AI-driven threat intelligence: A global perspective on cloud security risks and mitigation strategies

Aladiyan, Anbarasu (2025) AI-driven threat intelligence: A global perspective on cloud security risks and mitigation strategies. Global Journal of Engineering and Technology Advances, 23 (2). 031-036. ISSN 2582-5003

This article explores the transformative role of artificial intelligence in enhancing cloud security through advanced threat intelligence capabilities. As organizations increasingly migrate to cloud environments, they face evolving security challenges that traditional approaches struggle to address effectively. AI-driven threat intelligence offers powerful solutions by leveraging machine learning and deep learning techniques to analyze vast datasets, detect anomalous behaviors, and predict potential threats with greater accuracy than conventional methods. It examines global variations in cloud security risks across geographical regions, industry sectors, and regulatory environments, highlighting the need for contextually aware AI security solutions. It further delves into comprehensive mitigation strategies, categorizing them into proactive measures (anomaly detection, risk assessment, automated hardening) and reactive approaches (incident response, threat containment, post-incident analysis). While acknowledging the significant advantages of AI-enhanced security, the study also addresses persistent challenges including data quality issues, adversarial machine learning tactics, resource constraints, and organizational resistance. The findings offer valuable insights for cybersecurity professionals, cloud service providers, and policymakers navigating this rapidly evolving security landscape.