White Circle Raises $11M for AI Control Platform to Monitor

Paris-based cybersecurity startup White Circle raised $11 million in seed funding to develop its AI control platform that monitors model behavior and prevents security risks. According to SecurityWeek, the 2025-founded company built a unified platform for AI safety, evaluation, and optimization that scans all AI inputs and outputs for threats.

The funding round included multiple angel investors, though specific names were not disclosed. White Circle plans to use the capital to accelerate product development, expand hiring across the US, UK, and Europe, and grow its customer base.

AI Control Platform Addresses Enterprise Security Gaps

White Circle’s platform functions as a control layer that monitors AI systems for multiple risk categories. The solution identifies hallucinations, harmful content, prompt injection attacks, abuse, and model drift through organization-defined policy scans.

The platform can detect sensitive customer data leakage, block AI agents from performing malicious actions, flag and ban abusive users, and identify model failures. Security teams can enforce policies and monitor AI model behavior in real-time.

“Until now there’s not been a platform purpose-built to monitor AI’s behavior, catch it when it goes wrong, or shape how it acts,” White Circle founder and CEO Denis Shilov told SecurityWeek. “With White Circle, we’re finally giving companies everything they need to hold their AI accountable and optimize their models in a single place.”

The platform supports 150 languages and can be deployed across global products. White Circle claims its AI models improve accuracy over time through continuous learning.

Enterprise AI Infrastructure Faces Utilization Crisis

The AI control platform launch comes as enterprises struggle with massive infrastructure investments that remain underutilized. VentureBeat reported that average GPU utilization in enterprise environments sits at just 5%, despite Gartner estimating $401 billion in new AI infrastructure spending this year.

Many organizations locked in GPU capacity under three- to five-year depreciation cycles during the “GPU scramble” of 2024-2025. These assets are now fixed costs regardless of actual usage, forcing a shift from acquiring capacity to maximizing economic output from existing deployments.

The utilization problem stems from a self-reinforcing procurement loop that makes idle GPUs difficult to release. As these assets age, enterprises face pressure to generate measurable returns from depreciating infrastructure investments.

Agentic AI Drives Infrastructure Complexity

The rise of autonomous AI agents is creating new operational demands for enterprise infrastructure. Nutanix executives told VentureBeat that agentic systems introduce multi-step workflows across applications and data sources with unprecedented autonomy levels.

“It’s one thing to do an experiment, to do a prototype. It’s a different thing to take that prototype and deploy it for 10,000 employees,” said Thomas Cornely, EVP of product management at Nutanix. “We went from people focusing on training models to chatbots to now doing agents, where the demand and pressures on AI infrastructure are growing exponentially.”

Enterprises must now manage multiple agents running simultaneously, unpredictable real-time workloads, and coordinated infrastructure access across teams. This complexity gap between experimentation and production deployment is driving demand for specialized control platforms.

NVIDIA and ServiceNow Expand Enterprise AI Partnership

Major technology providers are responding to enterprise AI complexity with new partnerships. NVIDIA announced an expanded collaboration with ServiceNow at Knowledge 2026, delivering specialized autonomous AI agents for enterprise environments.

ServiceNow introduced Project Arc, a self-evolving autonomous desktop agent for knowledge workers, developers, IT teams, and administrators. Unlike standalone AI agents, Project Arc connects to the ServiceNow AI Platform through Action Fabric to provide governance, auditability, and workflow intelligence.

The partnership combines NVIDIA accelerated computing and open models with ServiceNow’s enterprise workflow context and AI Control Tower governance. This integration addresses enterprise needs for AI systems that operate with context, control, and consistency across real workflows.

Regulatory Pressure Mounts on Cloud Infrastructure

Enterprise AI deployment is also facing regulatory scrutiny in key markets. CNBC reported that the European Commission is considering rules to restrict US cloud platforms from processing sensitive government data across EU countries.

The Commission plans to present its “Tech Sovereignty Package” on May 27, responding to calls for Europe’s critical workloads to move away from US cloud providers that dominate the European market. This regulatory pressure could accelerate demand for on-premises AI control solutions and European-based alternatives.

The proposed restrictions reflect broader geopolitical tensions around data sovereignty and technology independence, potentially reshaping enterprise AI deployment strategies in regulated industries.

What This Means

White Circle’s funding represents growing investor confidence in AI governance and control solutions as enterprises move beyond experimentation to production deployment. The company’s focus on unified monitoring, policy enforcement, and risk detection addresses critical gaps in enterprise AI security infrastructure.

The convergence of underutilized GPU investments, complex agentic AI systems, and regulatory pressure creates a compelling market opportunity for specialized control platforms. Organizations need tools that maximize returns from existing infrastructure while ensuring compliance and security across autonomous AI deployments.

White Circle’s multi-language support and policy-driven approach position the platform to serve global enterprises navigating the transition from AI pilots to scaled production systems. The company’s timing aligns with enterprise recognition that AI governance is essential for realizing returns on massive infrastructure investments.

FAQ

What does White Circle’s AI control platform do?
White Circle’s platform monitors all AI inputs and outputs to identify risks including hallucinations, harmful content, prompt injection attacks, and model drift. It enforces organization-defined policies, detects data leakage, and can block malicious AI agent actions while supporting 150 languages.

Why is enterprise GPU utilization so low?
Average enterprise GPU utilization sits at 5% despite $401 billion in AI infrastructure spending this year. Organizations over-provisioned capacity during the “GPU scramble” and now face fixed costs from three- to five-year depreciation cycles, creating a self-reinforcing loop of idle resources.

How do autonomous AI agents change enterprise infrastructure needs?
Agentic AI introduces multi-step workflows across applications with unprecedented autonomy levels. Enterprises must manage multiple simultaneous agents, unpredictable real-time workloads, and coordinated infrastructure access, creating complexity that exceeds traditional AI model deployment requirements.