AI-Enhanced DevOps: Transforming Cloud-Based Software Development with Advanced Automation and Security
Abstract
As cloud-based software development continues to evolve, the integration of artificial intelligence (AI) and machine learning (ML) has become increasingly important in automating processes and enhancing security. This paper explores the intersection of AI-driven automation, DevOps practices, and cloud infrastructure, examining how AI can significantly improve the efficiency and security of DevOps pipelines. Specifically, we focus on how AI-enabled tools can optimize CI/CD workflows, provide advanced anomaly detection, and automate repetitive tasks, while also addressing the emerging cybersecurity challenges in cloud-based environments. By referencing the work of Banik & Kothamali et al. (2024) on strengthening cybersecurity through machine learning in edge computing, we draw parallels on how ML can be leveraged in DevOps for real-time threat detection, vulnerability management, and adaptive response. Through case studies and empirical analysis, we highlight the transformative potential of AI in DevOps and provide recommendations for future integration strategies.