Artificial intelligence truly enables DevOps teams to “do more with less.”
And that's just one way to improve your organization, says Sacha Labourey, co-founder and chief strategy officer at CloudBees, an enterprise software company based in San Jose, California.
But that doesn't mean there aren't concerns. I talked to Mr. Labourie. chief executive officer Learn how IT leaders and their DevOps teams can make the most of the ever-evolving world of AI.
How is AI impacting the DevOps industry?
AI has a huge positive impact on DevOps and, in turn, the business by enabling DevOps teams to get software into production faster than ever before. AI will perform many mundane and repetitive tasks that engineers currently perform.
Additionally, AI has the power to democratize software development. Junior developers will be able to take on more and more low-level development tasks, and senior engineers will be able to focus on strategic, high-impact initiatives.
AI lowers the barrier to entry into software development and raises the bar for innovation that can be more easily achieved by experienced developers. With a shortage of talented development talent, AI truly enables his DevOps teams to do more with less.
Specific areas where generative AI can positively impact DevOps teams include application code management, release management, testing, cybersecurity, and improving monitoring and uptime.
What are the aspects of AI that IT leaders need to be aware of now? What are the potential benefits of using AI?
It is clear that the pace at which AI is already being used to write code poses some challenges for software development. Much of the code written by AI is generated by general-purpose, large-scale language models like those that power ChatGPT. These LLMs are trained using code aggregated from across the web, regardless of quality.
It is not surprising that the generated code may be flawed or contain vulnerabilities. Therefore, it is important for DevOps teams to continue to review code, regardless of whether it is written by machines or humans. However, his LLM of a specific domain trained using carefully selected code will produce higher quality programs, thus increasing the overall quality of code produced by the machine in the long run. To do.
Similarly, LLMs built into DevOps platforms democratize software engineering. IT administrators, for example, don't have to write and test scripts; they can simply request them through a natural language interface. It's only a matter of time before LLM becomes operational across DevOps workflows.
It's hard to think of a DevOps job that isn't influenced by AI to some degree or another. DevOps teams should welcome these advances, not resist them. At the end of the day, DevOps is above all a ruthless approach to automation in the name of increasing productivity. AI is just the latest in a series of advances that have enabled DevOps teams to change the world.
In fact, DevOps teams are empowered not to fear AI, but to operate it securely in ways that will ultimately enable applications to be built and deployed at previously unimaginable levels of scale, resiliency, and flexibility. There is an obligation.
There are an ever-increasing number of compliance standards that software teams must adhere to, but what impact do you think this will have?
Compliance burden is definitely the second big trend. Compliance audits are one of the most stressful events DevOps teams experience. Organizations spend months preparing to pass an audit, but failing it can result in hefty fines, penalties, and even failed software deployments.
The more applications an organization has within its portfolio, the greater the challenge. The number of obligations and regulations that need to be addressed is increasing, with each new regulation becoming more stringent.
Many organizations also have applications that must pass audits of multiple standards. Examples include standards such as the Payment Card Industry Data Security Standard (PCI DSS) and the Health Insurance Portability and Accountability Act (HIPAA). Although many of these obligations have a common core subset of requirements, each also has its own drawbacks.
In fact, compliance has become a continuous series of processes and workflows that never end. The burden of proving compliance can actually slow down software delivery.
How can organizations keep compliance in mind while ensuring it doesn't get in the way of software delivery?
Organizations of all sizes are beginning to implement best practices that allow them to manage compliance as an ongoing process.
Rather than trying to manage audits as a series of independent events, organizations are approaching compliance requirements as codified controls embedded within larger DevSecOps workflows. This brings “continuous compliance,” or compliance, into the workflow and eliminates all the manual processes and workarounds that many teams currently work on.
As DevSecOps workflows evolve and mature, it's only a matter of time before more organizations embrace continuous compliance. The essence of DevOps is to automate manual processes to accelerate software deployment.
As DevOps workflows expand to address security and compliance requirements, the ultimate goal must be to build and deploy secure applications without slowing down application development that fosters the pace of innovation. there is.
Continuous compliance complements DevOps by ensuring that security best practice policies are embedded in software development and deployment processes, enabling organizations to build more secure, compliant, and reliable software while maintaining rapid, iterative development cycles.
