This article explores the potential of artificial intelligence (AI) in cybersecurity auditing and risk management within the context of ongoing digital transformation. Traditional approaches to information security auditing—based on manual data collection and periodic assessments—are increasingly insufficient for dynamic and large-scale digital ecosystems. They are limited in scalability, prone to human error, and lack the capacity for continuous monitoring. The integration of AI technologies allows for automated anomaly detection, proactive risk assessment, real-time decision support, and analysis of vast volumes of both structured and unstructured data, including event logs, network traffic, and audit reports. The study examines the application of machine learning and deep learning models in audit practices, including recurrent and convolutional neural networks, clustering algorithms, and natural language processing (NLP) techniques for detecting security policy violations. Particular attention is given to the concept of Network Situation Awareness, which enables the prediction of system behavior and potential threats based on historical and real-time behavioral data. In addition
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