Regulatory-Compliant Explainable AI: Auditing Decision Processes in Enterprise IT

Abstract

The adoption of artificial intelligence (AI) in enterprise IT systems has resulted in the introduction of a significant difficulty related to transparency, accountability, and regulatory compliance. Furthermore, many AI-driven decision-making systems operate in a black box, making it difficult for most businesses to audit judgments, justify outcomes, and comply with rules.

The purpose of this paper is to offer a regulatory-compliant AI(XAI) framework for auditing in the decision-making process in the enterprise IT environment. The governance framework requires connectors that explain methodologies, audit logging, and even the governance workflow to ensure transparency and compliance. As a result, simulation-based experiments and real-time experiences give scenarios that demonstrate how the usage of XAI enables auditing while also giving trust, lowering regulatory risks, and promoting responsibility for AI implementations.

Therefore, presenting a framework that analyses through simulation-based experiments and real-time corporate IT scenarios provides a practical assessment for enhancing transparency, audit readiness, and even trustworthiness. The end outcome will be a well-demonstrated XAI that enables audits to improve significantly regulatory compliance and decision accountability without imposing prohibitively high operational overhead. In reality, bridging the gap between explainable AI methodologies and enterprise governance requirements will aid in the development of a practical foundation for a responsive and compliant AI that can be deployed in modern enterprise IT environments.