WitnessAI is an AI security platform that provides unified visibility, intent-based behavioral controls, and runtime defense for enterprise AI usage across employees, models, applications, and agents. Unlike pattern-matching AI security tools that scan for known malicious strings, WitnessAI analyzes the meaning and purpose behind each prompt to catch sophisticated multi-turn attacks and contextual jailbreaks.
Co-founded by Rick Caccia (CEO) and incubated by Ballistic Ventures, WitnessAI is headquartered in Mountain View, California. Caccia brings over two decades of cybersecurity leadership experience from Palo Alto Networks, Google Cloud Security, and Exabeam.
In January 2026, WitnessAI announced $58 million in strategic funding led by Sound Ventures, with participation from Fin Capital, Samsung Ventures, Qualcomm Ventures, and Forgepoint Capital Partners — joining existing investors Google Ventures and Ballistic Ventures. The round followed 500% ARR growth and a 5x headcount expansion over the prior year.
The company has been recognized on Fortune’s Cyber60 list, as an SC Awards Excellence Award finalist, and named in the 2025 IDC Innovators report for Security for Agentic AI.
What is WitnessAI?
WitnessAI sits at the infrastructure layer between users and AI models. Instead of building safety features into models or relying on endpoint monitoring alone, the platform intercepts and analyzes AI interactions at the network level, applying intent-based behavioral controls in real time.
The platform is organized into three modules — Observe, Protect, and Control — that work together to give security teams full visibility into AI usage, defend against adversarial attacks, and enforce governance policies across the organization.
What distinguishes WitnessAI from pattern-matching approaches is its intent-based detection engine. Rather than flagging keywords or matching predefined patterns, the system analyzes the meaning and purpose behind each prompt, catching sophisticated multi-turn attacks and advanced prompt injection that rule-based filters miss.
Key Features
| Feature | Details |
|---|---|
| Detection Approach | Intent-based behavioral analysis of prompt meaning and purpose |
| Shadow AI Discovery | Catalogs all AI apps, agents, and MCP servers across the organization |
| Prompt Injection Defense | Blocks advanced attacks including multi-turn and jailbreak attempts |
| Automated Red Teaming | Pre-deployment vulnerability discovery for AI applications |
| Data Protection | Real-time redaction for regulatory compliance |
| Policy Engine | Role-based, department-based, and intent-based governance controls |
| Prompt Routing | Intelligent routing to appropriate models based on risk and cost |
| Agent Security | Monitors agent activity, MCP server access, and tool interactions |
| Audit Trails | Granular logging of all AI interactions for compliance |
| Recognition | Fortune Cyber60, SC Awards finalist, 2025 IDC Innovators (Agentic AI Security) |
| Deployment | Single-tenant with data sovereignty options |
Intent-based controls
Traditional AI security relies on pattern matching — scanning prompts for known malicious strings or keywords. WitnessAI takes a different approach by analyzing the behavioral intent behind each interaction. The system understands what a user or agent is trying to accomplish, not just what words they used.
This matters because sophisticated attackers craft prompts that look innocuous at the surface level but carry malicious intent when interpreted in context. Multi-turn attacks spread malicious instructions across several messages. Intent-based detection catches these by evaluating the conversation as a whole.
Agent and MCP security
As organizations deploy AI agents that interact with external tools and data sources through the Model Context Protocol (MCP), WitnessAI extends its monitoring to cover these interactions. The platform tracks which MCP servers agents connect to, what tools they invoke, and what data flows through these connections.
Intelligent prompt routing
The Control module can route prompts to different AI models based on the sensitivity of the request, the user’s role, and cost considerations. A routine query might route to a cost-efficient model, while a request involving sensitive data routes to a model within the organization’s secure perimeter.
Getting Started
When to use WitnessAI
Ideal for organizations that need infrastructure-level visibility and control over AI usage across the enterprise. The intent-based approach handles sophisticated attacks that bypass pattern-matching defenses — multi-turn prompt injection, contextual jailbreaks, and social engineering through AI channels.
The platform is well suited for large enterprises in regulated industries — financial services, utilities, telecommunications, and automotive — where data sovereignty requirements make cloud-only solutions impractical and where granular audit trails are a compliance necessity.
For a broader overview of AI security risks and solutions, see the AI security tools guide. For browser-level employee monitoring, consider Prompt Security (now part of SentinelOne).
For automated AI red teaming, see Mindgard or Garak. For open-source input/output guardrails, look at LLM Guard or NeMo Guardrails.
