Artificial Intelligence as a Service (AIaaS) for Secure Cloud Computing: Threat Detection, Risk Mitigation, and Autonomous Defence

Abstract

The rapid expansion of cloud computing has revolutionized digital service delivery by enabling scalable, on-demand, and distributed infrastructures. However, the dynamic and multi-tenant nature of cloud environments has intensified security vulnerabilities, including advanced persistent threats, data breaches, insider attacks, and misconfiguration risks. Conventional rule-based security mechanisms often lack adaptability and real-time responsiveness required in modern cloud ecosystems.This paper investigates the integration of Artificial Intelligence as a Service (AIaaS) into cloud computing architectures to establish intelligent, autonomous, and scalable security frameworks. A security-driven AIaaS model is proposed that incorporates machine learning-based threat detection, behavioral anomaly analysis, predictive risk mitigation, and automated defense orchestration. By leveraging distributed intelligence principles and edge-cloud collaboration, the framework enhances detection accuracy, reduces response latency, and improves system resilience.The study further examines architectural design considerations, deployment challenges, and performance trade-offs associated with AI-enabled security services in hybrid and multi-cloud environments. The results demonstrate that AIaaS-based security mechanisms significantly strengthen cyber resilience by enabling adaptive monitoring and autonomous response strategies. The proposed framework contributes to the advancement of secure, intelligent, and self-optimizing cloud infrastructures.