Artificial Insurance: Exposing the Coverage, Controls, and Measurement Gaps of Insurance for AI Risks

This position paper argues that current insurance markets are fundamentally misaligned with AI risk, creating significant coverage, control, and measurement gaps that threaten both organizations and insurers. Through analysis of insurance policy coverages, risk controls, and measurement approaches across 15 AI risk categories, we demonstrate that conventional insurance structures are inadequately addressing the unique challenges presented by AI systems. This misalignment stems from AI's autonomous nature, probabilistic operations, opacity, and rapid development cycles, which conflict with insurance assumptions about human control, causality, deterministic failures, and stable risk environments. While some argue that existing policies sufficiently cover AI risks, our evidence shows that even the most relevant cyber and technology liability insurance products leave organizations exposed to significant AI-specific harms. This qualitative analysis establishes a foundational framework for future quantitative studies and proposes measurement approaches that could enable more rigorous empirical analysis as AI incident data matures. Without deliberate evolution in AI risk transfer mechanisms, organizations face a protection gap while insurers confront potentially catastrophic unpriced exposure, creating an urgent need for market-based risk transfer solutions that can drive adoption of technical AI security and safety standards.