About This Publication

Deepfake-Driven Social Engineering: Threats, Detection Techniques, and Defensive Strategies in Corporate Environments is a 2025 cybersecurity study examining how AI-generated audio, video, images, and synthetic impersonation can exploit trust inside corporate communications and bypass conventional security controls.

Drawing on interviews with cybersecurity professionals from five organizations in IT, finance, education, research, and cybersecurity, the authors examine organizational readiness, deepfake detection gaps, employee training, incident response, real-time monitoring, vendor dependence, and the limits of generalized cybersecurity controls. The paper discusses executive impersonation, voice cloning, phishing and vishing, fraudulent payment instructions, disinformation, reputation risk, and the growing difficulty of distinguishing authentic communications from synthetic media.

A central contribution is the PREDICT framework — Policies, Readiness, Education, Detection, Incident Response, Continuous Improvement, and Testing — a cyclical corporate-defense model designed to combine governance, specialized detection, employee education, forensic validation, incident-response procedures, simulations, and continuous adaptation. The paper also discusses Zero Trust principles, secure verification channels, multimodal detection, digital forensics, and real-time analysis of suspicious media.

The study is relevant to organizations, incident-response teams, cybersecurity professionals, legal and compliance personnel, and executives confronting deepfake-driven social engineering, AI impersonation, business email and voice compromise, synthetic media, corporate fraud, and trust-based cyber risk.

Original Publication: April 27, 2025

Journal: Journal of Cybersecurity and Privacy

Volume / Article: 5, Article 18 (2025)

DOI: 10.3390/jcp5020018

Publication Status: Peer-Reviewed, Open Access

License: Creative Commons Attribution (CC BY 4.0)

Authors

• Kristoffer Torngaard Pedersen — DTU Compute, Technical University of Denmark
• Lauritz Pepke — DTU Compute, Technical University of Denmark
• Tobias Stærmose — DTU Compute, Technical University of Denmark
• Maria Papaioannou — DTU Compute, Technical University of Denmark
• Gaurav Choudhary — DTU Compute, Technical University of Denmark
• Nicola Dragoni — DTU Compute, Technical University of Denmark; Corresponding Author

The article credits Pedersen, Pepke, and Stærmose with investigation and data curation; all six authors with conceptualization, methodology, and original-draft preparation; and Papaioannou, Choudhary, and Dragoni with review/editing, supervision, and project administration.