A ZERO TRUST–BASED OPERATIONAL DEPLOYMENT ROADMAP FOR RESPONSIBLE AND SECURE AI INTEGRATION

Abstract

Generative Artificial Intelligence (AI) has the potential to transform decision making in multiple industries, but requires to be securely, ethically and trust-centric frameworks in order to ensure it is used or deployed responsibly. This paper proposes a Zero Trust-Based Operational Implementation Framework for safe and responsible integration of AI that considers aspects of accountability and resilience in Generative AI to mitigate significant risks across sectors. This research addresses a persistent need for transparency, auditability, and ethical projected governance in AI-based decision making. The research framework is intended to present a grounded operational governance framework for deploying and governance of AI that is consistent with Zero Trust principles: continuous verification, human in the loop (HITL) and guarding. The research is qualitative in nature and is primarily reliant on secondary research of examples of AI in academic journals and institutions, recognizing that the examples reflect on protocols. The findings indicate that there is significantly less risk of misuse where human accountability and verifiable trust is integrated into Zero Trust controls at the identity, data, inference and action borders. Human accountability and verifiable trust would therefore greatly influence the sustainable governance of AI. The hierarchy of risk by supervision of the integration of AI is regarding levels of automations or not, but but supervision verification and trust-based protocols, thus ensuring safe, secure and ethical integration of AI technologies in critical settings.