A Zero Trust–Guided Safeguarding Framework for Generative AI Systems in High-Consequence Environments

Abstract

Generative AI is moving into everyday decision-making work in places like hospitals, trading floors, public service offices, and industrial monitoring rooms. It is no longer limited to drafting text or summarizing documents. In many cases, the output of a model becomes part of how a situation is understood, which can influence what a professional decides to do next. This influence is subtle. A system does not need to be wrong to shift judgment; it only needs to present information with a certain tone or emphasis. That is where risks begin to appear, especially when prompt history, retrieval sources, or context can be nudged in ways that are hard to detect afterward. Traditional cybersecurity controls focus on who is allowed to access a system and whether the system is functioning as intended. That is necessary, but in high-consequence environments, it is not enough. The question is whether the reasoning the model produces should be allowed to guide or inform action. This paper introduces the Zero Trust–Guided Generative AI Cybersecurity Safeguarding Maturity Model (ZT-GAI-CSMM), which applies continuous verification at the point where model output intersects with operational decisions. Case applications in finance, clinical care, and industrial control settings demonstrate how organizations can gain value from Generative AI while maintaining accountability and oversight.