Deception By Design: Deepfakes And Malicious Insider Deviance In Cybersecurity - Anti, Emmanuel; Dang, Duong; Bui, Quang (Association for Information Systems, 2026)
Deepfake technology poses emerging risks for organizations by enabling the manipulation of audio, video, and images in ways that insiders can exploit to commit fraud, impersonate colleagues, or sabotage operations. This study extends Fraud Triangle Theory (FTT) to examine how deepfakes influence insider deviant behavior by reshaping perceptions of pressure, opportunity, and rationalization. This study will use quantitative methods to survey professionals across Europe, America, and Asia (n=250), testing a model of deepfake-enabled insider deviance while examining context-specific threats, motivations, and ethical rationalizations. Structural equation modeling will be used to validate and interpret findings. This study contributes to the IS literature by integrating emerging technologies into fraud theory, highlighting the misuse of deepfakes as a critical internal threat, and offering practical guidance for security governance, policy development, and AI-based detection strategies.
Keywords: Deepfakes; Malicious Insider Deviance; Fraud Triangle Theory; Cybersecurity; fi=Viestintätieteet|en=Communication Studies|; fi=Tietojärjestelmätiede|en=Information Systems Science|