Artificial Intelligence In Cyber Threat Detection: A Survey Of Predictive Security Systems

The scope and nuances of cyber threats have been escalating with the blistering pace of digital technologies development, and several questions are raised regarding the applicability of the traditional strategies of cyber security. Antivirus and firewall programs are examples of conventional security measures; however, they only protect against known threats and cannot detect or prevent new ones. By combining machine learning, neural networks, and natural language processing to identify outliers, anticipate attacks, and automate responses, Artificial Intelligence (AI) enables more proactive and adaptable defense. This article is a review of the state of the cyber threat detection using AI, in which it cites multi-layered system incorporating data collection, preprocessing, real-time analytics, and automated cancellation. It examines major types of threats, including phishing, ransomware, insider threats, and vulnerabilities at the protocol level, as well as issues related to implementation, including data quality, model transparency, and integration. New methods such as Federated Learning and Generative AI are also discussed, possibly to augment decentralized learning and to create attack-inspired scenarios. This paper highlights the necessity of developing intelligent systems that can adapt to cyber threats to enhance the resilience of infrastructure in the digital world.