IT has dreamed about autonomous IT networks for a decade. While a single all-in-one solution may not exist yet, the pairing of two innovative technologies — SASE and AIOps — points toward potential breakthrough innovation in network management.

AIOps Automation: Predictions, Prescriptions, Protections


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, marking 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 means that organizations that automate more elements of their network management processes are able to deliver services faster—and suffer fewer outages.


For instance, AIOps may tell you that video conferencing bandwidth needs are growing and that consumption will saturate your network within the next three months. Moreover, it can show you where to boost bandwidth, the optimal network path certain applications should take, and how to adjust application policies and settings to ensure a high quality of service. With closed-loop automation, network management changes would be made for you.


But the full potential of AIOps’ can only be realized when its engine has what it needs.

AIOps Challenges: Data, Control, Infrastructure


AIOps engines are powerful tools, but without the right environment, IT investments fail miserably. The challenges with AIOps are threefold:

  • It’s only as smart as the data it is given — AIOps must first observe before it can engage and then act.
  • It needs access to control panels and tools so it can modify configurations across the IT infrastructure — in the enterprise edge, the network edge and in the cloud.
  • AIOps needs a modern infrastructure in order to thrive.

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 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.

Driving The Future: How SASE And AIOps Work Together


  • Access to data: the ability to observe and engage. Because SASE solutions unite network and security analytics into one dashboard, they help provide end-to-end visibility. AIOps has one source of truth for configuration data and network traffic flows as well as network-related security data. They can also bring insights into user analytics, shadow IT applications, and historical data showing how issues were resolved successfully in the past.

  • Access to controls: the ability to act. AIOps engines would be nothing more than alerting systems and trouble ticket generators without closed-loop automation and the ability to act. As such, the engine must be connected to the network and security systems so it can act on its own advice. SASE’s unified controller allows AIOps to drive changes across the board. This simplicity is due in large part to the centralized management features of SD-WAN, which serve as the common operating system giving AIOps the self-service control it needs.

  • Agile infrastructure: the ability to thrive. Predictive analytics won’t work in an environment designed for an analog world; fragmented infrastructures will have a debilitating effect. SASE solves for this with an IT architecture that is cloud-based, virtualized and governed by software-defined principles for the type of real-time responsiveness expected today. SASE also means fewer silos. Before SASE, many IT organizations were using stand-alone solutions, applying AI to the network and security separately and then trying to tie all those insights together after the fact. Now, there is far less need for integration. With SASE and AIOps together, IT leaders don’t have to worry about connecting all the links in the chain.

However, pairing SASE and AIOps doesn’t solve every problem. 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. Learn more today.