Abstract As organizations increasingly rely on outputs from externally operated large language models and other generative AI systems, a new governance gap has emerged. Decisions, disclosures, and representations are being influenced by AI-mediated outputs that are produced outside the organization’s control, are transient in nature, and often leave no durable, reconstructable record once relied upon. Existing approaches to AI governance, logging, and observability focus primarily on system execution and internal model behavior, leaving organizational reliance on external AI outputs largely unaddressed. This paper introduces the AIVO Standard, an external AI reliance evidence standard designed to govern how organizations authorize, document, and defend reliance on AI-mediated representations generated by third-party AI systems. The AIVO Standard does not sit in the inference path, does not control or evaluate model behavior, and does not record model reasoning or internal decision logic. Instead, it produces a time-indexed evidentiary reliance record that binds an external AI output to the organization’s governance state and authorization at the moment reliance occurred. The paper defines the structural distinction between technical system logs and evidentiary reliance records, describes the concept of the “reconstructability gap” created by evolving external models, and outlines how Evidence Packs function as durable legal and regulatory artifacts. The AIVO Standard is intended for legal, risk, compliance, and audit functions seeking defensible records of AI reliance in regulated and high-accountability contexts.
Standard AIVO (Wed,) studied this question.