Exona Lab
Exona Lab provides an AI Risk Engine that helps (re)insurers and brokers price, underwrite, and monitor AI-related exposures. The platform combines dynamic risk-pricing models, silent AI exposure discovery within existing Cyber, Tech E&O, and PI policies, and custom stress-testing scenarios to quantify potential losses.
- Insurance Technology
- Artificial Intelligence
- Data & Analytics
- Software Only
- Financial Technology
Funding
Founders
Founder details are not available yet.
Product
Problem
AI is creating a new class of business risk for insurers that is fast-moving, hard to measure, and increasingly material. Traditional underwriting and risk assessment methods are not equipped to surface, quantify, or model these emerging AI-related exposures within existing policy portfolios.
Solution
Exona Lab builds the AI Risk Engine, a platform that helps (re)insurers and brokers price, underwrite, and monitor AI exposure. The engine measures and flags silent AI exposure hidden within existing Cyber, Tech E&O, and PI policies and portfolios, enabling carriers to understand and quantify these risks. It also provides proprietary, plug-in guardrails that reduce operational and safety risk, harden enterprise systems, and improve model reliability. Custom AI risk scenarios allow carriers to stress-test portfolios, monitor aggregate exposure, and quantify potential losses. The platform combines frontier AI safety research, industry-leading AI incident and litigation data monitoring, and deep actuarial expertise to give insurers the intelligence they need to act with confidence.
Target Audience
Primary customers are (re)insurers and brokers who need to price, underwrite, and monitor AI-related risks within their policy portfolios.
Features
- AI Risk Intelligence capabilities for pricing, underwriting, and monitoring AI exposure
- Silent AI Exposure Discovery to measure and flag hidden AI risk within existing Cyber, Tech E&O, and PI policies
- Scenarios & RDS for custom AI risk scenarios to stress-test portfolios, monitor aggregate exposure, and quantify potential losses
- Proprietary plug-in guardrails that reduce operational and safety risk while hardening enterprise systems
- Risk mitigation tools designed to improve AI model reliability
- Dynamic risk-pricing models that learn from real-world AI failures, near-miss data, and system behaviour