Mapping the fragmented landscape of privacy in the age of generative AI: a bibliometric analysis and future research agenda

Purpose This study aims to examine the evolution of privacy research in artificial intelligence (AI) and generative artificial intelligence (GenAI).This study investigates how privacy has been conceptualized across technical, organizational, behavioral, ethical and governance perspectives and identifies key gaps and emerging challenges within the literature. Design/methodology/approach A bibliometric-supported systematic literature review was conducted using publications indexed in Scopus and Web of Science between 2021 and 2025. The analysis combines performance analysis with science-mapping techniques, including co-word analysis, thematic evolution analysis and bibliographic coupling to examine the intellectual structure and development of privacy research in AI and GenAI. Findings The findings reveal that privacy scholarship remains fragmented across technical, organizational, behavioral, ethical and governance domains. Privacy has evolved from a focus on data protection toward a broader concern involving trust, transparency, accountability, governance and model-level vulnerabilities. The review further shows that Generative AI has intensified existing privacy concerns while introducing new risks associated with content generation, inference capabilities, synthetic identities and large language models. The results also indicate a growing gap between the pace of AI innovation and the development of privacy-related scholarship. Practical implications The findings provide guidance for managers, policymakers and technology developers seeking to address emerging privacy risks and governance challenges in AI and GenAI environments. Social implications The study underscores the societal importance of addressing privacy risks associated with AI systems, revealing the need for responsible design, regulation and transparency to safeguard users and reduce digital inequalities. Originality/value This study adds value by providing an integrated understanding of privacy in AI and Generative AI and by revealing structural gaps, conceptual fragmentation and emerging privacy challenges that are not readily visible in individual studies. The findings contribute to the development of a more comprehensive perspective on privacy as a multidimensional issue involving technical, organizational, behavioral, ethical and governance considerations.