Cloud computing environments have become the backbone of modern digital infrastructure, enabling scalable computing, distributed storage, and on-demand services. However, this rapid adoption has significantly expanded the attack surface, making cloud platforms prime targets for advanced cyber threats such as misconfiguration attacks, API exploitation, ransomware, and insider threats. Traditional security mechanisms struggle to keep pace with the speed, scale, and sophistication of these evolving threats
This paper proposes an AI-powered threat intelligence framework designed to enhance automated cloud security monitoring and response systems. The framework integrates machine learning, deep learning, and real-time behavioral analytics to detect anomalies, predict potential attacks, and enable autonomous response actions. By leveraging continuous threat intelligence feeds and security telemetry from cloud environments, the system dynamically adapts to emerging attack vectors
The study emphasizes the role of AI in improving detection accuracy, reducing false positives, and enabling near real-time incident response through automation. Additionally, it explores how orchestration tools and security information and event management (SIEM) systems can be enhanced using AI-driven decision-making models.
The proposed framework aims to strengthen cloud resilience, minimize human intervention, and improve overall cybersecurity posture in highly dynamic cloud ecosystems