Leveraging AI and Automation for Successful DevSecOps
Injecting security protocols within a DevOps environment can be made much more efficient and effective with AI.
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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.
2. Performance Engineering Is a Key Factor
One of the often-ignored areas during 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 complex performance scenarios such as load testing can be introduced in pre-production environments, but shifting left with performance engineering ensures the application is developed with performance in mind.
3. 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.
4. 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.
5. Automate Compliance Requirements
For many large enterprises, compliance requirements both in terms of infrastructure as well as application are very important. It is necessary that a holistic approach is taken during automation, to include compliance requirements as part of automation. The automated compliance checks should ensure all criteria are met and application/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. Organizations also invest heavily in configuration management tools to create infrastructure. It is important to leverage the power of these tools and cloud providers and manage infrastructure-as-code, and version it just as application code would be. This will ensure that environment creation is consistent, repeatable and reliable and would help in quicker deployments and rollbacks.
As organizations mature in their DevSecOps implementation, managing the end to end CI/CD pipeline as code provides advantages. Pipeline-as-code will enable various teams to trigger and manage their deployments better and help track the deployments better.
8. 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. If your pipeline cannot fail fast, you cannot achieve continuous delivery speed.
9. Monitor and Analyze
Once an application is deployed into production, being able to monitor the application for performance and security is important. AI-driven production monitoring enables predictive analytics to identify issues before they occur in production. Some of the most recent AI-driven tools also support in optimizing cloud infrastructure based on application loads, without the need for human intervention.
10. 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-driven monitoring also helps understand user behavior which can be used as feedback for enhancing application features.
Leveraging data to make informed decisions, driven by AI helps organizations be adaptable to future changes while implementing DevSecOps, understand user behaviors and enhance application delivery. Continuous improvement is key to getting better at DevSecOps implementation and AI- driven tools and automation become the enablers in the process.
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