This paper provides a comprehensive review of the growing cybersecurity challenges posed by deepfake technology and advanced language models such as ChatGPT. With the advancement of machine learning techniques like Generative Adversarial Networks (GANs), deepfake technology can produce increasingly realistic synthetic media, which malicious actors exploit for purposes ranging from misinformation to identity theft. The paper examines these technologies’ dual-use potential, noting their roles in both enhancing and undermining cybersecurity measures. Current detection strategies, categorised into image, video, and text-based methods, are analysed alongside potential integrations with social media platforms for more robust defence mechanisms. Emerging technologies, such as blockchain, are also discussed for their potential to strengthen digital content authentication and protect against cyber deception. By exploring application scenarios, including social media engineering and the Internet of Things (IoT), the paper highlights key insights, challenges, and open questions. It concludes by recommending proactive measures and future research directions, emphasising the urgency of developing adaptive, multi-modal detection systems to safeguard against deepfake-related threats in the evolving cybersecurity landscape.