The benefits and challenges of AI network monitoring

Introduction

For many years, artificial intelligence has been used in monitoring network infrastructure. However, recent advances in AI network monitoring have made it possible to implement it on a large scale in production networks.

The key goals of AI network monitoring are to maintain optimal service levels, gain accurate insight into potential infrastructure problems, and obtain that information before it has an impact on the network and business operations.

Machine learning, a subset of artificial intelligence, uses algorithms to analyze telemetry and other data streams in order to establish a baseline for typical operations. The AI network monitoring service can then search for deviations that might point to a possible infrastructure issue after it has established that baseline. In theory, AI monitoring can reduce expensive network outages and speed up troubleshooting.

An increasingly important component of AIOps—the fusion of artificial intelligence and IT operations—is AI monitoring, which uses machine learning on large amounts of data to diagnose issues and automate manual processes. Root cause analysis, anomaly detection, and event correlation are just a few of the network management techniques that are included in AIOps.

Utilizing AI monitoring, as well as AIOps more generally, to support network operations can be especially crucial as businesses install an increasing number of connected devices throughout their premises. Consolidated tracking of industrial devices and traditional IT infrastructure should boost productivity and cut expenses.

Benefits of network monitoring with AI

In use cases outside of IT management, like autonomous driving and natural language processing for smart speakers, artificial intelligence has emerged as a crucial component. Nowadays, there is a growing interest in and trust in AI to support IT operations.

AI network monitoring has a number of potential benefits that can improve enterprise environments and make them more dependable, high-performing, and well-tuned. AI network monitoring offers the following advantages:

Improved and simplified IT monitoring: The density of interconnected hardware and components, as well as the virtualized nature of networks, are still evolving. The process of monitoring these environments has grown complex and frequently ineffective. Finding problems before they have a detrimental impact on operations can be difficult. In order to identify the causes of service-level degradation, IT frequently searches across various monitoring platforms. On the other hand, artificial intelligence (AI) network monitoring claims to dispel the mist surrounding network activity tracking by offering an unobstructed perspective on all infrastructure components as they function together, simplifying IT operations. AI monitoring can also enhance and speed up root cause analysis, allowing you to locate an issue's source quickly.

Security insights: AI monitoring can also reveal possible security vulnerabilities by quickly and accurately detecting threats and accelerating their removal. Additionally, this lessens the number of false positive alerts that deluge security analysts and aids in enhancing an organization's overall security posture.

Driving automation: Systems that can automate manual processes can benefit from AI. Although certain processes might be part of an iterative, continuous maintenance support system, some organizations are investigating the possibility of automating higher-level tasks. AI can be used, for instance, to start automated remediation and mitigation processes.

The challenges of AI network monitoring

Even though AI network monitoring has many benefits in a production setting, certain obstacles could prevent its effective use. To use AI monitoring effectively, IT operations teams must, at a fundamental level, overcome a learning curve. In addition, network operations teams should take into account the following difficulties before implementing AI network monitoring:

Data quality: AI is only as good as the data it uses to distinguish between innocuous anomalies and real network issues. Artificial intelligence network monitoring may be erroneous if certain real-time data is not recorded or if there is a problem with data integrity. As an alternative to real-time data feeds, many organizations use synthetic data, which is generated to mimic activity on a production network. While synthetic data may resemble real network activity, it is not the same and can overlook real problems or identify false ones.

Integration: Integration can be challenging, as is the case with any new technology implemented in a production setting with pre-existing monitoring and management tools. The output might be inadequate if the IT department is unable to synchronize its AI network monitoring tools with other management as well as security services.

Ethical issues: As AI tools become more sophisticated, misuse, as well as operational integrity, may give rise to some troubling ethical issues. This is also true for network monitoring, where massive volumes of data are analyzed by AI technology. Governments are attempting to control the way AI uses data, but this is a challenging and dynamic endeavor.

Conclusion

Predictive maintenance, increased efficiency, and real-time threat detection are some of the benefits of AI network monitoring. By proactively addressing problems, it can reduce downtime and increase network efficiency. Nevertheless, issues such as potential false positives, the need for trained staff, and data privacy concerns still exist, requiring a careful balance between benefits and drawbacks when implementing AI-based network monitoring systems.