Enterprise AI security startup WhiteMatter has raised $20 million in Series A funding to help organizations secure, govern and manage autonomous AI agents across their technology environments. Formerly operating as QuilrAI, the company is developing an agent-native security platform that combines pre-deployment governance, runtime protection and AI activity monitoring. The funding round was led by Blumberg Capital, with participation from existing investors Crew Capital and Sprout & Oak.
WhiteMatter Raises $20M to Build Security Infrastructure for the Agentic Enterprise
The growing adoption of autonomous AI agents is creating a new challenge for enterprise cybersecurity teams: traditional security infrastructure was largely designed around human users, predictable software and clearly defined access permissions. AI agents introduce a different operating model. They can independently retrieve information, execute tasks, interact with business applications and initiate actions across connected systems.
WhiteMatter wants to address this challenge with a security platform designed specifically for the agentic enterprise. Its $20 million Series A funding will support research and development, go-to-market expansion and investment in executive leadership as the company expands its enterprise AI governance capabilities.
The company, previously known as QuilrAI, positions its platform as a centralized control layer covering AI agents, foundation models, integrations and Model Context Protocol (MCP) servers. Rather than relying exclusively on monitoring systems after an AI agent performs an action, WhiteMatter aims to introduce security controls throughout the agent lifecycle, from development and validation to deployment and runtime execution.
Why Traditional Enterprise Security Is Facing an AI Challenge
Conventional enterprise security systems typically manage identities, applications, endpoints and network activity. While these controls remain important, autonomous AI agents introduce additional complexity because their behavior can change depending on instructions, available tools, contextual information and interactions with other agents.
An AI agent authorized to retrieve business information, for example, could also interact with connected applications, create documents or initiate operational workflows. Without appropriate restrictions, those capabilities can create opportunities for unauthorized access, unintended data exposure and excessive automation.
The governance challenge is already receiving attention from cybersecurity researchers. Gartner predicts that by 2028, an average Fortune 500 enterprise will have more than 150,000 AI agents in use, compared with fewer than 15 in 2025. The research also found that only 13% of organizations believe they have appropriate AI agent governance in place.
This gap creates demand for security infrastructure capable of identifying agents, understanding their permissions and enforcing policies at the point where actions occur.
WhiteMatter’s Guardian Architecture Targets Agent-Level Control
WhiteMatter’s central proposition is to govern AI activity through a dedicated Guardian agent architecture.
For every enterprise AI agent deployed, the platform can provision a corresponding Guardian agent responsible for enforcing policies around its activities. If an organization introduces additional agents, the system is designed to provision additional Guardians automatically.
This architecture is intended to provide continuous oversight as AI deployments expand across endpoints, browsers, SaaS applications, cloud infrastructure and software development environments.
The platform combines several capabilities:
- Agent discovery and governance: Identifying and managing AI agents, models and MCP servers across enterprise environments.
- Pre-deployment validation: Applying governance controls before agents enter production.
- Runtime protection: Checking agent actions against organizational policies before and after execution.
- Centralized monitoring: Providing visibility into AI interactions and activity across connected systems.
- Traffic and cost optimization: Managing agent traffic and AI-related spending through a controlled gateway.
The approach differs from security products focused primarily on detecting suspicious activity after an incident or monitoring a single layer of the technology stack.
WhiteMatter instead wants to establish a common governance infrastructure through which enterprise AI agents operate.
Enterprise Customers and Reported Growth
The company says its platform is already deployed at organizations including Sentara Health, Alter Domus, New American Funding and Concentrix, alongside multiple Fortune 500 businesses.
Its customer examples highlight two different applications of agent security.
At Sentara Health, which employs approximately 35,000 people, the platform is being used to improve visibility into AI adoption, protect sensitive information and guide employees toward appropriate AI tools.
Alter Domus, meanwhile, is using WhiteMatter as part of its cyber defense infrastructure. The company’s CTO, Demetry Zilberg, described a production environment involving industrialized AI agents, where governance and policy enforcement need to operate alongside automation.
WhiteMatter reports that its customer base tripled during 2026, revenue increased fivefold over six months, net revenue retention reached approximately 200%, and customer churn remained at zero. These are company-reported business metrics rather than independently audited growth figures.
The company also reports more than 2.25 million agents under management, with a projection exceeding 5.5 million by year-end. It says its platform has governed 116 billion AI interactions and saved customers more than 11 trillion tokens over nine months.
These figures indicate the scale WhiteMatter is targeting, although the company has not independently substantiated the underlying methodology in its announcement.
AI Agent Security Is Becoming a Distinct Enterprise Technology Market
WhiteMatter’s funding arrives as AI agent governance becomes a more prominent cybersecurity priority.
The Cloud Security Alliance reported in April 2026 that 82% of surveyed enterprises had unknown AI agents operating in their environments. Its research also found that 65% had experienced AI agent-related incidents during the preceding 12 months.
This creates opportunities for security providers developing agent discovery, identity management, runtime authorization, monitoring and policy enforcement capabilities.
The competitive landscape includes emerging specialists such as Zenity, Noma Security and Onyx Security, alongside established cybersecurity providers expanding their AI security portfolios. The market is still developing, and enterprises are likely to combine multiple security layers rather than rely on a single platform.
WhiteMatter’s Guardian architecture is an attempt to differentiate through automated, agent-specific enforcement rather than conventional centralized monitoring alone.
Funding Supports the Next Phase of Agentic AI Infrastructure
Blumberg Capital led the $20 million investment, with Crew Capital and Sprout & Oak participating. WhiteMatter plans to use the capital to expand engineering research, product development, commercial operations and its executive team.
The company’s longer-term ambition is to become an operational and security layer for AI wherever agents are developed, deployed and used.
The underlying opportunity extends beyond preventing security incidents. Enterprises also need to understand what their agents are doing, which resources they consume, whether their actions follow organizational policies and how their performance translates into business value.
As autonomous AI moves deeper into enterprise operations, security will increasingly need to function as part of the infrastructure supporting agent deployment rather than as a separate monitoring activity.
WhiteMatter’s funding highlights growing investor interest in that emerging category: security platforms built specifically for organizations operating with increasingly autonomous digital workforces.
Market Landscape
The AI agent security market is emerging as a distinct segment of enterprise cybersecurity. As organizations deploy autonomous systems across business applications, traditional identity management, endpoint protection and cloud security tools need additional capabilities to manage machine-driven activity.
Four developments are shaping this market:
- AI agent governance: Enterprises need centralized inventories, identity controls, permissions and lifecycle management for autonomous agents.
- Runtime AI security: Security platforms are introducing real-time controls to prevent unauthorized actions, data exposure and misuse of connected tools.
- MCP and integration security: The expansion of MCP servers and third-party AI integrations increases the importance of securing connections between models, agents and enterprise applications.
- AI infrastructure optimization: Token consumption, model selection, agent traffic and operational costs are becoming additional management priorities.
WhiteMatter competes in this emerging category alongside Zenity, Noma Security and Onyx Security. Its Guardian architecture provides a distinctive approach through dedicated AI agents that enforce policies for other agents.
However, its long-term differentiation will depend on integration coverage, policy accuracy, deployment scalability and its ability to demonstrate measurable security outcomes.
Top Insights
- WhiteMatter secured $20 million in Series A funding to expand its enterprise AI agent security and governance platform.
- Its Guardian architecture deploys dedicated security agents to enforce policies across autonomous AI agents and their activities.
- The platform combines pre-deployment validation, runtime protection, agent discovery and centralized monitoring across enterprise environments.
- WhiteMatter reports managing 2.25 million agents and governing 116 billion AI interactions across customer environments.
- Gartner forecasts significant enterprise agent growth, increasing demand for AI-native security, governance and operational control infrastructure.
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