为自主智能系统设计新型保险框架,应对其带来的新型风险。
Insurance of Agentic AI

- 将自主智能分为不同等级,区分信息输出与主动引发风险的系统。
- 识别出幻觉、提示注入、模型漂移等六大核心风险路径。
- 建议构建多险种协同的保险生态,需透明数据与监管支持。
自主智能(Agentic AI)系统正突破信息生成范畴,具备自主规划、调用工具、执行决策及持续修改数字与物理环境的能力,带来传统保险类别无法覆盖的新风险。本文分析了新兴的自主智能保险市场,提出理解承保、定价、再保险与产品设计的框架。将自主智能视为自治与授权的连续体,强调信息输出与能通过外部行为独立生成可保事件系统的区别。分析了包括幻觉、提示注入攻击、自主决策错误、模型漂移、依赖失效和网络物理危害在内的主要风险路径,并评估现有保险产品如何适应。论文进一步提出基于暴露评估、情景分析、依赖关系映射和累积风险管控的精算框架,类比网络安全保险的发展历程。最后,提出一种协调保险架构,通过明确分配机制与专用AI聚合,整合网络安全、技术过失、产品责任、性能保证及正面AI责任等覆盖范围。研究表明,未来自主智能保险不在于单一险种,而在于由改进治理、透明度、遥测数据和监管清晰性支撑的分层互补保障体系。
原文摘要 · Abstract (English)
Agentic artificial intelligence (AI) systems are transforming the risk landscape by extending beyond information generation to autonomous planning, tool invocation, decision execution, and persistent modification of digital and physical environments. These capabilities introduce novel exposures that do not fit neatly within traditional insurance categories such as cyber, professional liability, product liability, or directors and officers coverage. This paper examines the emerging insurance market for agentic AI and develops a framework for understanding its underwriting, pricing, reinsurance, and product-design implications. We characterize agentic AI as a continuum of autonomy and delegated authority, emphasizing the distinction between informational outputs and systems capable of independently generating insured events through external actions. We analyze major risk pathways, including hallucinations, prompt-injection attacks, autonomous decision errors, model drift, dependency failures, and cyber-physical harms, and evaluate how existing insurance products are adapting to address these exposures. The paper further proposes an actuarial framework based on exposure assessment, scenario analysis, dependency mapping, and accumulation-risk management, drawing parallels to the evolution of cyber insurance. Finally, we present a coordinated insurance architecture that integrates cyber, technology errors and omissions, product liability, performance-warranty, and affirmative AI-liability coverages through explicit allocation mechanisms and dedicated AI aggregates. The analysis suggests that the future of agentic-AI insurance lies not in a single monoline product but in a layered ecosystem of complementary coverages supported by improved governance, transparency, telemetry, and regulatory clarity.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品;中文卡片由大模型生成,请以原文为准。