Integrating Artificial Intelligence into Cloud Security: A Layered Framework for Threat Detection, Compliance, and Automation Across the SDLC

Cloud Computing provides a new landscape for application development and infrastructure management, and introduces a dynamic thread space due to its distributed and elastic nature. Classic security methods are not enough to fight off cyber-threats despite modern agile and cloud-native environments. This paper explores how to integrate Artificial Intelligence (AI) in an efficient manner across the Software Development Life-Cycle (SDLC) that would enhance cloud security. Add more-attractive threat detection, anomaly discovery, and policy-based automation into the development, testing, deployment, and runtime life cycles, so that you can detect threats more quickly, as well as more proactively break down risk. In this work we explore the state of the art and tools of security automation, with a particular focus on AI models for anomaly detection for security and case demonstrations of its real applications. A holistic framework for AI-driven security orchestration in Cloud application Pipelines: Towards Reducing Attack Surfaces, Compliance and Resilience.