Insurance of Agentic AI
- URL: http://arxiv.org/abs/2606.05449v1
- Date: Wed, 03 Jun 2026 21:10:38 GMT
- Title: Insurance of Agentic AI
- Abstract summary: Agentic artificial intelligence (AI) systems are transforming the risk landscape by extending beyond information generation to autonomous planning.<n>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.<n>This paper examines the emerging insurance market for agentic AI and develops a framework for understanding its underwriting, pricing, reinsurance, and product-design implications.
- Score: 20.036142577511455
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: 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.
Related papers
- AI-Native Insurance for Agentic AI: Pricing, Underwriting, and End-to-End Automation [20.036142577511455]
This paper develops an AI-native mathematical framework for underwriting, pricing, and contract design for agentic AI deployments.<n>A healthcare case study illustrates contract optimization, sensitivity analysis, and automated claims processing for agentic AI systems.
arXiv Detail & Related papers (2026-07-14T19:46:21Z) - From Frontier to Shadow AI: A Simmering Threat to Assurance and Security in Critical Infrastructure [8.167474882691755]
We present the first empirical study of shadow AI in critical infrastructure environments.<n>We analyse how shadow AI manifests in practice, how it interacts with existing technical and governance controls, and the resulting security, assurance, and compliance risks.
arXiv Detail & Related papers (2026-05-23T07:21:08Z) - Towards trustworthy agentic AI: a comprehensive survey of safety, robustness, privacy, and system security [57.35851886874902]
Agentic AI systems execute complex tasks autonomously, but their multi-step trajectories introduce new failure modes that challenge trustworthiness.<n>This survey provides a focused examination of trustworthy agentic AI through two core dimensions that are critical for high-risk deployments.<n>For each dimension, we clarify key concepts, identify where risks emerge along the agent workflow, and summarize stage-targeted mitigation strategies.
arXiv Detail & Related papers (2026-05-17T10:26:37Z) - Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses [168.50301366360344]
Embodied Artificial Intelligence (Embodied AI) integrates perception, cognition, planning, and interaction into agents that operate in safety-critical environments.<n>Unlike digital AI systems, embodied agents must act under uncertain sensing, incomplete knowledge, and dynamic human-robot interactions.<n>This survey provides a comprehensive review of safety research in embodied AI, examining attacks and defenses across the full embodied pipeline.
arXiv Detail & Related papers (2026-03-28T13:21:44Z) - A Survey of Agentic AI and Cybersecurity: Challenges, Opportunities and Use-case Prototypes [7.02443431688472]
Agentic AI marks an important transition from single-step generative models to systems capable of reasoning, planning, acting, and adapting over long-lasting tasks.<n>This survey examines the implications of agentic AI for cybersecurity.
arXiv Detail & Related papers (2026-01-08T02:46:06Z) - A cybersecurity AI agent selection and decision support framework [0.0]
This paper presents a novel, structured decision support framework that aligns AI agent architectures, reactive, cognitive, hybrid, and learning.<n>By integrating agent theory with industry guidelines, this framework provides a transparent and stepwise methodology for selecting and deploying AI solutions.
arXiv Detail & Related papers (2025-10-02T07:38:21Z) - Rethinking Autonomy: Preventing Failures in AI-Driven Software Engineering [1.6766200616088744]
SAFE-AI Framework is a holistic approach emphasizing Safety, Auditability, Feedback, and Explainability.<n>We introduce a novel taxonomy of AI behaviors categorizing suggestive, generative, autonomous, and destructive actions to guide risk assessment and oversight.<n>This paper provides a roadmap for responsible AI integration in software engineering, aligning with emerging regulations like the EU AI Act and Canada's AIDA.
arXiv Detail & Related papers (2025-08-15T22:13:54Z) - Rethinking Data Protection in the (Generative) Artificial Intelligence Era [138.07763415496288]
We propose a four-level taxonomy that captures the diverse protection needs arising in modern (generative) AI models and systems.<n>Our framework offers a structured understanding of the trade-offs between data utility and control, spanning the entire AI pipeline.
arXiv Detail & Related papers (2025-07-03T02:45:51Z) - AILuminate: Introducing v1.0 of the AI Risk and Reliability Benchmark from MLCommons [62.374792825813394]
This paper introduces AILuminate v1.0, the first comprehensive industry-standard benchmark for assessing AI-product risk and reliability.<n>The benchmark evaluates an AI system's resistance to prompts designed to elicit dangerous, illegal, or undesirable behavior in 12 hazard categories.
arXiv Detail & Related papers (2025-02-19T05:58:52Z) - Position: Mind the Gap-the Growing Disconnect Between Established Vulnerability Disclosure and AI Security [56.219994752894294]
We argue that adapting existing processes for AI security reporting is doomed to fail due to fundamental shortcomings for the distinctive characteristics of AI systems.<n>Based on our proposal to address these shortcomings, we discuss an approach to AI security reporting and how the new AI paradigm, AI agents, will further reinforce the need for specialized AI security incident reporting advancements.
arXiv Detail & Related papers (2024-12-19T13:50:26Z) - EARBench: Towards Evaluating Physical Risk Awareness for Task Planning of Foundation Model-based Embodied AI Agents [53.717918131568936]
Embodied artificial intelligence (EAI) integrates advanced AI models into physical entities for real-world interaction.<n>Foundation models as the "brain" of EAI agents for high-level task planning have shown promising results.<n>However, the deployment of these agents in physical environments presents significant safety challenges.<n>This study introduces EARBench, a novel framework for automated physical risk assessment in EAI scenarios.
arXiv Detail & Related papers (2024-08-08T13:19:37Z) - AI Liability Insurance With an Example in AI-Powered E-diagnosis System [22.102728605081534]
We use an AI-powered E-diagnosis system as an example to study AI liability insurance.
We show that AI liability insurance can act as a regulatory mechanism to incentivize compliant behaviors and serve as a certificate of high-quality AI systems.
arXiv Detail & Related papers (2023-06-01T21:03:47Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.