Artificial Intelligence Techniques for Real-Time Cyber Threat Detection in Cloud Computing Environments

Abstract

Artificial intelligence has become a vital technology that has been used to enhance cloud cybersecurity through real-time detection of cyber threats and automatic incident response. Traditional rule-based security mechanisms usually have challenges with detecting zero-day attacks, behavioural anomalies, and evolving threats in cloud-native infrastructure. This paper focuses on the use of Artificial Intelligence (AI) methods such as Machine Learning (ML), Deep Learning (DL), and hybrid AI in enhancing cyber threat detection, response time, and cloud security performance. The paper uses a secondary data collection strategy involving peer-reviewed journal articles, cybersecurity reports and industry literature. A qualitative research design has been adopted to analyse the existing evidence in an inductive approach. Thematic analysis has been used to analyse different themes in the literature, such as AI technologies for threat detection, detection accuracy, response time, real-time monitoring, and cloud security performance. The results show that AI-based Intrusion Detection Systems detect different cyber threats such as DDoS attacks, IoT attacks, phishing attacks and industrial denial-of-service attacks with a detection accuracy of 94%-99.99%. Moreover, the use of AI-based technology results in a significant reduction in the MTTD and MTTR of 30-50% by providing continuous monitoring and automated reaction capabilities to enhance organisational resilience. In addition, the study notes that risk management, network perimeter security, and business continuity are still among the main cybersecurity concerns in cloud computing. The study concludes that using AI for cyber threat detection is scalable, precise, and proactive. Using AI with continuous monitoring, Zero Trust strategy, and DevSecOps can enhance cloud security from cyber threats.