With advancements being made in the cloud infrastructure wherein the users get access to utilize the cloud resources by storage allocation, this process might at times put the user data at risk. In such a scenario, it is important to authenticate the user and further allocate cloud resources to the respective person. This makes the process of breach difficult for non-users. The architecture used for the same is termed as zero trust security wherein all the respective users within or outside the organization must authenticate so as to gain access to the allocated resources. A continuous process of configuration and validation takes place on the networking edge so that overall security is maintained. The presented research covers how threat detection and response capabilities are enhanced when AI and ML are included into several cyber security systems including IDS. It discusses the potential and problems that AI and ML bring to the cyber security space, with an emphasis on data protection, ethical dilemmas, and the need for continual professional development in this quickly developing industry occurs. The efficacy of combining AI and ML with these cyber security tools within the System Log Management is affirmed in the paper’s conclusion. The overall cyber security resilience, response effectiveness, and threat detection are all greatly improved by this combination. The goal is to offer a thorough grasp of cyber security technologies as they exist today, their interactions with AI and ML, and predictions for future advancements in the technical field.