The Impact Of Generative Artificial Intelligence On Internal Auditing: A Conceptual Literature-Based Analysis

Generative Artificial Intelligence (GenAI) is rapidly reshaping auditing practices by extending artificial intelligence capabilities from automation and prediction toward content generation, scenario building, and decision support. Building on a conceptual and literature-based research design, this paper synthesizes recent academic studies, professional reports, and regulatory perspectives to examine how GenAI, particularly large language models such as ChatGPT, is influencing internal auditing. Drawing on illustrative use cases documented in prior research, the paper analyses how GenAI can enhance audit planning, risk assessment, testing, reporting, and continuous auditing, while simultaneously introducing new governance, ethical, and professional risks. The findings indicate that GenAI is more likely to augment rather than replace internal auditors, shifting their role toward higher-value analytical and advisory activities. At the same time, concerns related to data privacy, hallucinations, explainability, and professional judgment necessitate robust governance frameworks and competency development. The paper contributes by integrating fragmented insights into a coherent analytical structure that supports both researchers and internal audit practitioners.