International Journal of Science, Engineering and TechnologyAuthors: Uwaisu Abubakar Umar, Ibrahim Haruna Ibrahim, Aliyu Aminu Dahiru, Emmanuel Martin Teman
Abstract: The rapid migration of organizational infrastructure to cloud computing has introduced unprecedented scalability and efficiency alongside increasingly complex security vulnerabilities. This review examines the evolving relationship between artificial intelligence (AI) and cloud security through an AI-Augmented Systematic Literature Review (ASLR), in which AI serves simultaneously as a research instrument for synthesizing literature and as the object of study within cloud security architectures. Drawing on studies published between 2020 and 2026 sourced from IEEE Xplore, ACM Digital Library, and SpringerLink, the review addresses three objectives: examining how cloud infrastructure supports the deployment of resource-intensive machine learning (ML) models, investigating AI's role as an adaptive defensive layer, and identifying new security vulnerabilities introduced by embedding AI into cloud networks. Findings show that cloud platforms enable resource-intensive ML through elastic compute allocation, architectural abstraction, and intelligent orchestration; that AI strengthens defense through predictive threat detection, continuous policy adaptation, and privacy-preserving federated learning; and that this same integration introduces distinct new vulnerability classes, including agent privilege escalation, lifecycle-specific exploitation, cascading cross-layer failures, serverless architectural weaknesses, and adversarial manipulation. The review concludes that AI functions as a double-edged capability within cloud ecosystems, simultaneously strengthening and expanding the attack surface, and argues for lifecycle-aware, zero-trust security models validated under real-world rather than solely simulated conditions.
