This article explores the integration of artificial intelligence (AI) and machine learning (ML) into DevOps practices to enhance security in modern software development and operations. DevOps, a methodology that combines development and operations to accelerate delivery, improve quality, and enhance security, is complemented by AI and ML, which can automate testing, monitoring, and troubleshooting. The article covers best practices for using AI and ML in security-critical tasks such as threat detection, vulnerability management, and authentication. It also presents case studies demonstrating successful applications of these technologies in real projects, highlighting improvements in security, reduced risks, and accelerated delivery. The importance of integrating security measures into DevOps is emphasized, as inadequate security can lead to data breaches, malicious intrusions, and reputational damage. The article concludes by discussing the future of DevOps security, including the role of AI and ML in automating threat detection and vulnerability management, and the need for adaptive security measures in the cloud-native era.This article explores the integration of artificial intelligence (AI) and machine learning (ML) into DevOps practices to enhance security in modern software development and operations. DevOps, a methodology that combines development and operations to accelerate delivery, improve quality, and enhance security, is complemented by AI and ML, which can automate testing, monitoring, and troubleshooting. The article covers best practices for using AI and ML in security-critical tasks such as threat detection, vulnerability management, and authentication. It also presents case studies demonstrating successful applications of these technologies in real projects, highlighting improvements in security, reduced risks, and accelerated delivery. The importance of integrating security measures into DevOps is emphasized, as inadequate security can lead to data breaches, malicious intrusions, and reputational damage. The article concludes by discussing the future of DevOps security, including the role of AI and ML in automating threat detection and vulnerability management, and the need for adaptive security measures in the cloud-native era.