Public-source research
Vendor evidence
Review what vendors say publicly, the exact quoted source text, the security requirement each statement may support, and what still needs verification.
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Evidence records1280
Source checked863
Needs verification0
Source captured0
No supporting claim found411
Excluded from evidence6
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.
Related framework references (5)
WitnessAI claims automated predeployment AI red teaming to identify weaknesses in model defenses before deployment.
Automate AI red-teaming to find vulnerabilities pre-deployment.
Related framework references (5)
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.
Related framework references (5)
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.
Related framework references (5)
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.
Related framework references (5)
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.
Related framework references (5)
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.
Related framework references (5)
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
Related framework references (5)
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.
Related framework references (5)
WitnessAI claims it visualizes AI conversations including prompts and responses in real time.
Visualize all AI conversations, including prompts and responses, in real time
Related framework references (5)
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
Related framework references (5)
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
Related framework references (5)
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
Related framework references (5)
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.
Related framework references (5)
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.
Related framework references (5)
WitnessAI claims governance across human and AI-agent workforces, including visibility into agent tools and data access.
human and digital workforce
Related framework references (5)
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
Related framework references (5)
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
Related framework references (5)
No public claim found for this capability.
No quoted source text is recorded for this claim.
Related framework references (2)
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
Related framework references (5)