Threat actors are using artificial intelligence (AI) to enhance their skills, improve their breach success rates, and gain access to organizations’ data. As such, companies need to do the same. They must adapt their approach to IT operations (ITOps), utilizing AI to ensure that they are well equipped to withstand the latest threats and protect their organization.

Moreover, IT environments and networks have become increasingly complex, with factors such as hybrid and multi-cloud systems creating diverse data silos and the need to be able to link disparate sources of data across security and network domains. At the same time, the volume and scale of data that is being generated has grown exponentially, making it more challenging to monitor and respond to any potential threats.

Cloud complexity is creating network challenges

As a result, many IT teams are struggling with daily operations, incident management, hardware failure, human errors and an overall lack of visibility, as well as being unable to gain a proactive understanding of the health of their network. Driven by the cloud, networks incorporate large numbers of connected devices and IoT sensors and carry vast amounts of traffic, increasingly to and from cloud applications. All of which makes network monitoring more important and more challenging than ever before.

Organizations are turning to AIOps and tools that can proactively observe, predict and respond to threats on the network as they occur. At the same time, such tools are providing meaningful insights and a clear understanding of the network status to drive improved performance and allow for proactive forecasting, with context-based intelligent anomaly detection to prevent issues.

AIOps for optimal user experience

AIOps tools enhance efficiency by seamlessly integrating with IT management tools, enabling proactive issue identification and streamlining IT management processes.  But more than that, they optimize an organization’s network by improving the performance, efficiency, and dependability of its network resources to ensure optimal user experience.

When it comes to infrastructure, many organizations now rely on SD-WAN – software-defined wide area network – to manage and optimize data traffic across different types of networks efficiently. SD-WAN is an effective way to connect the organization and provide users with application access. It helps businesses improve their network performance, cut costs, and be more flexible by easily connecting to various network types.

However, legacy SD-WAN solutions often fail to identify network problems ahead of time, requiring IT teams to take corrective action after disruptions occur. This is where AIOps can help by leveraging machine learning and data analysis to proactively monitor network performance, detect anomalies, predict potential issues, and automatically optimize traffic routing across different network paths. AIOps can improve application performance and ensure better user experience by making intelligent decisions based on real-time data analysis across the SD-WAN infrastructure.

AIOps negate the need for human intervention 

AIOps tools use the information extracted from SD-WAN systems and autonomously resolve issues without human intervention. In other words, AIOps tools utilize predictive analytics to forecast future events or outcomes related to network operations. This makes the whole system run smoother and more reliably, while machine learning algorithms can use this historical data to make predictions and proactively improve the performance of critical applications.

However, traditional SD-WAN solutions often depend on manual configurations, which can give rise to issues when it comes to AIOps. In fact, AIOps is more challenging to achieve in SD-WAN environments because SD-WAN differs in how it connects multiple network circuits together, with traffic moving between circuits all the time.

Not all AIOps tools are created equal

Herein lies the problem: not all AIOps tools are created equal, and many of the typical monitoring tools are very complicated. Therefore, IT and network teams need a simple, single interface to help them be more proactive and enable rapid issue identification and resolution.

This is where they need an advanced AIOps solution for SD-WAN that takes a holistic approach and looks at the interconnectedness of multi-vendor IT systems to understand network traffic in its entirety. This means if a device changes, it can monitor this in a more holistic way and understand whether there is an impact or not on the network and it can dynamically prioritize specific business-critical network applications and resources.

In short, organizations need an advanced AIOps tool for their SD-WAN environment that:

  • Improves time to resolution – including the time to diagnose faults and the time to repair any problems.
  • Reduces ticket volumes and alert fatigue through intelligent alerting and proactive anomaly detection.
  • Correlates network events, and based on its ability to learn baseline network patterns, predicts issues and capacity shortages before they happen.
  • Delivers a clear understanding of user experience – enabling network teams to understand the health of the network.  This includes understanding the end-to-end performance of the network and the ability to forecast and predict future issues.