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How Can You Combine DevOps and Automation for Robust Security? How Can You Combine DevOps and Automation for Robust Security?
In this article, we will be taking a look at how the organizations can leverage the potential of DevOps and automation in order to... How Can You Combine DevOps and Automation for Robust Security?

In this article, we will be taking a look at how the organizations can leverage the potential of DevOps and automation in order to evolve their business.

[Related Article: 5 DevOps Challenges To Overcome To Gain Productivity]

As the engineering teams are trying to innovate at a quicker and faster pace, they are also able to maintain the quality, security, and performance of the applications. Enterprises are seeing an enormous effect on by and large item quality while guaranteeing security controls and consistence necessities. AI-driven mechanization arrangements help to help to build groups in computerizing key procedures so as to use prescient investigation and identify the issues by improving the general item quality.

With the assistance of predictive analysis, the operational groups are performing continuous application observing by distinguishing the issues with application security, execution, and foundation to improve the general operational productivity. Enterprises have started to implement the DevOps solutions which will help to accelerate the system changes with ease. 

 

  • Automating the Quality Parameters

Quality checks or quality gates permit the basic leadership whether you can be elevated to higher conditions or not.  In order to achieve faster and continuous deployment, it is crucial to automate the quality gates at each stage of your DevOps lifecycle for the unit tests, automated code analysis, and end-to-end tests.

  • Performance Engineering

Performance testing and engineering are defined as one of the often-ignored areas during DevSecOps. Performance tests are a part of the DevOps lifecycle and help to detect the bugs from an earlier stage of the code to perform efficiently. Multiple complex performance scenarios can also be done during the pre-development phase to ensure a smooth performing application. 

  • LifeCycle Security

All of the security vulnerabilities can cause potential loss to the businesses and can also low down the brand value. By making security analysis and testing some portion of your lifecycle, the engineers guarantee to pursue a fitting coding practice by not infusing security issues and making security an enormous need during their item design. 

  • Continuous Testing

The term continuous testing is often misunderstood as just automating the test. Instead, it is an automated in-sprint during the development of the features. The test automation approach is enabled in early mornings for faster automation and test execution to provide faster feedback. If the test automation runs for more minutes than it indicates a longer release. 

  • Automate the compliance Requirements

For multiple enterprises, compliance requirements are its infrastructure or an application is equally important. It is vital that a holistic approach is taken during the automation phase so as to include all the compliance requirements as a part of the automation. The automated compliance checks need to ensure that all the criteria are met and the app features are released into production. The automated compliance checks need to guarantee that every one of the criteria is met and the application highlights are discharged into generation.

  • Infrastructure-as-code

Multiple enterprises are investing heavily in the infrastructure in terms of both the data centers or cloud providers. Enterprises are largely investing in the configuration management tools for creating an app to leverage the power of these tools and cloud providers. By doing so, the testing teams ensure that the environment creation is consistent, reliable and repeatable which can help them to quicker rollbacks and deployments.

  • Monitor and Analyze

Once an application gets delivered into production, it’s time to monitor the app performance and checks the security standards. AI-oriented production monitoring allows predictive analysis to identify the issues before they even occur in production. Some of the recent AI-driven tools support the cloud infrastructure optimization which is based upon the app loads without a need for human input. 

  • Get Constant Feedback

AI-based chatbots are building inroads into a customer support tool and these chatbots help to provide rapid solutions to the customers by making sense of the customer feedback to create defects in planning tools for fixing the issues during the implementation phase. All of such an AI-driven monitoring approach helps to visualize the user behavior that can be further used to enhance the app features in the form of feedback. 

Wrap Up

[Related Article: Why Use Continuous Intelligence in DevOps/DataOps]

Here, we come to the end of the article. Leveraging data to make informed decisions are driven by artificial intelligence that helps enterprises to adapt in the future with the endless changes thereby understanding user behaviors to enhance the app delivery. Till then—keep learning!


Author Bio: 

DevOps and Automation

Kibo Hutchinson working as a Technology Analyst at Tatvasoft UK. She has a keen interest in learning the latest practices of development so she is spending her most of the time on the Internet navigating the unique topics and technology trends. Her technical educational background, combined with a know-how of content marketing, gives her an edge over others in a variety of blog posts. You can visit here to know more about her company.

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The Open Data Science community is passionate and diverse, and we always welcome contributions from data science professionals! All of the articles under this profile are from our community, with individual authors mentioned in the text itself.

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