The rapid advancement of Generative Artificial Intelligence (GenAI) has attracted considerable attention in industry and academia due to its transformative potential to automate the generation of text and multimedia content. However, concerns have emerged about privacy and information security implications in industrial settings, where sensitive data and intellectual property are essential assets.
While prior works have examined GenAI or information security in isolation, few have systematically analysed how GenAI is being integrated into privacy-preserving and security-enhancing practices in industrial sectors. By conducting a bibliometric review, this study fills this gap and provides a comprehensive mapping of how GenAI relates to information security and privacy issues in industry.
Leveraging a structured methodological framework, the bibliometric analysis conducted follows a two-phase approach to analyse a sample of 426 papers collected from Scopus and Web of Science (WoS): a preliminary analysis to assess publication trends, disciplinary contributions, document types, and geographic distributions; and a co-occurrence analysis, conducted using VOSviewer, to uncover thematic clusters. The bibliometric analysis identifies five dominant thematic clusters: (i) Generative Adversarial Networks (GANs) and Cybersecurity, focusing on adversarial attacks and industrial system protection; (ii) Industrial Cyber Protection, addressing federated learning and critical infrastructure defence; (iii) Disruptive GenAI, emphasizing Large Language Models and real-time automation; (iv) Privacy Preservation, covering anonymization and synthetic data generation; and (v) Ethical AI, exploring societal and governance implications. Overall, the study reveals critical research gaps such as the underrepresentation of medicine and energy in research landscape, limited decision sciences attention, regional asymmetries, and a lack of ethical discourse, which give rise to a forward-looking research agenda. This agenda advocates for interdisciplinary integration, regulatory alignment, and the development of context-sensitive and ethically robust GenAI systems in industry.
This study offers both theoretical contributions, advancing a more holistic understanding of GenAI’s role in information security, and practical implications aimed at ensuring the safe, effective, and socially responsible deployment of this technology.