Utilizing Predictive DevOps to Enhance Collaboration in 2024

Ed Frederici, CTO at Appfire, envisions a transformative shift in DevOps through Predictive AI in 2024. Learn how AI enhances collaboration and efficiency, shaping the future of software development.

With the explosion of AI in 2024, DevOps will grow to be more predictive, adjusting proactively to releases and updates. In particular, DevOps and SecOps will work together more closely through predictive DevOps.  

The rapid adoption of and advancements around artificial intelligence (AI) are revolutionizing how teams approach DevOps. AI is applied to every part of the software development and delivery process to automate and optimize operations seamlessly.

While automation and AI solutions have been used in DevOps pipelines for years, the recent surge in AI development means DevOps is primed to become even smarter and more predictive. It’s also creating opportunities for DevOps teams to be more collaborative, particularly with SecOps teams.

AI Makes the Software Development Process Smarter

The process for software releases and updates is fairly mechanical. Ideally, you build your software and commit it to a branch. From there, automation is kicked off, which will build the software and run automated tests against it, and if it passes, it is then pushed into the production environment.

 When you introduce AI, the software development process becomes much smarter. For instance, AI can identify when system utilization is lower and determine the best time of day for a release. During test runs, it can tell you if the reason for a test fail is meaningful enough to pause production or if the test fail won’t impact overall performance. It makes the process smarter, and AI also increases the speed and accuracy of these processes, freeing up developers to work on more critical and strategic projects, like brainstorming new features. 

See More: Using a Least Privilege Framework to Boost DevSecOps

The Impact of Predictive AI on DevOps

When we drill down into the different applications of AI across DevOps, predictive AI will make a big impact in 2024 — with DevOps becoming even more predictive and adjusting proactively to release and update software. Using statistical algorithms and advanced machine learning (ML) techniques, predictive AI is already helping to identify patterns from past events and uses that information to make predictions about future events. 

These predictive capabilities are transforming how DevOps teams engage with and analyze data. For example, AI can go through large data sets in minutes and identify patterns that might not be as obvious to the human eye, enabling teams to make more informed decisions.

 Here are three examples where predictive DevOps comes into play:  

• Code analysis: Predictive analysis can be used to identify parts of the code and historical data that might be susceptible to vulnerabilities or bugs in the future. This helps teams identify the underlying cause of the problem and take steps to prevent it from happening again.
• Compliance automation: The regulatory landscape is complex, and there are many regulations and compliance standards teams need to adhere to today. With predictive AI, compliance checks can happen automatically, ensuring teams comply with various security and regulatory requirements.
• Risk prediction: AI can identify and predict potential issues that could arise post-development. This allows teams to address issues before they affect users, enhancing reliability and visibility for SecOps teams. 

Predictive AI Increases Collaboration Between DevOps and SecOps

Tighter integration between DevOps and SecOps has been an ongoing challenge as security is often viewed as a barrier to Agile development, slowing down the development cycle and delaying releases. This integration has become tighter recently, but AI allows these two teams to become even more collaborative.

 With predictive AI systems taking on processes such as risk prediction, security analysis, and compliance automation, the SecOps teams will define the parameters around what’s acceptable and what’s not, what qualifies as potential weaknesses or vulnerabilities, and more. Because SecOps teams are defining the parameters, they can add specific guidelines or stipulations. For example, if they want to receive a notification every time there is a change or receive a security risk analysis, they can work that into the process. 

With these parameters in place, SecOps teams have more visibility into the nature of the code being released, making them more prepared. They know what threats to look for and what potential issues could arise. This also creates an open dialogue between the DevOps and SecOps teams and enables developers to take corrective actions with guidance from the security team.

What to Consider When DevOps is More Predictive

While integrating AI into DevOps – particularly predictive AI – provides many benefits, there are some considerations teams should keep in mind as they work through implementations.

Early on, a sense of trust in AI systems – and that what they produce is right – needs to be established. This requires time and dedication from every person involved. Team members need to understand how these systems work to accept that the results they produce are fully accurate. There is a level of education and, in some cases, subsequent training that needs to happen to build the right trust in the systems.  

AI bias is another area that should be thoughtfully considered. Most AI systems today learn from the information they are fed, which is usually a very small sample set. Because of this, results can be systemically prejudiced. In DevOps, this bias manifests as algorithmic bias, affecting everything from automated testing results to security vulnerability scans. If someone on the team is not checking the outputs, the quality of the results could degrade over time, and, in some situations, teams might not be aware of them until it’s too late.

Finally, these AI systems must be fully integrated into the DevOps pipeline. Engineers are triggering the AI system to write or run an automated task instead of this happening organically. Eventually, we’ll get to a point where the DevOps tools are stitched together with AI systems, but we’re a few years from that. For now, putting more trust into AI systems will play a big role in enabling them to integrate into workflows seamlessly.

AI has already made a huge impact on DevOps and will only continue. New advancements are emerging every day that will further shape the future of AI in DevOps. As teams prioritize speed, security, and stability, AI makes the process smarter and more predictive. This will enable DevOps to automate production cycles faster, more efficiently, and more securely.

How can Predictive AI reshape your DevOps strategy? Let us know on FacebookOpens a new window , XOpens a new window , and LinkedInOpens a new window . We’d love to hear from you!

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