This category gathers foundational reference articles on the insurability of AI systems in production. It defines how generative and agentic systems enter insurance contracts, how evidence can be produced over time, and where coverage stops.
The series distinguishes Silent AI from dedicated insurance products, static audit from continuous behavioral evidence, and measurement signals from insurance verdicts. It is complementary to the AI Governance Reference corpus: metrology documents observable behavior; this series examines what that documentation can, and cannot, support in underwriting, claims, and cover design. NeoMundi measures. It does not insure, price, or decide liability.
Introduction Insurance can transfer a risk only when that risk can be described, bounded, and evidenced. Many AI systems in production fail this test. They change after the contract is signed. Their failures are often distributed across a model provider, an integrator, a prompt, a knowledge base, and a human workflow. The resulting loss may […]
Introduction For several years, losses linked to AI systems have been absorbed by enterprise policies that were neither designed nor priced for that exposure. The mechanism is not new. It reproduces, in another technological cycle, what the market already observed with Silent Cyber. Silent AI names that situation: a generative or agentic risk that sits
