As engineering teams try to innovate at a faster pace, being able to maintain the quality, performance and security of the applications become much more important. Organizations have found huge success in improving their overall product quality while ensuring security controls and compliance requirements are met. AI-driven automation solutions have aided engineering teams in automating key processes and leverage predictive analytics, to identify issues before they occur and taking corrective actions, improving the overall product quality. Predictive analytics has helped Operations teams perform real-time application monitoring and identify issues with application security, performance, and infrastructure thus improving overall operational efficiency. Implementing AI-driven DevOps solutions will help organizations accelerate in the present and adapt to changes easily in the future.
The article will provide ten ways in which organizations of any size can leverage the power of AI and automation for their DevSecOps pipeline and continuously improve their implementation as their business evolves.
1. Automate Your Quality Gates
Quality gates or check gates enable the decision making on whether a build can be promoted to higher environments. To achieve faster and continuous releases, automating the quality gates at each stage of the pipeline helps automate the Go-No Go decision of a build into various environments. Automated quality gates can include unit tests, automated code analysis, end-to-end tests based on the pipeline stage.