Authors : Sreenath P. U.; Dr. A. Senthil Kumar

Volume/Issue : Volume 11 - 2026, Issue 10 - October

Google Scholar : https://tinyurl.com/pmenhm98

DOI : https://doi.org/10.38124/ijisrt/26Oct1113

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Deposit Money Banks (DMBs) are embracing AI-powered intrusion and fraud detection, but there is no theory of adoption that asks if the AI is vulnerable to deception. Adversarial machine learning demonstrates how ‘tricky' a small, purposely made change to the input can cause a detector to mistake malicious activity for benign activity. The TOE framework is thus insufficiently specified for the process of adopting AI cybersecurity in high-risk industries, the argument of this paper is that this is the case. By combining three literatures, the paper spans the research domains of TOE adoption studies (2022-2026), adversarial machine learning in intrusion detection, and recent financial and AI regulations, such as the CBN (Central Bank of Nigeria) 2024 Risk Based Cybersecurity Framework and Article 15 of the EU AI Act. Building on this synthesis it builds an extended TOE model with a new construct, Adversarial Robustness Preparedness (ARP): an organisation's ability to anticipate, test, defend against and govern attacks to its AI model. The model proposes eight propositions connecting technological, organisational and environmental factors and ARP with the adoption of AI for cybersecurity in Nigerian DMBs. It points to ARP as a direct cause of adoption, a partial mediator of organisational and regulatory influences, and a moderator of the impact of perceived AI capability. A measurement and validation roadmap based on structural equation modelling is provided. In addition to a testable theoretical extension, the paper provides a vocabulary for bank boards and regulators and a research agenda for new and developing financial markets.

Keywords : AI-Driven Cybersecurity; Adversarial Machine Learning; Adversarial Robustness Preparedness; Technology Adoption; TOE Framework; Intrusion Detection; Deposit Money Banks; Nigeria.

References :

1. Achuthan, K., Ramanathan, S., Srinivas, S., & Raman, R. (2024). Advancing cybersecurity and privacy with artificial intelligence: Current trends and future research directions. Frontiers in Big Data, 7, Article 1497535. https://doi.org/10.3389/fdata.2024.1497535
2. Agbo, C. (2024, April 30). Financial institutions lost N17.6 billion to fraud in 2023 – Report [Reporting NIBSS, Annual fraud landscape, January–December 2023]. 21st Century Chronicle. https://21stcenturychronicle.com/financial-institutions-lost-n17-6-billion-to-fraud-in-2023-report/
3. Alaba, J. S., Ahmed, S. J., Farida, A. P., & Oluwatosin, O. V. (2025). Adoption of AI-driven fraud detection system in the Nigerian banking sector: An analysis of cost, compliance, and competency. Financial Economics Letters, 4(2), 40–53. https://doi.org/10.58567/fel04020004
4. Alotaibi, A., & Rassam, M. A. (2023). Adversarial machine learning attacks against intrusion detection systems: A survey on strategies and defense. Future Internet, 15(2), Article 62. https://doi.org/10.3390/fi15020062
5. Apruzzese, G., Anderson, H. S., Dambra, S., Freeman, D., Pierazzi, F., & Roundy, K. A. (2023). "Real attackers don't compute gradients": Bridging the gap between adversarial ML research and practice. In 2023 IEEE Conference on Secure and Trustworthy Machine Learning (SaTML). IEEE.
6. Badghish, S., & Soomro, Y. A. (2024). Artificial intelligence adoption by SMEs to achieve sustainable business performance: Application of technology–organization–environment framework. Sustainability, 16(5), Article 1864. https://doi.org/10.3390/su16051864
7. Central Bank of Nigeria. (2024). Risk-based cybersecurity framework and guidelines for deposit money banks and payment service banks. https://www.cbn.gov.ng/Out/2024/BSD/CBN%20Risk-Based%20Cybersecurity%20Framework%20for%20DMBs%20and%20PSBs_2024.pdf
8. Cioli, A., Marinelli, L., La Rocca, A., & Perna, A. (2025). AI adoption in B2B marketing: An analysis of technological, organizational, and environmental factors. Micro & Macro Marketing, (3), 491–527. https://doi.org/10.1431/118666
9. Cybercrimes (Prohibition, Prevention, etc.) (Amendment) Act, 2024 (Nigeria). https://digitalpolicyalert.org/change/12205
10. Dönmez, S., Tuncay Çelikel, A., Soykut Sarıca, Y. P., & Develi, E. İ. (2025). Understanding AI adoption at organizations: Literature review of TOE framework. PressAcademia Procedia, 21(1), 64–69. https://doi.org/10.17261/Pressacademia.2025.1994
11. Ennaji, S., De Gaspari, F., Hitaj, D., Kbidi, A., & Mancini, L. V. (2024). Adversarial challenges in network intrusion detection systems: Research insights and future prospects (arXiv:2409.18736) [Preprint]. arXiv. https://doi.org/10.48550/arXiv.2409.18736
12. European Parliament & Council of the European Union. (2024). Regulation (EU) 2024/1689 laying down harmonised rules on artificial intelligence (Artificial Intelligence Act). Official Journal of the European Union, L series. https://ai-act-service-desk.ec.europa.eu/en/ai-act/article-15
13. European Union Agency for Cybersecurity. (2024). ENISA threat landscape 2024. https://www.enisa.europa.eu/publications/enisa-threat-landscape-2024
14. Federal Ministry of Communications, Innovation and Digital Economy. (2024). National artificial intelligence strategy [Draft]. National Information Technology Development Agency & National Centre for Artificial Intelligence and Robotics.
15. Financial Stability Board. (2024). The financial stability implications of artificial intelligence. https://www.fsb.org/uploads/R14112024.pdf
16. Goodfellow, I. J., Shlens, J., & Szegedy, C. (2015). Explaining and harnessing adversarial examples. In International Conference on Learning Representations (ICLR 2015). https://arxiv.org/abs/1412.6572
17. Grosse, K., Bieringer, L., Besold, T. R., Biggio, B., & Krombholz, K. (2023). Machine learning security in industry: A quantitative survey. IEEE Transactions on Information Forensics and Security, 18, 1749–1762. https://doi.org/10.1109/TIFS.2023.3251842
18. Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2022). A primer on partial least squares structural equation modeling (PLS-SEM) (3rd ed.). Sage.
19. Hassan, A. S. (2024). Factors driving artificial intelligence adoption in South Africa's financial services sector. Academic Journal of Interdisciplinary Studies, 13(5). https://doi.org/10.36941/ajis-2024-0173
20. Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115–135. https://doi.org/10.1007/s11747-014-0403-8
21. Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1–55. https://doi.org/10.1080/10705519909540118
22. International Monetary Fund. (2024). Cyber risk: A growing concern for macrofinancial stability. In Global financial stability report: The last mile: Financial vulnerabilities and risks (Chapter 3). https://www.imf.org/en/Publications/GFSR/Issues/2024/04/16/global-financial-stability-report-april-2024
23. Jaakkola, E. (2020). Designing conceptual articles: Four approaches. AMS Review, 10(1–2), 18–26. https://doi.org/10.1007/s13162-020-00161-0
24. Kaur, R., Gabrijelčič, D., & Klobučar, T. (2023). Artificial intelligence for cybersecurity: Literature review and future research directions. Information Fusion, 97, Article 101804. https://doi.org/10.1016/j.inffus.2023.101804
25. Khaleel, Y. L., Habeeb, M. A., Albahri, A. S., Al-Quraishi, T., Albahri, O. S., & Alamoodi, A. H. (2024). Network and cybersecurity applications of defense in adversarial attacks: A state-of-the-art using machine learning and deep learning methods. Journal of Intelligent Systems, 33(1). https://doi.org/10.1515/jisys-2024-0153
26. Madry, A., Makelov, A., Schmidt, L., Tsipras, D., & Vladu, A. (2018). Towards deep learning models resistant to adversarial attacks. In International Conference on Learning Representations (ICLR 2018). https://arxiv.org/abs/1706.06083
27. Mink, J., Benkraouda, H., Yang, L., Ciptadi, A., Ahmadzadeh, A., Votipka, D., & Wang, G. (2023). Everybody's got ML, tell me what else you have: Practitioners' perception of ML-based security tools and explanations. In 2023 IEEE Symposium on Security and Privacy (SP). IEEE.
28. Monetary Authority of Singapore. (2024). Information paper on artificial intelligence model risk management.
29. Nigeria Data Protection Act, 2023 (Nigeria). https://kpmg.com/ng/en/insights/2023/08/nigeria-data-protection-act-2023-review.html
30. Ofuani, A. B., Omoera, C. I., & Akagha, C. (2024). Artificial intelligence integration and performance of deposit money banks in Lagos Metropolis: A study of United Bank for Africa. UNILAG Journal of Business, 10(1), 54–81.
31. Owusu, A. (2025). Achieving operational excellence through artificial intelligence: The case of Ghanaian banks. International Journal of Information Management Data Insights, 5(2), Article 100377. https://doi.org/10.1016/j.jjimei.2025.100377
32. Papernot, N., McDaniel, P., Wu, X., Jha, S., & Swami, A. (2016). Distillation as a defense to adversarial perturbations against deep neural networks. In 2016 IEEE Symposium on Security and Privacy (SP) (pp. 582–597). IEEE. https://doi.org/10.1109/SP.2016.41
33. Pinto, A. S., Abreu, A., Cota, M. P., & Paiva, J. (2024). Exploring the drivers of AI adoption: A meta-analysis of technological, organizational and environmental (TOE) factors [Preprint]. Research Square. https://doi.org/10.21203/rs.3.rs-5634577/v1
34. Rahman, M., Ming, T. H., Baigh, T. A., & Sarker, M. (2023). Adoption of artificial intelligence in banking services: An empirical analysis. International Journal of Emerging Markets, 18(10), 4270–4300. https://doi.org/10.1108/IJOEM-06-2020-0724
35. Rjoub, G., Bentahar, J., Abdel Wahab, O., Mizouni, R., Song, A., Cohen, R., Otrok, H., & Mourad, A. (2023). A survey on explainable artificial intelligence for cybersecurity. IEEE Transactions on Network and Service Management, 20(4), 5115–5140. https://doi.org/10.1109/TNSM.2023.3282740
36. Simonetto, T., Dyrmishi, S., Ghamizi, S., Cordy, M., & Le Traon, Y. (2022). A unified framework for adversarial attack and defense in constrained feature space. In Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence (IJCAI-22) (pp. 1313–1319). https://doi.org/10.24963/ijcai.2022/183
37. Tornatzky, L. G., & Fleischer, M. (1990). The processes of technological innovation. Lexington Books.
38. Uren, V., & Edwards, J. (2023). Technology readiness and the organizational journey towards AI adoption: An empirical study. International Journal of Information Management, 68, Article 102588. https://doi.org/10.1016/j.ijinfomgt.2022.102588
39. Vassilev, A., Oprea, A., Fordyce, A., Anderson, H., Davies, X., & Hamin, M. (2025). Adversarial machine learning: A taxonomy and terminology of attacks and mitigations (NIST AI 100-2e2025). National Institute of Standards and Technology. https://doi.org/10.6028/NIST.AI.100-2e2025
40. Wallace, S. A., Green, K. Y., Johnson, C. M., Cooper, J. T., & Gilstrap, C. M. (2020). An extended TOE framework for cybersecurity-adoption decisions. Communications of the Association for Information Systems, 47, 338–363. https://doi.org/10.17705/1CAIS.04716