Deepfake technology has evolved astonishingly by applying artificial intelligence (AI) to inspire ultra-realistic audio and video content. Initially praised for its legitimate use cases in entertainment and education, deepfake technology has increasingly become a tool for organized fraud and other malicious purposes. This paper investigates the role of deepfake technology in enabling identity theft, financial fraud, and unlawful activities. By conducting a qualitative comparative analysis of three cases, this paper analyzes deepfakes' legal, security, and governance aspects, indicating that deepfakes have posed a massive threat at the national and global levels. Also, this research demonstrates how the current regulatory regimes cannot adequately mitigate these emerging threats. The results expose glaring deficiencies in accountability and enforcement, which are made even more glaring by the global character of the internet and the accelerative pace of technological innovation. This research provides implications of deepfake technology in organized fraud and offers policy recommendations to mitigate the threats and prevent misuse of deepfake technology in the future.

The rapid rise of AI-driven cybercrime and deepfake fraud poses complex organisational challenges for US law enforcement, particularly the Federal Bureau of Investigation (FBI). Applying Maguire’s (2003) police organisation theory, this qualitative single-case study analyses the FBI’s structure, culture, technological integration, and inter-agency collaboration. Findings underscore the organisational strengths of the FBI, including a specialised Cyber Division, advanced detection tools, and partnerships with agencies such as the Cybersecurity and Infrastructure Security Agency (CISA). However, constraints, such as resource limitations, detection inaccuracies, inter-agency rivalries, and ethical concerns, including privacy risks associated with AI surveillance, hinder operational effectiveness. Fragmented global legal frameworks, diverse national capacities, and inconsistent detection of advanced deepfakes further complicate responses to this issue. This study proposes the establishment of agile task forces, public–private partnerships, international cooperation protocols, and ethical AI frameworks to counter evolving threats, offering scalable policy and technological solutions for global law enforcement.

This article aims to conduct a preliminary comparative study on different models of citizens’ participation in Chinese societies. In examining citizen policing in Chinese societies, this article covers the major geographical locations in China, Hong Kong, and Taiwan. By using thematic analysis, this article has identified three main themes to assess volunteer policing in three Chinese societies, which all connect to the concepts of institutions and social capital: institutional development, institutional impact, and Guanxi as a common socio-cultural challenge. The role of citizens in law enforcement work is contextualized in the different forms of governments in these three societies. In China, there is a lack of legislature at the central level to regulate auxiliary police forces. The local governments developed various forms of auxiliary police systems monitored by public security authorities. Hong Kong, due to its former status as a British colony, has developed a well-established model of the modern auxiliary police system. In Taiwan, the auxiliary police system was influenced by both Chinese institutional development and the Japanese colony; however, a lack of detailed legislature at the local level is found, which has impacted the implementation of volunteer policing. Despite the difference, three of them all have faced the common challenges affected by Guanxi, which is the rudimentary dynamic in personalized social networks of power and is a crucial system of beliefs in Chinese culture. The author concludes that the institutional factors and Guanxi are important determinants of successful implementation of volunteer policing in three Chinese societies.

This chapter explores the integration of artificial intelligence (AI) in police education by examining the case of Taiwan's Central Police University (CPU). It focuses on the institution's top-down approach to implementing AI technologies within its curriculum and training programs. The findings indicate that as the landscape of law enforcement evolves with rapid technological advancements, CPU has aimed to modernise its educational offerings since 2023 to better prepare future police officers for contemporary challenges. The study highlights the crucial role of institutional leadership in guiding AI adoption among faculty and students, ensuring that educational strategies align with both technological relevance and traditional pedagogical values. By enhancing its capabilities through various projects, conferences, workshops, and guidelines, CPU sets a precedent for other law enforcement vocational education institutions seeking to incorporate AI into their training frameworks.