New paper proposes insurance framework for agentic AI risks
Autonomous AI actions create novel exposures that traditional policies can't cover
Agentic AI systems are moving beyond generating information to autonomously planning, invoking tools, executing decisions, and persistently modifying digital and physical environments. This shift introduces novel risk exposures that traditional insurance categories—cyber, professional liability, product liability, and D&O—cannot adequately address. A new paper on arXiv by Quanyan Zhu, titled 'Insurance of Agentic AI,' provides a comprehensive framework for underwriting, pricing, reinsurance, and product design for this emerging class of risk.
The paper characterizes agentic AI as a continuum of autonomy and delegated authority. It analyzes key risk pathways including hallucinations, prompt-injection attacks, autonomous decision errors, model drift, dependency failures, and cyber-physical harms. Zhu proposes an actuarial framework based on exposure assessment, scenario analysis, dependency mapping, and accumulation-risk management. The conclusion suggests that the future of agentic AI insurance lies not in a single monoline product but in a layered ecosystem of complementary coverages—cyber, tech E&O, product liability, performance warranty, and affirmative AI liability—supported by improved governance, transparency, telemetry, and regulatory clarity.
- Agentic AI systems can autonomously generate insured events through external actions, unlike traditional AI that only produces information.
- Key risk pathways include hallucinations, prompt injection, decision errors, model drift, dependency failures, and cyber-physical harms.
- The paper proposes a layered insurance architecture integrating cyber, tech E&O, product liability, performance warranty, and affirmative AI liability coverages.
Why It Matters
As agentic AI deployment grows, proper insurance frameworks are critical to manage novel autonomous risks.