A mismatch exists between rapid enterprise adoption of artificial intelligence (AI) and the fragmented market for insuring AI-related losses. This report examines AI incidents, U.S. lawsuits, enacted state laws, and insurance filings to assess how insurers are responding through exclusions, endorsements, affirmative coverage, or silence.

The Insurability of Artificial Intelligence

Sasha Romanosky, Celine Robinson

ResearchPublished Sep 16, 2026

Businesses increasingly embed artificial intelligence (AI) in operations, products, and services, creating both new opportunities and new forms of risk. Firms are deploying AI in customer service, employment screening, coding, fraud detection, and autonomous systems. At the same time, AI-related harms are already emerging, including incorrect or misleading outputs, deepfakes, privacy violations, intellectual property disputes, fraud, product defects, and discriminatory decisions. These harms create demand for insurance but do not fit neatly within existing insurance lines.

In this report, the authors examine AI incidents, U.S. lawsuits, enacted state laws, and admitted-market insurance filings to assess how insurers are responding through exclusions, endorsements, affirmative coverage, or silence. Key findings include the dominance of misinformation incidents, concentration of litigation around intellectual property, and five mechanisms through which AI could generate accumulation losses across insurance lines.

Key Takeaways

  • Of public generative AI incidents, 84 percent relate to misinformation (false, deceptive, or manipulative content) and deepfakes (AI-generated or altered media impersonating a real person or event).
  • Of U.S. litigation for generative AI harms, 60 percent relate to the alleged violations of intellectual property or improper training by AI developers rather than to the use, misuse, or outputs of these models.
  • Most U.S. states have adopted laws focusing on restricting harmful synthetic content, such as nonconsensual intimate images and child sexual abuse material.
  • Unlike cyber, AI risks are not contained to a single insurance line but span many lines: technology errors and omissions and professional liability, cyber, directors and officers, commercial property, and more.
  • Insurance carriers are responding to AI risks unevenly, with some offering affirmative coverage for certain risks (e.g., model errors), and others filing broad exclusions for any AI-related loss.
  • Most carriers are remaining silent, neither affirming nor excluding AI-related risks in their policies, leaving coverage for such losses untested and open to dispute.

Recommendations

  • State insurance regulators and the National Association of Insurance Commissioners, supported by carriers and brokers, should develop an AI Coverage Notice enabling carriers to clarify how, and under which lines, AI-related losses are affirmatively covered, excluded, or left silent.
  • Incident reporting for attritional and accumulation loss should be standardized. Policy researchers and brokers, together with carriers and reinsurers, should develop a common, adaptable taxonomy for AI incidents, claims, and controls to properly track and report on AI risks. Actual incident data should remain private, however.
  • Insurers and reinsurers should analyze AI aggregation scenarios, focusing on shared model dependency, common infrastructure failure, and regulatory shock to manage correlated AI risk.

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Romanosky, Sasha and Celine Robinson, The Insurability of Artificial Intelligence. Santa Monica, CA: RAND Corporation, 2026. https://www.rand.org/pubs/research_reports/RRA5130-1.html.
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This work was conducted within the RAND Institute for Civil Justice and the RAND Feinberg Center for Catastrophic Risk Management and Compensation.

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