Can AI Help Network Staffs Manage Network Complexity?

As IoT, cloud-based, and on-premises networks converge, data on network performance is pouring in from all points. How can network staffs keep up? Is there a place for AI in network monitoring, and where should you draw the line?

Network Computing, Contributor

May 30, 2024

2 Min Read
AI net management

Between now and 2032, the IoT (Internet of Things) market is expected to grow by a CAGR of 24.3%, reaching $4,062.34 billion by 2032. The primary growth drivers are the movement of more business operations to remote locations, the ability of more powerful IoT devices to do more IT on their own, and the ability of IoT to find a place in almost every business use case that companies develop.

At the same time, new network protocols like Wi-Fi 6 are dramatically increasing the number of devices that networks can carry.

Both trends lay the groundwork for corporate network expansions, but the complexity of having to monitor all of these network nodes and devices also expands exponentially for network staffs. Even with current network monitoring and remediation tools, how will network professionals be able to catch every emerging performance or security issue?

The industry answer is by adopting artificial intelligence (AI) for network monitoring, maintenance, and remediation. AI has the potential to automate a large share of work in these areas that staff must do manually today—with the added advantage of being able to rapidly process and assess incoming real time data so the AI can act quickly. This is what makes AI a key component of AIOps (AI for IT operations).

Here’s what network AI tools can do

In a baseline startup, network AI needs a ruleset with which to operate. It's up to the network staff to define and input a complete set of rules that cover network performance parameters and monitoring, security threat detection, governance, etc. These are the rules that the AI engine will use for its daily monitoring. Additionally, the data coming into the AI data repository from network sources must be clean (e.g., no jitter or useless metadata) and of high quality. It is up to network staff to ensure that incoming data meets these standards.

An AI “model” is constructed from the rules of monitoring, etc., that the IT network staff provides. Once these rules are in place and the incoming network data is clean, AI network monitoring can begin.

From this point on, AI should be able to deliver the following capabilities that IT network staffs don’t have:

  • Built-in and automated machine learning that detects new patterns and anomalies in network data and assesses their impact on network rulesets

  • The ability to detect potential issues and trends from real-time data

  • The potential to automate routine network operations, such as using an AI RPA (robotic process automation) function to automate the provisioning of physical and virtual devices in the network.

Advantages like these give network staffs the ability to automate routine daily work that otherwise would eat into schedules. AI’s ability to predict network trends and issues also gives network staffs ways to anticipate and intercept these issues before they ever manifest themselves in a network service degradation or outage.

Read the rest of the article at Network Computing.

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Network Computing


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