White Circle provides a real-time AI governance and security platform that lets companies define, enforce, and monitor behavioral policies across both users and AI models. The platform operates model-agnostically, enabling organizations to control inputs, outputs, and tool usage while adapting rules to regional jurisdictions and continuously analyzing risk patterns.
Funding
Funding not disclosed
Founders
Product
Problem
AI-powered applications face growing risks from malicious user inputs, model hallucinations, and attempts to override system instructions, yet traditional moderation tools are too slow and rigid to keep pace with evolving threats. Companies struggle to enforce safety, security, and compliance policies consistently across millions of sessions without adding noticeable latency or breaking the experience for legitimate users.
Solution
White Circle provides a unified control layer that enforces behavioral policies across the entire AI lifecycle, covering user inputs, model outputs, and tool usage in real time. The platform lets organizations define custom policies, automatically block or rewrite risky behavior, and escalate threats before they reach production systems. It operates independently of underlying model providers, so companies can switch models or vendors without rebuilding their supervision infrastructure. White Circle includes backtesting tools that allow teams to test new policies against historical traffic before deployment, and it provides analytics for tracking user sentiment, failure patterns, and friction points. The system is designed for very low latency, making it effectively invisible to normal users while still catching sophisticated attacks.
Target Audience
Primary customers are AI-first companies and enterprises running generative AI applications in production, particularly those with large user bases and high-volume conversational interfaces that need real-time safety, security, and compliance controls.
Features
- Real-time policy enforcement across user inputs, model outputs, and tool usage with automatic blocking, rewriting, or escalation of risky behavior
- Model-agnostic architecture that works independently from underlying AI providers, enabling seamless model switching without rebuilding supervision layers
- Backtesting tools that let teams validate new policies against historical traffic before pushing to production
- Region-adaptive policy application that adjusts rules based on user location or jurisdiction
- User risk scoring that builds persistent profiles to track repeat offenders across sessions
- Custom moderation policies scoped per feature, use case, event type, or user segment
- Audit trail showing what happened, why it happened, and which policies were triggered
- Analytics dashboard for monitoring user sentiment, failure patterns, and common friction points