Pieterse, Heloise2025-09-252025-09-252025-070-7988-5673-4http://hdl.handle.net/10204/14416The advancement of artificial intelligence (AI) technologies has become a trending topic in the cybersecurity domain. These technologies, however, present cybersecurity with a double-edged sword as AI offers enhanced threat detection and protection, but also enables cybercriminals to craft sophisticated cyberattacks. Deepfakes, which are a form of digitally manipulated synthesised media created using deep learning techniques, have garnered widespread attention due to the use of deepfakes in cyberattacks to cause influence, spread disinformation, or conduct fraudulent activities. While extensive research efforts have been undertaken to develop defences against deepfakes, the solutions are technical and not easily accessible. Innovative strategies are required to equip personnel from government, academia, and the business sector with the fundamental knowledge to detect and defend against cyberattacks employing deepfake technology. This paper evaluates the most significant events involving deepfakes since the emergence of the technology in November 2017. Key trends and characteristics are identified and mapped to a temporal attack model to separate the different stages of a cyberattack involving deepfakes. The outcome is a Deepfake Attack Framework that offers valuable insights essential to understanding the risks associated with deepfakes. The Deepfake Attack Framework presents a theoretical solution that can be applied practically to minimise risk and enable personnel to be better prepared to defend against deepfake-driven cyberattacks.FulltextenArtificial IntelligenceDeepfakesDeep LearningAttack ModelCyberattacksCybersecurity AwarenessConference PresentationN/A