Enhancing Security in DevOps by Integrating Artificial Intelligence and Machine Learning

Abstract

In modern software development and operations, DevOps (a combination of development and operations) has become a key methodology aimed at accelerating delivery, improving quality and enhancing security. Meanwhile, artificial intelligence (AI) and machine learning (ML) are also playing an increasingly important role in cybersecurity, helping to identify and respond to increasingly complex threats. In this article, we'll explore how AI and ML can be integrated into DevOps practices to ensure the security of software development and operations processes. We'll cover best practices, including how to use AI and ML for security-critical tasks such as threat detection, vulnerability management, and authentication. In addition, we will provide several case studies that show how these technologies have been successfully applied in real projects and how they have improved security, reduced risk and accelerated delivery. Finally, through this article, readers will learn how to fully leverage AI and ML in the DevOps process to improve software security, reduce potential risks, and provide more reliable solutions for modern software development and operations.