This article explores how integrating artificial intelligence (AI) and machine learning (ML) into DevOps can enhance software security. DevOps, a methodology that emphasizes automation and collaboration between development and operations teams, aims to accelerate delivery, improve quality, and enhance security. AI and ML play a crucial role in cybersecurity by enabling threat detection, vulnerability management, and authentication. The article discusses best practices for integrating AI and ML into DevOps, including automated code review, code analysis, and unit testing. It also presents case studies that demonstrate how these technologies have improved security, reduced risk, and accelerated delivery in real-world projects. The article highlights the importance of security in DevOps, noting that while DevOps focuses on fast delivery, this can introduce security vulnerabilities. Integrating security measures into the DevOps process ensures continuous monitoring and feedback to quickly detect and respond to potential threats. Compliance requirements and regulations also impose higher standards on data security, making it essential to ensure that security is compliant with regulations. Maintaining security helps reduce costs, as it is often more expensive to fix security breaches than to prevent them. The article also discusses challenges in DevOps, including a shortage of skilled professionals, uncertainty about the development path, and technical challenges in implementing DevOps. These challenges highlight the need for continuous improvement and cross-team collaboration to ensure security, compliance, and reliability in software delivery. As technology continues to advance, AI and ML will play an increasingly important role in DevOps, helping to automate threat detection, vulnerability management, and security audits. The widespread adoption of cloud-native and container technologies will introduce new security considerations, requiring adaptive security measures. Ultimately, DevOps security will require more cross-team collaboration and continuous improvement to ensure the security, compliance, and reliability of software delivery.This article explores how integrating artificial intelligence (AI) and machine learning (ML) into DevOps can enhance software security. DevOps, a methodology that emphasizes automation and collaboration between development and operations teams, aims to accelerate delivery, improve quality, and enhance security. AI and ML play a crucial role in cybersecurity by enabling threat detection, vulnerability management, and authentication. The article discusses best practices for integrating AI and ML into DevOps, including automated code review, code analysis, and unit testing. It also presents case studies that demonstrate how these technologies have improved security, reduced risk, and accelerated delivery in real-world projects. The article highlights the importance of security in DevOps, noting that while DevOps focuses on fast delivery, this can introduce security vulnerabilities. Integrating security measures into the DevOps process ensures continuous monitoring and feedback to quickly detect and respond to potential threats. Compliance requirements and regulations also impose higher standards on data security, making it essential to ensure that security is compliant with regulations. Maintaining security helps reduce costs, as it is often more expensive to fix security breaches than to prevent them. The article also discusses challenges in DevOps, including a shortage of skilled professionals, uncertainty about the development path, and technical challenges in implementing DevOps. These challenges highlight the need for continuous improvement and cross-team collaboration to ensure security, compliance, and reliability in software delivery. As technology continues to advance, AI and ML will play an increasingly important role in DevOps, helping to automate threat detection, vulnerability management, and security audits. The widespread adoption of cloud-native and container technologies will introduce new security considerations, requiring adaptive security measures. Ultimately, DevOps security will require more cross-team collaboration and continuous improvement to ensure the security, compliance, and reliability of software delivery.