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Why evidentiary breakdown, not hallucination, is the dominant governance risk Abstract This article sets out a taxonomy of empirically observed failure modes in AI-generated corporate narratives, derived from controlled, repeatable testing across multiple large language models. The taxonomy does not rely on anecdotal incidents, post-hoc reconstruction, or hypothetical

Why defensibility turns on evidence continuity, not model accuracy Abstract This article examines reconstructability as a threshold condition in AI-mediated enterprise contexts. It does not argue that courts, regulators, or enterprises require AI outputs to be accurate, explainable, or deterministically reproducible in all cases. Instead, it isolates a narrower

Recent reporting has surfaced concrete cases in which AI-generated representations influenced enterprise procurement judgment, with material consequences when those representations proved wrong. A recent article in Fortune describes how AI systems mischaracterized financial, governance, and safety attributes in enterprise decision contexts, triggering commercial and reputational fallout. The significance of

Control Summary for Boards, Audit Committees, and Investors What problem this control addresses As AI systems increasingly generate recommendations, explanations, summaries, and classifications in governed workflows, organizations face a growing evidentiary risk: they often cannot reconstruct what an AI system presented at the moment its output could have influenced a

A risk-modeling analysis of governance failure under evidentiary scrutiny Abstract This article does not argue that courts have resolved enterprise liability for third-party AI outputs. They have not. Instead, it models a set of emerging risk conditions under which enterprises face increased legal, regulatory, and reputational exposure when

An evidence-first approach to AI-mediated reliance Executive premise AI governance discussions still focus heavily on model quality, controls, and deployment risk. Those issues matter. But once AI-mediated statements influence disclosure, eligibility, pricing, health guidance, or internal conclusions, a narrower and distinct risk emerges: whether the organisation can

Why BIPA-style lawsuits expose an evidence gap, not a transcription problem The recent Illinois Biometric Information Privacy Act case against Fireflies.AI Corp has largely been framed as a biometric privacy dispute. That framing is legally correct but analytically incomplete. The more consequential issue is not voice recognition accuracy