An AI for operations, or AIOps, platform is a smart way to deliver visibility, insights and automation to a next-generation network. Given the proliferation of distributed computing, remote users and numerous layers of software abstraction found in large-scale, next-generation network architectures, monitoring and securing networks are now more challenging than ever.
Network professionals can no longer effectively use manual tools to monitor modern networks for performance, cybersecurity and other areas of network management. Enter predictive AIOps tools, which comb through large amounts of network data, determine normal behavior and troubleshoot issues that may occur.
Understand and monitor ‘normal’ network behavior
Network operations (NetOps) professionals typically spend extended periods of time, ranging from months to even years, trying to understand network traffic flows for the various applications and services that traverse a corporate network. NetOps professionals use this knowledge, along with network observability tools, to identify when traffic flows veer from the norm, indicating a performance- or cybersecurity-related anomaly that requires attention.
The problem with this manual baseline method is evident in modern networks. For one, businesses rely on technology now more than ever. Hybrid infrastructure adds, removes and distributes new systems at faster rates, making it nearly impossible for NetOps teams to keep up.
Secondly, when seasoned NetOps professionals leave organizations, their knowledge goes with them. New staff undergo a learning curve to understand correct versus incorrect traffic flows. The time gap between losing knowledgeable staff and waiting for new staff to develop skills can put an organization at significant risk.
The benefit of machine learning and AI in next-generation network management is that, unlike human workers, they never leave. AIOps platforms learn network traffic baseline behavior at faster rates. Additionally, these platforms automatically detect network architecture adds or changes and rebuild network traffic baselines in a fraction of the time it would take using manual processes.
Analyze network health data
Another problem with manual operations is the speed to identify and fix performance- and security-related issues. Even if a network professional understands traffic flows for critical business applications, traditional manual tools and processes are simply too slow.
The increased reliance on technology and the growing risk of data theft, loss or outages due to cybersecurity threats have made it so that networks, as well as the applications and systems they support, are in a continuous state of flux. Network professionals can pull streaming network telemetry data from various parts of large modern network infrastructures. But, with so much data to analyze, humans now require AI to comprehend it all.
Identify problems and provide recommendations
Because of the increased amount of network health data to analyze, the number of performance and security alerts is also rising. Network teams must first categorize and prioritize these alerts and then troubleshoot to find a root cause and steps to remediate.
It usually takes network admins days or weeks to identify a root cause and manually troubleshoot even a single performance or security alert; finding a workable approach to fix the problem can also take a significant amount of time. This is where AIOps platforms shine. These tools alert, categorize and prioritize network issues, as well as pinpoint where problems occur and recommend approaches on how to fix them.
The power of predictive analysis
AIOps platforms are evolving to the point where they can help predict performance-related issues and recommend steps to fix problems before they worsen and impede business operations. While a great deal of hype exists about the level at which network teams should use predictive analysis tools, decision-makers and network professionals working in large and complex environments should watch AIOps closely.