Cybersecurity in the AI Era Measures Deepfake Threats and Artificial Intelligence-Based Attacks

Abstract

The development of artificial intelligence (AI) has had a significant impact on cybersecurity, both as a defense tool and as a threat. One of the biggest emerging risks is deepfake attacks and AI-based cyberattacks, which are increasingly difficult to detect and mitigate. Deepfake technology, which uses Generative Adversarial Networks (GANs), enables video and audio manipulation with a high level of realism, which can be used for disinformation, fraud, and threats to digital security systems. This research aims to analyze cybersecurity threats caused by deepfakes and AI-based attacks, evaluate the effectiveness of conventional security systems in dealing with them, and propose more adaptive AI-based mitigation strategies. The method used in this study is a literature study with a qualitative approach, collecting data from various academic sources, industry reports, and regulations related to cybersecurity. The analysis was carried out using thematic analysis and data triangulation techniques, which allowed mapping of the latest threat trends and solutions that had been implemented. The results show that signature-based security is increasingly ineffective in the face of evolving AI attacks. The implementation of AI in cyber defense systems, such as machine learning-based detection, Zero Trust Architecture (ZTA), and incident response automation systems, is the main solution in dealing with increasingly complex threats. Therefore, an adaptive security approach that combines technology, regulatory policies, and public education is needed to reduce the risk of deepfake attacks and other AI threats.