Deepfake-as-a-Service: The Next Challenge for Enterprise Cybersecurity
DOI:
https://doi.org/10.47941/jts.2752Keywords:
Deepfake-as-a-Service (DFaaS), Enterprise Cybersecurity, Synthetic Media, Cognitive Authenticity, Digital Deception, Impersonation Threats, AI Regulation, Threat Modeling.Abstract
Purpose: This study investigates the emerging threat of Deepfake-as-a-Service (DFaaS) and its implications for enterprise cybersecurity. It aims to explore how the commodification of synthetic media is reshaping threat landscapes, operational vulnerabilities, and strategic responses within organizational settings.
Methodology: An exploratory qualitative design was adopted, combining thematic document analysis with expert commentary. A corpus of 85 peer-reviewed articles, industry reports, and threat intelligence briefs published between 2018 and 2024 was systematically reviewed. NVivo software facilitated thematic clustering, while Braun and Clarke’s six-phase framework guided data coding and interpretation.
Findings: The study reveals five key themes: (1) DFaaS platforms are increasingly accessible and sophisticated, (2) enterprises face specific threats such as executive impersonation, internal disinformation, and financial fraud, (3) current detection infrastructures are inadequate against adversarial synthetic media, (4) corporate governance and policy responses are fragmented and reactive, and (5) resilience strategies such as content provenance, cross-channel verification, and employee training are emerging but unevenly adopted.
Unique Contribution to Theory, Practice, and Policy: Theoretically, the study introduces the construct of Cognitive Authenticity to reframe cybersecurity around perceptual integrity. In practice, it outlines a DFaaS Attack Lifecycle Model and recommends enterprise-level operational shifts to address synthetic threats. From a policy standpoint, it advocates for legal recognition of synthetic identity fraud, mandates for content authenticity standards, and international cooperation on AI-enabled cyber norms. These contributions bridge technical, organizational, and regulatory perspectives in confronting synthetic deception.
Downloads
References
Adewole, K. S., Alozie, E., Olagunju, H., Faruk, N., Aliyu, R. Y., Imoize, A. L., Abdulkarim, A., Imam-Fulani, Y. O., Garba, S., Baba, B. A., Hussaini, M., Oloyede, A. A., Abdullahi, A., Kanya, R. A., & Usman, D. J. (2024). A systematic review and meta-data analysis of clinical data repositories in Africa and beyond: recent development, challenges, and future directions. Discover Data, 2(1). https://doi.org/10.1007/S44248-024-00012-4
Admass, W. S., Munaye, Y. Y., & Diro, A. A. (2024). Cyber security: State of the art, challenges and future directions. Cyber Security and Applications, 2. https://doi.org/10.1016/j.csa.2023.100031
Ainslie, S., Thompson, D., Maynard, S., & Ahmad, A. (2023). Cyber-threat intelligence for security decision-making: A review and research agenda for practice. Computers and Security, 132. https://doi.org/10.1016/j.cose.2023.103352
Ali, A., Khan Ghouri, K. F., Naseem, H., Soomro, T. R., Mansoor, W., & Momani, A. M. (2022). Battle of Deep Fakes: Artificial Intelligence Set to Become a Major Threat to the Individual and National Security. International Conference on Cyber Resilience, ICCR 2022. https://doi.org/10.1109/ICCR56254.2022.9995821
Allen, D. (2021). Deepfake Fight: AI-Powered Disinformation and Perfidy Under the Geneva Conventions. SSRN Electronic Journal. https://doi.org/10.2139/SSRN.3958426
Almahmoud, Z., Yoo, P. D., Damiani, E., Choo, K. K. R., & Yeun, C. Y. (2025). Forecasting Cyber Threats and Pertinent Mitigation Technologies. Technological Forecasting and Social Change, 210. https://doi.org/10.1016/j.techfore.2024.123836
Belen-Saglam, R., Altuncu, E., Lu, Y., & Li, S. (2023). A systematic literature review of the tension between the GDPR and public blockchain systems. Blockchain: Research and Applications, 4(2). https://doi.org/10.1016/j.bcra.2023.100129
Belur, J., Tompson, L., Thornton, A., & Simon, M. (2021). Interrater Reliability in Systematic Review Methodology: Exploring Variation in Coder Decision-Making. Sociological Methods and Research, 50(2), 837–865. https://doi.org/10.1177/0049124118799372
Fallis, D. (2021). The Epistemic Threat of Deepfakes. Philosophy Technology, 34(4), 623–643. https://doi.org/10.1007/s13347-020-00419-2
Guandalini, I. (2022). Sustainability through digital transformation: A systematic literature review for research guidance. Journal of Business Research, 148, 456–471. https://doi.org/10.1016/j.jbusres.2022.05.003
Hasan, H. R., Salah, K., Jayaraman, R., Yaqoob, I., & Omar, M. (2024). NFTs for combating deepfakes and fake metaverse digital contents. Internet of Things (Netherlands), 25. https://doi.org/10.1016/j.iot.2024.101133
Javaid, M., Haleem, A., Singh, R. P., & Suman, R. (2023). Towards insighting cybersecurity for healthcare domains: A comprehensive review of recent practices and trends. Cyber Security and Applications, 1. https://doi.org/10.1016/j.csa.2023.100016
Kaur, A., Noori Hoshyar, A., Saikrishna, V., Firmin, S., & Xia, F. (2024). Deepfake video detection: challenges and opportunities. Artificial Intelligence Review, 57(6). https://doi.org/10.1007/S10462-024-10810-6
Khan, R., Sohail, M., Usman, I., Sandhu, M., Raza, M., Yaqub, M. A., & Liotta, A. (2024). Comparative study of deep learning techniques for DeepFake video detection. ICT Express. https://doi.org/10.1016/j.icte.2024.09.018
Li, Y., & Liu, Q. (2021). A comprehensive review study of cyber-attacks and cyber security; Emerging trends and recent developments. Energy Reports, 7, 8176–8186. https://doi.org/10.1016/j.egyr.2021.08.126
Lucas, K. T. (2022). Deepfakes and Domestic Violence: Perpetrating Intimate Partner Abuse Using Video Technology. Victims and Offenders, 17(5), 647–659. https://doi.org/10.1080/15564886.2022.2036656
Matli, W. (2024). Extending the theory of information poverty to deepfake technology. International Journal of Information Management Data Insights, 4(2). https://doi.org/10.1016/j.jjimei.2024.100286
Mishra, A., Alzoubi, Y. I., Anwar, M. J., & Gill, A. Q. (2022). Attributes impacting cybersecurity policy development: An evidence from seven nations. Computers and Security, 120. https://doi.org/10.1016/j.cose.2022.102820
Momeni, M. (2024). Artificial Intelligence and Political Deepfakes: Shaping Citizen Perceptions through Misinformation. Journal of Creative Communications. https://doi.org/10.1177/09732586241277335
Ramanaharan, R., Guruge, D. B., & Agbinya, J. I. (2025). DeepFake video detection: Insights into model generalisation — A Systematic review. Data and Information Management. https://doi.org/10.1016/j.dim.2025.100099
Ray, A. (2021). Disinformation, deepfakes and democracies: The need for legislative reform. The University of New South Wales Law Journal, 44(3), 983–1013. https://doi.org/10.53637/dels2700
Riphagen, Q., & of Twente, U. (2022). The deepfake problem.
Salahdine, F., & Kaabouch, N. (2019). Social Engineering Attacks: A Survey. Future Internet 2019, Vol. 11, Page 89, 11(4), 89. https://doi.org/10.3390/FI11040089
Sandoval, M. P., de Almeida Vau, M., Solaas, J., & Rodrigues, L. (2024a). Threat of deepfakes to the criminal justice system: a systematic review. Crime Science, 13(1). https://doi.org/10.1186/S40163-024-00239-1
Sandoval, M. P., de Almeida Vau, M., Solaas, J., & Rodrigues, L. (2024b). Threat of deepfakes to the criminal justice system: a systematic review. Crime Science, 13(1). https://doi.org/10.1186/S40163-024-00239-1
Sandoval, M. P., de Almeida Vau, M., Solaas, J., & Rodrigues, L. (2024c). Threat of deepfakes to the criminal justice system: a systematic review. Crime Science, 13(1), 1–16. https://doi.org/10.1186/S40163-024-00239-1/FIGURES/3
Vaccari, C., & Chadwick, A. (2020). Deepfakes and Disinformation: Exploring the Impact of Synthetic Political Video on Deception, Uncertainty, and Trust in News. Social Media and Society, 6(1). https://doi.org/10.1177/2056305120903408
Vecchietti, G., Liyanaarachchi, G., & Viglia, G. (2025). Managing deepfakes with artificial intelligence: Introducing the business privacy calculus. Journal of Business Research, 186. https://doi.org/10.1016/j.jbusres.2024.115010
Wazid, M., Mishra, A. K., Mohd, N., & Das, A. K. (2024). A Secure Deepfake Mitigation Framework: Architecture, Issues, Challenges, and Societal Impact. Cyber Security and Applications, 2. https://doi.org/10.1016/j.csa.2024.100040
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Nkiru Ali Suleiman
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution (CC-BY) 4.0 License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
