Given the sophistication of today’s attacks, cyber security has taken on more importance in the digital world. Conventional security measures are no longer sufficient; instead, a more sophisticated and reactive reaction is needed. In this regard, handling this escalating threat seems to depend on the integration of machine learning (ML) and artificial intelligence (AI). But even with their great efficacy, there remains a growing issue: integrating different data sources and technologies so that the overall security of the system model is maintained. With a special emphasis on Open Extended Detection and Response (Open XDR) technology, this paper provides a thorough analysis of the integration of AI and ML in cyber security. A thorough literature review, which looks at the interactions and functionality of several cyber security components, is the methodology employed. In the context of AI and ML, the paper also examines the function of Active Directory, a directory service for Windows domain networks, and the procedure known as log forwarding, which involves sending log files to a central server for analysis. The study explores the evolution of AI and ML, highlighting their applications in cyber security for sophisticated data processing, threat detection, and pattern identification. It examines how machine learning approaches, such as supervised learning (where the model is trained on labeled data) and unsupervised learning (where the model learns from unlabeled data), support cyber security protocols. The paper emphasizes Open XDR’s importance as a breaking invention that combines information from several sources to provide thorough security analysis.