Deepfake Threats in Business Communications: Detection, Governance, and Trust Management
DOI:
https://doi.org/10.56830/WRBA07202601Keywords:
Deepfake Threats, Business Communication, Governance, Detection, Trust ManagementAbstract
Deepfake technologies can create seamless impersonations of real people, making them say or do things they never actually said or did. These impersonations can be entertaining parodies, but they pose challenges for organizations. Because the technology enables smooth, easy fake video generation, it is contributing to the proliferation of fake digital media and fake news. The implications of Deepfake technologies are far-reaching, threatening democracy, physical privacy, personal reputation, and financial stability. Domestically, the challenge of managing trust is already daunting. It will worsen substantially as malicious actors continue to leverage generative models to produce increasingly convincing synthetic content. Creating strong governance frameworks is a necessary and appropriate response to Deepfakes, though it is not clear who should take the lead and how. Such fundamental tenets of modern society are core to stakeholder confidence in cross-border investment, financial systems, digital currencies, and currency exchange. As AI-based Deepfake technologies advance and proliferate, their risks to digital communications and governance systems are expected to increase. Organizations should proactively understand their influence trends and implement robust detection and evaluation methods to correct the effects of Deepfake technologies. The detection challenge spans multiple scopes and levels, extending to online comments and digital platforms. Mechanisms deployed in understanding these trends should extend beyond the corporate premise. These may involve synthetic media and Deepfake techniques, fast-evolving tools that should be anticipated and analyzed thoroughly. Such an understanding might help leverage the associated protocols before they inflict damage and may also help build competitive intelligence.
References
Abdel-Wahab, A., & Alkhatib, M. (n.d). Toward Robust Deepfake Defense: A Review of Deepfake Detection and Prevention Techniques in Images. Computers.
Almutairi, Z., & Elgibreen, H. (2022). A review of modern audio deepfake detection methods: challenges and future directions. Algorithms. DOI: https://doi.org/10.3390/a15050155
Armingol, E., Baghdassarian, H. M., & Lewis, N. E. (2024). The diversification of methods for studying cell–cell interactions and communication. Nature Reviews Genetics. DOI: https://doi.org/10.1038/s41576-023-00685-8
Arora, G., Kalia, Y., & Arora, N. (2025). Sustainability in the Deepfake Era—The Role of CSR and Ethical Leadership in Mitigating Risks. In Navigating the Deepfake Conundrum:. A Manager's Roadmap , (pp. 49-62). Cham: Springer Nature Switzerland. DOI: https://doi.org/10.1007/978-3-031-90742-5_4
D. Bray, S., D. Johnson, S., & Kleinberg, B. (2022). Testing Human Ability To Detect Deepfake Images of Human Faces. DOI: https://doi.org/10.1093/cybsec/tyad011
Edwards, P., Nebel, J. C., Greenhill, D., & Liang, X. (2024). A review of deepfake techniques: Architecture, detection, and datasets. IEEE Access. DOI: https://doi.org/10.1109/ACCESS.2024.3477257
Etienne, H. (2022). The future of online trust (and why Deepfake is advancing it).
Farid, H. (2022). Creating, using, misusing, and detecting deep fakes. Journal of Online Trust and Safety. DOI: https://doi.org/10.54501/jots.v1i4.56
Gupta, S., & Ozen, E. (2025). of Deepfakes in G7 Business Practices. Navigating the Deepfake Conundrum: A Manager's Roadmap, 207. DOI: https://doi.org/10.1007/978-3-031-90742-5_15
Heidari, A., Jafari Navimipour, N., Dag, H., & Unal, M. (2024). Deepfake detection using deep learning methods: A systematic and comprehensive review. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 14(2), e1520. DOI: https://doi.org/10.1002/widm.1520
Kietzmann, J., Lee, L., McCarthy, I., & Kietzmann, T. (2019). Deepfakes: trick or treat? DOI: https://doi.org/10.1016/j.bushor.2019.11.006
Savale, T. K., Kaur, D., & Kamble, S. M. (2026). Leveraging AI and Business Intelligence for Financial Decision-Making in the Age of Deepfakes. Enhancing Consumer Trust and Marketing Resilience. DOI: https://doi.org/10.1108/978-1-80592-911-620261003
Seng, L. K., Mamat, N., Abas, H., & Ali, W. N. (2024). AI integrity solutions for deepfake identification and prevention. Open International Journal of Informatics, 12(1), 35-46. DOI: https://doi.org/10.11113/oiji2024.12n1.297
Singh, D. P. (2023). Exploding AI-generated deepfakes and misinformation. A threat to global concern in the 21st century. DOI: https://doi.org/10.36227/techrxiv.24715605.v1
Thi Nguyen, T., Viet Hung Nguyen, Q., Tien Nguyen, D., Thanh Nguyen, D., Huynh-The, T., Nahavandi, S., et al. (2019). Deep Learning for Deepfakes Creation and Detection. A Survey.
Wahab, A., Wang, J., Shojaei, A., & Ma, J. (2023). A model-based smart contracts system via blockchain technology to reduce delays and conflicts in construction management processes. Engineering, Construction and Architectural Management, 30(10), 5052-5072. DOI: https://doi.org/10.1108/ECAM-03-2022-0271
