The Role of Artificial Intelligence and Machine Learning in Strengthening Threat Intelligence and Anomaly Detection in Cloud Networks

Abstract

This paper examines the role of artificial intelligence (AI) and machine learning (ML) in enhancing threat intelligence and anomaly detection within cloud networks. As cloud environments become more complex and dynamic, traditional security methods struggle to keep pace with evolving threats. AI and ML offer a solution by automating the analysis of vast amounts of data, identifying patterns, and detecting anomalies in real time. AI-driven threat intelligence improves data collection, predictive analysis, and the sharing of threat information across cloud environments, enabling faster detection and response to potential cyber threats. Meanwhile, ML-based anomaly detection systems establish behavioral baselines and continuously monitor network activity to identify deviations that may indicate security incidents, all while minimizing false positives through adaptive learning. Despite these advantages, the implementation of AI and ML in cloud security presents challenges, including data privacy concerns, computational overhead, and the risk of adversarial attacks. The paper highlights the benefits and limitations of AI and ML in cloud security, emphasizing the need for secure data handling, resource optimization, and robust defenses against adversarial threats. By addressing these challenges, organizations can leverage AI and ML to strengthen their cloud security posture, improving their ability to detect and mitigate cyber threats in real time. The paper concludes by discussing the future role of AI in cloud security, noting that advancements in AI technologies will continue to drive innovation in cloud threat detection and defense strategies.