Issues and future prospects of artificial intelligence adoption in public sector auditing under global digital transformation

Abstract

This article provides a comprehensive analysis of the challenges and prospects of introducing artificial intelligence (AI) into the public audit system in the context of global digital transformation. The study substantiates the limitations of traditional selective audit approaches under conditions of increasing data dependency and demonstrates the strategic role of AI in processing big data, early risk detection, anomaly identification, forecasting, and continuous monitoring, thereby enabling the transition toward a proactive and results-oriented audit model. Based on international experience (EU, USA, United Kingdom, Singapore, Estonia, China), the effectiveness of AI implementation is shown to depend directly on institutional conditions such as data infrastructure, governance standards, legal frameworks, and secure analytical environments. In Kazakhstan, indicators of the Supreme Audit Chamber for 2023–2025 reveal a significant increase in financial violations, reinforcing the objective and urgent need to introduce intelligent analytics for working with large volumes of data. The article proposes a four-layer methodological model for AI integration, consisting of the data layer, model layer, audit evidence layer, and governance-monitoring layer. It is concluded that the implementation of AI represents not merely technological modernization but a comprehensive transformation that systematically strengthens the legal, human resource, institutional, and managerial foundations of public audit.