Secure and Scalable AI-Driven DevSecOps Pipelines for Multi-Cloud Environments: A Review
Abstract
With the increasing adoption of multi-cloud infrastructures, ensuring security and scalability in DevSecOps pipelines has become critical. This review paper explores the integration of AI and ML techniques for automating threat detection, vulnerability assessment, and compliance monitoring in DevSecOps workflows. It examines intelligent orchestration tools, anomaly detection models, and predictive security analytics. The paper also addresses challenges related to data integrity, cross-cloud interoperability, and zero-trust architectures. Case studies and frameworks are analyzed to provide insights into best practices. Future directions include self-healing systems and AI-driven policy enforcement mechanisms.
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