Artificial Intelligence and the Evolving Cybercrime Paradigm: Current Threats to Businesses

Document Type : Research Paper

Authors

1 Associate Professor, Universiti Kuala Lumpur (UniKL) Business School, Malaysia.

2 Universiti Kuala Lumpur, Business School, Malaysia.

10.22059/jitm.2024.99505

Abstract

This paper provides a comprehensive overview of the evolving Artificial Intelligence (AI) threat to cybersecurity, emphasizing the urgent need for finance leaders and cybersecurity professionals to adapt their strategies and controls to effectively combat AI-powered scams and cyber-attacks. The study delves into the specific ways in which AI is being used maliciously in cybercrime, such as enhanced phishing and Business Email Compromise (BEC) attacks, the creation of synthetic media including deepfakes, targeted attacks, automated attack strategies, and the availability of black-market AI tools on the dark web. Furthermore, it highlights the critical need for enhanced cybersecurity strategies and international cooperation to combat cyber threats effectively. The findings of this study provide valuable insights for finance leaders, cybersecurity professionals, policymakers, and researchers in understanding and addressing the challenges posed by generative AI in the cyber threat landscape.

Keywords

References

Beerman, J., Berent, D., Falter, Z., & Bhunia, S. (2023). A Review of Colonial Pipeline Ransomware Attack. Proceedings - 23rd IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing Workshops, CCGridW 2023, 8–15. https://doi.org/10.1109/CCGridW59191.2023.00017
Camacho, N. G. (2024). The Role of AI in Cybersecurity: Addressing Threats in the Digital Age. Journal of Artificial Intelligence General Science (JAIGS) ISSN:3006-4023, 3(1), 143–154. https://doi.org/10.60087/jaigs.v3i1.75
Chen, H., Chung, W., Xu, J. J., Wang, G., Qin, Y., & Chau, M. (2004). Crime data mining: a general framework and some examples. Computer, 37(4), 50–56.
Dion, M. (2011). Corruption, fraud and cybercrime as dehumanizing phenomena. International Journal of Social Economics, 38(5), 466–476. https://doi.org/10.1108/03068291111123156
Dwyer, A. C. (2023). Cybersecurity’s grammars: A more-than-human geopolitics of computation. Area, 55(1), 10–17. https://doi.org/10.1111/area.12728
Eftsure. (2024). Cybersecurity Guide for CFOs. 7th edition.
Gao, S., Xu, D., Wang, H., & Green, P. (2009). Knowledge-based anti-money laundering: a software agent bank application. Journal of Knowledge Management, 13(2), 63–75.
Guembe, B., Azeta, A., Misra, S., Osamor, V. C., Fernandez-Sanz, L., & Pospelova, V. (2022). The Emerging Threat of Ai-driven Cyber Attacks: A Review. In Applied Artificial Intelligence (Vol. 36, Issue 1). Taylor & Francis. https://doi.org/10.1080/08839514.2022.2037254
Iasiello, E. (2014). Is cyber deterrence an illusory course of action? Journal of Strategic Security, 7(1), 54–67. https://doi.org/10.5038/1944-0472.7.1.5
Kingdon, J. (2004). AI fights money laundering. Intelligent Systems, IEEE, 19(3), 87–89.
Lau, C. K., & Ooi, K. W. (2016). A case study on fraudulent financial reporting: Evidence from Malaysia. Accounting Research Journal, 29(1). https://doi.org/10.1108/ARJ-11-2013-0084
Manoharan, A., & Sarker, M. (2024). Revolutionizing Cybersecurity: Unleashing the Power of Artificial Intelligence and Machine Learning for Next-Generation Threat Detection. International Research Journal of Modernization in Engineering Technology and Science, March. https://doi.org/10.56726/irjmets32644
Moyes, G. D. (2007). Audit Techniques & Inventory Fraud Detection In Accounting Information Systems. Review of Business Information Systems (RBIS), 1(1), 63–76.
Ren, A. L. Y., Liang, C. T., Hyug, I. J., Brohi, S. N., & Jhanjhi, N. Z. (2020). A three-level ransomware detection and prevention mechanism. EAI Endorsed Transactions on Energy Web, 7(26), 1–7. https://doi.org/10.4108/eai.13-7-2018.162691
Satyapanich, T., Ferraro, F., & Finin, T. (2020). Casie: Extracting cybersecurity event information from text. AAAI 2020 - 34th AAAI Conference on Artificial Intelligence, 8749–8757. https://doi.org/10.1609/aaai.v34i05.6401
Slapničar, S., Vuko, T., Čular, M., & Drašček, M. (2022). Effectiveness of cybersecurity audit. International Journal of Accounting Information Systems, 44(January 2021). https://doi.org/10.1016/j.accinf.2021.100548
Teichmann, F. (2023). Ransomware attacks in the context of generative artificial intelligence—an experimental study. International Cybersecurity Law Review, 4(4), 399–414. https://doi.org/10.1365/s43439-023-00094-x
Turk, Ž., Sonkor, M. S., & Klinc, R. (2022). Cybersecurity assessment of bim/cde design environment using cyber assessment framework. Journal of Civil Engineering and Management, 28(5), 349–364. https://doi.org/10.3846/jcem.2022.16682
Valkenburg, B., & Bongiovanni, I. (2024). Unravelling the three lines model in cybersecurity: a systematic literature review. Computers and Security, 139(December 2023), 103708. https://doi.org/10.1016/j.cose.2024.103708
Yamin, M. M., Ullah, M., Ullah, H., & Katt, B. (2021). Weaponized AI for cyber attacks. Journal of Information Security and Applications, 57, 1–35. https://doi.org/10.1016/j.jisa.2020.102722
 
 
 
 
Journal of Information Technology Management
Volume 16, Issue 4
2024
Pages 162-170

Files

Share

How to cite

Statistics