AI-Enhanced Security: Mastering Multi-Cloud Posture Management

The rapid adoption of multi-cloud environments has improved scalability and flexibility but introduced significant security complexity. Managing distributed workloads across heterogeneous cloud providers often results in inconsistent policies, hidden vulnerabilities, and delayed incident response. This paper proposes an AI-Enhanced MultiCloud Security Posture Management (MCSPM) framework to address these challenges. The approach integrates continuous risk assessment, machine learning–driven anomaly detection, and zero trust access control to proactively monitor and harden cloud infrastructures. By collecting telemetry from multiple platforms and applying predictive analytics, the system identifies misconfigurations, evaluates threat exposure, and automates remediation with minimal human intervention. A layered architecture combining cloud-level security intelligence, DevSecOps automation, and edge-aware analytics ensures scalability and faster response to dynamic attacks. Experimental evaluation demonstrates improved detection accuracy, reduced false positives, and enhanced compliance alignment. This study highlights how AI-driven posture management can significantly strengthen cloud security resilience while supporting agile, large-scale enterprise operations.