The Weaponization of Deepfakes: Implications for Cybersecurity and Society

Deepfakes are AI-generated synthetic media created using deep learning techniques such as Generative Adversarial Networks (GANs). While these technologies enable innovation in digital media and accessibility, their misuse has emerged as a significant cybersecurity threat. Deepfakes are increasingly weaponized for political disinformation, financial fraud, identity theft, and non-consensual imagery, undermining trust in digital evidence and public information systems. This paper examines the cybersecurity and societal implications of deepfake weaponization, reviews existing technical, legal, and regulatory countermeasures, and evaluates their limitations. Current defences, including AI forensic detection, digital watermarking, and policy interventions, are analysed alongside emerging approaches such as adversarial training, hardware-level media provenance, and federated learning. The paper concludes that a multi-layered defence strategy combining adaptive AI techniques, secure content provenance, legal frameworks, and digital literacy is essential to mitigate the evolving risks posed by deepfake technologies