One such area where artificial intelligence is making quite a mark is DevSecOps. We are going to take a quick minute here to tell everybody what is DevSecOps. DevSecOps is the practice of integrating security testing at every stage of the software development process. This would include the tools & processes which facilitate collaboration among developers, security specialists, operations team members, etc. to ensure that the final product, the software, or the application is both efficient and secure. To put it in a very simplified form, DevSecOps adds the element of security to the DevOps culture, weaving it into the process itself, instead of adding measures as an afterthought after the software or the application has been produced.

Going back to our topic for the day, how is artificial intelligence reshaping the roles of a developer in the DevSecOps environment?

The recent Seventh Annual Global DevSecOps Report by GitLab has found that artificial intelligence and machine learning in the software development workflow have found promise but challenges like the complexity of the toolchain and concerns about security continue to remain. According to this research, about 65% of the developers are not using artificial intelligence and machine learning in their code-testing efforts or have plans to do this in the next three years. This is quite a huge step in the automation of the software development process.

This survey by GitLab covers over 5,000 IT leaders, CISOs, and developers across various sectors including financial services, automotive, healthcare, telecommunications, and information technology. The survey focused on understanding the successes, challenges, and priorities for the DevSecOps implementation.

One of the very interesting things this report found, as we mentioned just now, was how artificial intelligence and machine learning is being adopted in the software development process. In 2022, only 55% of the developers were using AI/ML to check their code, while this number is now up to 62%. Also, last year, only 39% of the developers were using bots in the testing process; this number is up to 53% this year.

Some of the top skills, Git Lab reports are considered essential for security professionals are:

1. Artificial intelligence and machine learning

2. Soft skills

3. Subject matter expertise

4. Metrics & quantitative insights

This definitely indicates that all-round expertise and skill set are essential for a successful career in security and overcoming security challenges they would encounter in their work.

To make the most of emerging technologies like artificial intelligence and machine learning in the security and DevSecOps space, enterprises must invest in the right training and tools for their teams to leverage the potential of these technologies in the software development and security workflow processes.