IT has dreamed about autonomous IT networks for a decade. While no vendor has brought such a solution to market yet, the pairing of two new technologies — SASE and AIOps — accelerates innovation, creating a new breakthrough for the future network of all companies.
When AI is applied to the problems of IT operations, it’s referred to as AIOps. Machine learning, behavioral analytics and predictive analytics are used to observe and evaluate performance across the network, security and cloud applications. Using data science, AIOps visibility engines recommend ways to optimize performance, and the engine has the potential to act nearly instantly on its own recommendations. When it does, this is known as closed-loop automation, and this marks the key difference between partial automation and a fully automated AIOps system or autonomous network.
With the ability to automate manual processes that have plagued companies for decades, AIOps is expected to completely revolutionize IT the same way that desktop computing and the internet did in years past. When humans can’t crunch network data fast enough nor pinpoint the root cause of an application outage in today’s multi-cloud environments, AIOps steps in with an answer and a prescription to put the right bandwidth in the right places at the right time. It can even predict when network and security performance will not meet expectations. That helps explain why, as Gartner, Inc. notes (and I’ve quoted previously), “organizations that automate more than 70% of their network change activities will reduce the number of outages by at least 50% and deliver services to their business constituents 50% faster.”
For instance, AIOps will tell you that videoconferencing continues to grow and that consumption will saturate your network within the next three months. Moreover, it will show you where to boost bandwidth, which network path those applications should take and how to revise application policies and settings to ensure a high quality of service. With closed-loop automation, network management changes are made for you.
But all of AIOps’ greatness can only be achieved when its engine has what it needs.
AIOps engines are powerful tools, but without the right environment, IT investments fail miserably. The challenges with AIOps are threefold:
This is where secure access service edge (SASE) solutions can make the perfect pairing for AIOps because it solves all three problems with one “stone.” This new category of solutions combines software-defined wide area networks (SD-WAN) and security capabilities in one service. When these converged solutions act as the underlying technology platform for AIOps, the AI engine has everything it needs.
In order for AIOps to have the transformational impact IT leaders are looking for, it needs to observe and engage with a wealth of data streams, including information about the network, security, cloud services, applications and users.
However, pairing SASE and AIOps doesn’t solve every problem. There is still much work to be done. The engine needs time to learn the behavior of its new environment and fail at its own attempted problem-solving. IT managers need to take time to coach it. Smart networks may know to alert to hardware failures, but they will still need to be taught finer skills. For example, it may not understand what a surge in website traffic means during December and how to adjust accordingly. Training periods are key for making the AI engine more effective at its job as well as for building trust between humans and machines. After all, automation isn’t possible if AIOps isn’t coached and trusted to act alone.
As SASE unlocks the power of AIOps, a new breakthrough in autonomous networking is unfolding. The self-managing, self-healing networks that industry leaders have forecasted for years are revealing themselves. It’s time for forward-leaning IT executives to start paying attention because autonomous networks should be hitting the market in the first half of this decade.
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