<![CDATA[This study aims to develop a conceptual framework of an Artificial Intelligence (AI)-based Audit Trail to strengthen accountability and transparency in corporate governance. Adopting the Design Science Research Methodology (DSRM), the research designs a framework consisting of four main layers: data acquisition, AI processing, governance & compliance, and reporting & accountability. Traditional audit trails are limited in anomaly detection, audit efficiency, and alignment with Good Corporate Governance (GCG) principles. By leveraging machine learning algorithms, natural language processing (NLP), and explainable AI (XAI), the proposed audit trail functions not merely as a passive log but as an intelligent system that actively supports decision-making. A case study in procurement fraud illustrates the potential of this framework to enhance internal audit effectiveness. The study contributes theoretical, practical, and regulatory insights, while acknowledging its limitation in excluding demonstration and evaluation stages within the current DSRM cycle.]]>
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