The article will describe ten ways in which companies of any size are able to leverage the power of AI and automation for their DevSecOps pipeline and continuously enhance their implementation as their business evolves.
- 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.
- Performance Engineering Is a Key Factor
One of the most neglected areas in DevSecOps is performance testing and engineering. Performance tests should be made part of the pipeline from the early stages so that issues can be identified earlier and code can be engineered to perform better.
More difficult performance cases such as load testing may be introduced in pre-production, but shifting left with performance engineering makes sure the application is developed with performance keeping in mind.
- Make Security a Part of Your Pipeline
Security vulnerabilities identified in production cause huge losses to businesses and cause a dent to the brand value, especially for enterprises. Making security analysis and testing part of the DevSecOps pipeline ensures developers follow coding best practices to not inject security issues and make security a priority during their product architecture and design phases. Security scans must run as a job in the pipeline, incrementally analyzing the code and reporting issues.
- Mature from Test Automation to Continuous Testing
Continuous testing is often misunderstood to be just automating the tests. What is important though, in achieving continuous testing, is to be automating in-sprint as features are developed. The test automation approach should enable in starting early, automating faster, and executing tests in parallel to provide quicker feedback. If the test automation runs more than 15 minutes, it would mean the release of a feature would take that much longer.
- Automate Compliance Requirements
For many big organizations, compliance requirements both in terms of application as well as infrastructure are very significant. It is mandatory that a holistic method is taken during automation, to introduce compliance requirements as part of automation.
The automated compliance tests should make sure all criteria are encountered and features can be released into production. The automated compliance checks can be as simple as a set of tests designed specifically to check for compliance, to as complex as a framework to automate the infrastructure compliance.
Many organizations invest heavily in the infrastructure both in terms of data centers or cloud providers. Companies also capitalize on configuration management tools to build infrastructure. It is essential to leverage the impact of these tools and cloud suppliers and manage infrastructure-as-code and update it just as application code would be. This will make sure that environment creation is constant, repeatable and consistent and would use in faster deployments and rollbacks.
As companies mature in their DevSecOps execution, managing the end to end CI/CD pipeline as code provides benefits. Pipeline-as-code will enable various teams to trigger and manage their deployments better and help track the deployments better.
- Deliver at Speed
Once all the pieces of the CI/CD puzzle are put together, being able to track the speed of delivery, from commit to release, is important. To be able to release every day, or multiple times a day requires the various stages of the pipeline to be optimized for faster feedback and completion.
- Monitor and Analyze
Once the software is deployed into production, being capable to monitor the software for performance and security is essential. AI-driven production monitoring enables predictive analytics to identify issues before they occur in production. Most recent AI-oriented tools also work in optimizing cloud infrastructure based on application loads, without the need for human intervention.
- Leverage Feedback to Get Better
AI-driven chatbots are making inroads into customer support tools. These chatbots help provide quick answers to customers and try to make sense of customer feedback to automatically create defects or user stories in planning tools that can be picked up by engineering teams to fix issues or for implementing enhancements/features. AI-oriented monitoring also useful in understand user behavior which may be used as feedback for improving application features.