Securing the DevOps Lifecycle: A Comprehensive Review of DevSecOps Frameworks, Software Supply Chain Security, and Automated Threat Detection

Abstract

The rapid adoption of cloud computing, microservices, containerization, and continuous software delivery has increased the complexity of securing modern software development and deployment environments. Traditional security approaches, which often introduce testing and validation at the final stages of development, may fail to identify vulnerabilities early enough to prevent operational risks. DevSecOps addresses these limitations by integrating security controls, automated testing, compliance validation, and continuous monitoring throughout the software development lifecycle. This review paper presents a comprehensive analysis of DevSecOps principles, architectures, implementation strategies, and emerging security technologies. It examines the integration of static application security testing, dynamic application security testing, software composition analysis, container image scanning, infrastructure-as-code security, and automated vulnerability management into continuous integration and continuous delivery pipelines. Furthermore, the paper explores software supply chain security, software bills of materials, secrets management, identity and access management, and zero-trust principles for protecting distributed development environments. Particular attention is given to securing Kubernetes clusters, cloud-native applications, APIs, and third-party dependencies against evolving cyber threats. The review also investigates the potential of artificial intelligence and machine learning for intelligent vulnerability prioritization, anomalous activity detection, threat prediction, and automated security remediation. In addition, it discusses implementation challenges involving toolchain integration, false-positive findings, compliance complexity, developer awareness, operational costs, and the balance between security assurance and deployment speed. The paper identifies research opportunities in adaptive security automation, AI-assisted threat analysis, policy-as-code, and continuous risk assessment. Finally, it proposes future research directions for developing scalable, resilient, and security-aware DevOps ecosystems that support reliable software delivery without compromising confidentiality, integrity, and availability.