The use of artificial intelligence (AI) and machine learning (ML) is fundamentally changing the way we think about DevOps. Most notably, it is delivering a new form of DevOps that recognizes the need to have systems that are intelligent by design and underpinned by comprehensive security (DevSecOps). For many, this will be the crucial next step if DevOps is to shorten the software development lifecycle for all connected intelligent systems, ensuring the continuous delivery of secure high-quality software.
By now, most organizations understand DevOps is a substantial discipline that they must adopt – according to Deloitte, organizations adopting DevOps see an 18%-21% reduction in time to market. By breaking down the silos between business and IT operations, DevOps can ensure consistent levels of productivity, efficiency and service delivery, all of which hold weight in these times of heightened uncertainty.
To put it simply, DevOps can help businesses compete in already congested marketplaces. Through a foundation of continuous integration (CI) and continuous delivery (CD), organizations can ensure the customer receives the product they demand in the fastest time possible, while mitigating any elongated frustrations experienced from a lack of harmony between systems engineers and operations teams. Incorporating AI and ML into that DevOps strategy will take things to the next level.
AI and ML Are the Next Evolutionary Step for DevOps
Today’s companies are data-driven and being built as digital platforms. However, just 28% of organizations are currently succeeding in their digital transformation journeys. Increasingly, corporations are looking at what AI and ML can do to help them realize their transformative ambitions.
Both the AI and ML markets are expected to experience huge growth over the next three to four years. Analyst firm IDC, predicts worldwide spending on AI systems will reach a href=”https://www.marketresearchfuture.com/reports/machine-learning-market-2494″>42.8% by 2024.
What AI and ML reveals for these companies, and the increasing value of what Al, ML and devices can do together, are changing the way organizations view the world as they digitally transform. Coupling AI and ML with DevOps will lead to a significant shift in what DevOps gets involved with.
Primarily, it sets DevOps front and center of an organization’s wider digital transformation ambitions. If digital companies run on living data, then the development of these intelligent systems is an enticing environment for DevOps to prove its wider value to the organization like never before.
Security Takes the Center Stage
According to a TechTarget survey of key IT decision makers, cybersecurity and risk management were found to be the number one area for spending this year, with more than 53% of respondents saying they saw budgets increase in this area. And security is a crucial element that any organization employing a DevOps strategy should be considering.
DevSecOps is the simple premise that everyone involved in the software development cycle is responsible for security. In fact, the saying “security is everyone’s responsibility,” is a mantra that has been repeated often, and just as often ignored. Embedding security within every part of the development process, ensures security and compliance monitoring tools keep pace with the speed and agility that DevOps offers. It is a common understanding that security is the biggest block to the rapid and seamless development and deployment of systems, as security solutions have not traditionally been built to test and code at the speed DevOps requires.
Ultimately though, more automation from the start, reducing the need for manual configuration from security architect is where DevSecOps shines. This reduces the chance of misadministration and occurring faults, which can lead to downtime, and potential breaches or attacks. Quite often, the best ways to secure your organization is through simple hygiene factors, such as regular patching and software updates.
The reality is that every organization employing DevOps should really be approaching it as DevSecOps. Security is of ever growing importance to each and every organization and, through empowering it with AI and ML processes, it can be enhanced too, simplifying the processing of data to easily identify threats or potential vulnerabilities in the security makeup.
Reaching Beyond Human Capability
The future will be made up of connected intelligent systems that span the intelligent edge, all the way to the cloud. The expectations of these systems are based on the lifecycle of a typical mobile application with constant feature enhancements. But, how do you know what to deliver next, especially when things are moving so quickly, and as developers are inundated with information and data from a number of different sources? This data deluge can be powerful, but it can also be overwhelming.
Crucially, this is where the power of AI and ML comes into play. AI and ML will aid developers in making sense of the information housed across various data warehouses. In DevSecOps, there has always been an approach to automate everything, and AI and ML will be instrumental in automating the analysis and processing of data – a task that is now far beyond the capabilities of humans.
Incorporating AI and ML will enable developers to better understand and use the data at hand. This will see developers understand not only the error, or the occurrence of a fault, but the detail of what happened in the run up to the fault – vastly reducing the chances of incurring that fault again. AI and ML will also be responsible for enabling the transformation from diagnostics to prognostics across any and all tools and systems, making it easier for developers to anticipate, identify and resolve faults or errors.
Increasingly, we’ll see organizations benefit and drive value from real-time insights. They’ll do this through AI and ML frameworks deployed on active systems to deliver optimizations based on real-time development, validation and operational data. This level of real-time actionable insight bolsters DevSecOps’ ability to achieve CI and CD, through improved, reduced mean time to resolution, easing the impact on operations and, ultimately, enhancing the end-user experience. It remains to be seen if incorporating AI and ML into the DevSecOps cycle will achieve the delivery of self-healing systems that can detect and resolve issues without the need for human intervention. With true AI, however, this might well be possible.
The benefits and potential use cases of AI and ML are widely acclaimed, and while we are still some way from seeing their full potential, the capabilities they currently possess are revolutionizing many sectors. The fact is, if DevSecOps is to continue to keep pace with the vastly growing digital data economy, then it will become reliant on advanced technologies such as AI and ML. Data is powerful if used correctly, but with so much of it to process and analyze, that can be a challenge.
A move toward secure automated processes is the progressive step that many organizations need to consider if they are to realize their digital transformation ambitions. Organizations cannot continue to do things the traditional way and expect to get the results that the new world is looking for.