A Framework for Auditable and Responsible AI-Driven Decision Making

Abstract

Enterprise analytics is rapidly moving from descriptive dashboards toward generative, predictive and agentic systems that can recommend or execute business decisions. This shift creates a governance problem that traditional model approval, data governance and access control mechanisms were not designed to solve: an AI decision can be influenced simultaneously by user intent, retrieved enterprise data, model behavior, prompts, tools, external context and prior agent actions. A system that was trusted yesterday may not be trustworthy for the next decision.

This article proposes a Zero-Trust AI Decision Governance (ZTAIDG) framework for enterprise analytics. Inspired by zero-trust security principles, the framework assumes no implicit trust in an AI model, agent, prompt, dataset, retrieved context, tool or prior decision. Each material decision must re-establish trust through six runtime gates covering identity and intent, data and context, model and tool trust, policy and risk, evidence and explainability, and action boundaries. The framework introduces an AI Decision Evidence Ledger (AIDEL), a Decision Trust Authorization Score (DTAS), risk-based autonomy levels and a closed-loop governance model that connects pre-decision authorization with post-decision outcome monitoring.

The objective is not to eliminate AI risk or require human approval for every analytical interaction. It is to make autonomy conditional, evidence-based, auditable and proportional to decision impact. The paper positions zero-trust AI governance as a runtime complement to established approaches such as NIST AI RMF, ISO/IEC 42001 and emerging regulatory requirements including the EU AI Act.