with runtime security.
Discover which agents are running and what external MCP servers and tools they connect to
WitnessAI claims enforcement for agent deployments at the tool-call and Model Context Protocol (MCP)-server level.
Govern every form of agent deployment, from custom cloud agents to agentic IDEs, with enforcement at the tool call and MCP server level.
Superseded stale absence record for WitnessAI agent-to-agent security.
Govern every form of agent deployment, from custom cloud agents to agentic IDEs, with enforcement at the tool call and MCP server level.
WitnessAI claims governance across human and AI-agent workforces, including visibility into agent tools and data access.
human and digital workforce
WitnessAI claims it provides granular role- and team-based AI access, enforces usage policies, and attributes agent activity to human identities.
Provide granular role and team-based AI access
WitnessAI claims it blocks prompt injection and jailbreak attempts with bidirectional runtime defense and filters outputs before they reach users or agents.
Block prompt injection and jailbreak attempts with bidirectional runtime defense
No public claim found for this capability.
No quoted source text is recorded for this claim.
WitnessAI claims it routes prompts to the right models based on risk, cost, or purpose and applies governance across employees and agents.
Intelligently route prompts to the right models based on risk, cost, or purpose