Identity has become the front door to all our online experiences, and the security perimeter for all our data. However, there's been no easy way to handle scenarios that involve a combination of human and machine access. The problem gets worse when you have a stream of activity spanning a wide array of apps and backend systems. This problem surfaces in two use cases that concern the DevOps toolchain: gaining visibility into data and automating DevOps actions. If you're an engineer, how many times has someone left your organization and for months or years there are still Jira entries with their name on them?

DevOps is the combination of two terms'Development' and'Operations' and deals with the automation of tasks. It asserts the automation and evaluating of all the steps of the software delivery process, making sure that every task is conducted quickly and efficiently. However, it does not neglect human responsibilities, it encourages DevOps service companies to create repeatable processes that reduce inconsistency and improve efficiency. In such a scenario, machine learning and AI are ideal fits for DevOps as they can process enormous information and help conduct tedious tasks, hence allowing the IT department to concentrate more on important and targeted work. AI can learn patterns, giving solutions, and anticipate future problems.