Optimizing Cloud-Native DevOps Practices with Machine Learning for Cost Efficiency and Agility

Authors

  • Dr. Raju Dindigala Professor & Head Department of Mathematics, JB Institute of Engineering & Technology, India, 20122102india@gmail.com Author
  • Dr Swarna Reddy Associate professor, Dept of CSE, Swarnaa@vjit.ac.in Author

Keywords:

Multi-cloud, DevOps, AI-powered security, Machine Learning, Predictive Analytics, Agile Development, Risk Management, Security Automation.

Abstract

In the rapidly evolving landscape of DevOps, where development and operations work together in an agile environment, the need for advanced security measures is increasingly vital. Multi-cloud environments, where organizations use services from multiple cloud providers, present unique security challenges due to their distributed nature and complex infrastructure. This paper proposes a framework for integrating AI-powered security tools within multi-cloud DevOps environments to enhance security while supporting agile development practices. The framework leverages predictive analytics, machine learning algorithms, and automated security protocols to manage risks, detect anomalies, and ensure the security of sensitive data across diverse cloud infrastructures. We also explore the integration of machine learning models for continuous monitoring and automated risk assessment, drawing from insights in "Leveraging Machine Learning Algorithms in QA for Predictive Defect Tracking and Risk Management" (Kothamali & Banik, 2019). The paper highlights key challenges, ethical considerations, and best practices for seamless AI integration in multi-cloud DevOps environments.

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Published

2024-03-19

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