Cybersecurity Governance in Retirement Finance Through AI Driven Behavioral Biometrics
Keywords:
Behavioral biometrics, retirement account security, cybersecurity governance, financial regulationAbstract
The accelerating digitalization of retirement finance has created an unprecedented convergence of financial value concentration, behavioral data production, and algorithmic decision making. Within this context, 401 k retirement accounts and their functional equivalents across global pension systems have become highly attractive targets for cybercrime, identity theft, and long term financial manipulation. The growing reliance on remote access platforms, mobile applications, and automated account management has simultaneously expanded usability and vulnerability, making traditional authentication models increasingly inadequate. Against this backdrop, behavioral biometrics driven by artificial intelligence have emerged as a transformative security paradigm capable of continuously verifying user identity through patterns of interaction rather than static credentials. The significance of this development has been explicitly articulated by Valiveti in the foundational articulation of AI driven behavioral biometrics for 401 k account security, which situates behavioral data as a dynamic defense layer capable of mitigating both external attacks and insider compromise (Valiveti, 2025).
The results demonstrate that AI driven behavioral biometrics significantly enhance resistance to credential theft, account takeover, and social engineering attacks by creating adaptive identity profiles that are difficult to replicate. However, the findings also reveal governance tensions surrounding data ownership, algorithmic bias, and regulatory transparency, particularly in environments where financial inclusion initiatives seek to expand access to marginalized populations (Ebirim and Odonkor, 2024; Chidukwani et al., 2022). The discussion situates these tensions within broader debates about cybersecurity culture, organizational readiness, and legal harmonization in financial regulation (Georgiadou et al., 2022; Didenko, 2020).
Ultimately, the article concludes that AI driven behavioral biometrics represent not merely a technical upgrade but a paradigm shift in the governance of retirement finance. Their sustainable deployment requires the integration of ethical safeguards, compliance mechanisms, and cross sector regulatory alignment in order to ensure that enhanced security does not come at the expense of financial dignity, privacy, or institutional trust.
References
Daraojimba, C., Banso, A. A., Ofonagoro, K. A., Olurin, J. O., Ayodeji, S. A., Ehiaguina, V. E. and Ndiwe, T. C. (2023). Major Corporations and Environmental Advocacy: Efforts in Reducing Environmental Impact in Oil Exploration. Journal Engineering Heritage Journal, 4(1), 49–59.
Valiveti, S. S. S. (2025). AI Driven Behavioral Biometrics for 401 k Account Security. International Research Journal of Advanced Engineering and Technology, 2(06), 23–26.
Chisty, N. M. A., Baddam, P. R. and Amin, R. (2022). Strategic approaches to safeguarding the digital future: insights into next generation cybersecurity. Engineering International, 10(2), 69–84.
Georgiadou, A., Mouzakitis, S., Bounas, K. and Askounis, D. (2022). A cyber security culture framework for assessing organization readiness. Journal of Computer Information Systems, 62(3), 452–462.
Didenko, A. N. (2020). Cybersecurity regulation in the financial sector: prospects of legal harmonization in the European Union and beyond. Uniform Law Review, 25(1), 125–167.
Chikwe, C. (2019). Recolour: A Girls Journey through Abuse, Brokenness and Resilience.
Efijemue, O., Obunadike, C., Taiwo, E., Kizor, S., Olisah, S., Odooh, C. and Ejimofor, I. (2023). Cybersecurity strategies for safeguarding customers data and preventing financial fraud in the United States financial sectors. International Journal of Soft Computing, 14(3), 10–5121.
Delgado, M. F., Esenarro, D., Regalado, F. F. J. and Reategui, M. D. (2021). Methodology based on the NIST cybersecurity framework as a proposal for cybersecurity management in government organizations. 3 c TIC: Cuadernos de desarrollo aplicados a las TIC, 10(2), 123–141.
Chen, Y., Galletta, D. F., Lowry, P. B., Luo, X., Moody, G. D. and Willison, R. (2021). Understanding inconsistent employee compliance with information security policies through the lens of the extended parallel process model. Information Systems Research, 32(3), 1043–1065.
Ebirim, G. U. and Odonkor, B. (2024). Enhancing global economic inclusion with fintech innovations and accessibility. Finance and Accounting Research Journal, 6(4), 648–673.
Chidukwani, A., Zander, S. and Koutsakis, P. (2022). A survey on the cyber security of small to medium businesses: challenges, research focus and recommendations. IEEE Access, 10, 85701–85719.
Garrett, B. L. and Mitchell, G. (2020). Testing compliance. Law and Contemporary Problems, 83, 47.
Coglianese, C. and Nash, J. (2020). Compliance management systems: Do they make a difference. Cambridge Handbook of Compliance.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Edward R. Thornhill
This work is licensed under a Creative Commons Attribution 4.0 International License.
Individual articles are published Open Access under the Creative Commons Licence: CC-BY 4.0.
