Rythem Anderson-Seawell
Old Dominion University
Exploring the Intersection of AI and Cybercrime
One of the critical points of the International Journal of Cybersecurity Intelligence & Cybercrime is bringing forth several essential insights into the cybercrime arena. As Choi, Dearden, and Parti (2024) argued, artificial intelligence is one of the new inventions offenders employ to serve their criminal demands. This emphasizes the pressing need to comprehend how AI-related cybercrime develops so that adequate preventative measures can be applied, which is very underdeveloped.
The articles of this special issue have employed different methodological approaches. In this paper, Shetty, Choi, and Park (2024) use a complex mixed methods methodology that involves the quantitative analysis of 102 malicious AI prompts combined with expert interviews. They looked at their quantitative data collection and discovered that 62.7% of malicious AI prompts were used on dark web forums in 75.4% of the cases with Chat GPT. The most common cause people jailbroke ChatGPT was to use it to create malware (15.7%) and jailbreak it to use it for themselves (45.1%). This is complemented by the analysis of high-tech cyber victimization case studies in the healthcare industry with routine activities theory (RAT): ransomware (40.1%) was the most used attack method; financial gain (60.9%) was the main motive of cyberattacks within the healthcare industry.
Theoretically, these studies are instrumental in helping us explain the cybercrime dynamics across these studies. Smith (2024) describes the ‘Integrated Model of Cybercrime Dynamics (IMCD) as a newly theorized model of how individual traits, cybercrime activity, online behavior, and external influence of each other may interact. This synthetic model derives from concepts of psychology, criminology, and computer science. It comprises components of cybercrime determinants and end outcomes within a holistic conceptualization. Like most other theories, such as Routine Activities Theory (RAT) and Social Learning Theory (SLT), Smith’s framework includes personality traits, gratifications, social norms, online behaviors, guardianship factors, and cyber attacks. Similarly, Shetty, Choi, and Park (2024) apply the Cyber Routine Activities Theory (Cyber-RAT) to explain how AI technologies can worsen criminal dynamics by empowering offenders and enlarging the pool of potential targets.
These studies are concerning results regarding the facilitation of cybercrime. According to Praveen et al. (2024), the most apparent country of origin for cyber attacks on a healthcare institution is Russia (7.7%), and the most common target of a cyber attack is a healthcare institution (76.1%). Smith (2024) presents several testable propositions from the IMCD regarding the correlation between certain cybercrime offenses and deviant online subcultures, including hacking normalization, and between personality traits of impulsiveness and certain cybercrime offenses. Collectively, these findings help us gain some insights into the ecology of cybercrime, and papers on this special issue bring us further insight into cyber security threats and future research and policy development in cyber security.
Besides the theoretical contributions, the articles within the present special issue show the need for more integration among the academic community, the industry professional practice, and law enforcement agencies to thwart the power of AI in cyber criminality. However, as AI tools are becoming increasingly sophisticated and able to carry out malicious activities without supervision, there is an urgent need for a more vigorous defense against it. The artificial integration of criminal operations increases the pace of its execution and the complexity of detecting and preventing crimes. Praveen et al. (2024) give an example of how cybercriminals conduct cyberattacks on healthcare institutions by using AI and AI to take advantage of the sector-specific vulnerability of health institutions. Therefore, interdisciplinary approaches are needed along the lines of criminological theories, technological innovation, and law enforcement programs possessing elements of artificial intelligence (AI) cybercrime. This inherent response to the work of these two will reinforce resilience and form a stronger, more proactive cybersecurity ecosystem capable of answering other today’s and tomorrow’s threats.
References
Choi, S., Dearden, T., & Parti, K. (2024). Understanding the Use of Artificial Intelligence in Cybercrime. International Journal of Cybersecurity Intelligence & Cybercrime, 7(2). https://doi.org/10.52306/2578-3289.1185
Shetty, S., Choi, K.-S., & Park, I. (2024). Investigating the Intersection of AI and Cybercrime: Risks, Trends, and Countermeasures. International Journal of Cybersecurity Intelligence & Cybercrime, 7(2). https://doi.org/10.52306/2578-3289.1187
Smith, T. (2024). Integrated Model of Cybercrime Dynamics: a Comprehensive Framework for Understanding Offending and Victimization in the Digital Realm. International Journal of Cybersecurity Intelligence & Cybercrime, 7(2). https://doi.org/10.52306/2578-3289.1163
Yashna Praveen, Kim, M., & Choi, K.-S. (2024). Cyber Victimization in the Healthcare Industry: Analyzing Offender Motivations and Target Characteristics through Routine Activities Theory (RAT) and Cyber-Routine Activities Theory (Cyber-RAT). International Journal of Cybersecurity Intelligence and Cybercrime, 7(2). https://doi.org/10.52306/2578-3289.1186