Recent advances in consumer AI have led to the introduction of domain-specific systems designed to improve safety, privacy, and contextual relevance in sensitive areas such as healthcare. The launch of ChatGPT Health in January 2026 represents a significant and responsible step in this direction, introducing isolation, enhanced protections, and physician-informed evaluation for health-related AI interactions. This article argues that while such measures reduce the probability of harm, they do not resolve the governance challenge that emerges after reliance on AI-generated representations occurs. In regulatory, legal, and board-level scrutiny, the decisive question is not whether an AI output was accurate or well-intentioned, but whether organizations can reconstruct exactly what was shown, under what conditions, and on what basis at the moment decisions were shaped. Distinguishing between safety controls and accountability mechanisms, the article examines why evaluation frameworks, disclaimers, and privacy protections are insufficient to meet post-incident evidentiary requirements. It positions healthcare as the first visible domain where this gap has forced architectural change, and argues that similar pressures will extend to other regulated sectors. The article concludes that the next phase of AI governance will be defined not by improved answers, but by provable, reconstructable records of AI-mediated representations once scrutiny begins.