What is AI network monitoring?

Networking environments have become so complex as data volumes grow and enterprises manage network devices and endpoints on-prem and in the cloud. As the backbone of a business, the network can’t go down or allow in security threats. AI network monitoring brings the self-optimization, massive scale, and extreme speed of AI to a discipline in need of technological support.

What is AI network monitoring?

Enterprise networks serve as the backbone of the company, connecting computers, servers, cloud services, software, and devices with employees, the internet, and with other companies. So networking monitoring, continuously assessing the health of the many connections, has long been a must-have capability for businesses.

Networks are incredibly complex in the modern digital era. They also produce massive amounts of data compared to even a decade ago, as data sources and types proliferate. Typically, network monitoring relied on an initial manual setup by the IT networking team, with monitoring sensors deployed to network devices, with protocols in place to collect data on device status, traffic flow, and overall performance. 

Now, with advanced AI capabilities quickly emerging, AI network monitoring offers a modern method of network management. AI, machine learning (ML), and big data analytics, used in conjunction, can help optimize and automate many of the normally manual processes of network monitoring.

AI network monitoring’s proactivity can prevent many common issues, such as predicting hardware failure before it happens based on device metrics; identifying potential security breaches; automatically increasing cloud bandwidth during a traffic spike; noticing insider data access; and catching misconfigurations that could cause downtime hours faster than a human team.

Traditional vs. AI network monitoring

Before the advent of AI, teams setting up network monitoring processes used polling methods to collect data from network sensors on a regular basis. The focus with traditional monitoring is on device-level metrics, so that if one device shows any issues, the networking team can address it. The networking team also put in place thresholds and other rules to generate alerts when a network problem occurs. That might be CPU usage going over a certain percentage, DNS resolution failures, or signal drops — any issue that could affect users. 

AI network monitoring puts the abilities of artificial intelligence to work: namely, applying AI systems to networks to learn what normal behavior looks like, then detect anomalies against baselines. Because AI can learn over time and handle massive quantities of data quickly, these newer systems can continuously analyze massive data sets and bring better visibility.

How does AI network monitoring work? 

Broadly, AI network monitoring platforms can analyze all of the data relevant to a network from cloud and on-prem locations. Algorithms detect anomalies, make predictions, and suggest actions for network engineers to take. Because AI can work so quickly, it’s able to detect and identify network issues before they’re able to affect users or cause bigger problems.

There are a few important areas to understand within AI network monitoring.

Collecting the data

Network monitoring ML models rely on historical data and other relevant datasets (such as code repositories, curated public datasets, academic papers, or synthetic data). So AI network monitoring solutions capture observability data from multiple related sources: devices like switches and routers, data queries and other transactions. Then, AI tools can overlay streaming telemetry data, which involves network devices pushing performance metrics out continuously, rather than older polling methods. AI network monitoring tools learn continuously from all this data to update what’s normal and expected from the network and optimize accordingly.

Training the models

If you’ve used AI at all, you know the underlying model is at the core of AI’s capabilities. The ML models learn baseline behaviors of the network, as well as typical traffic volumes and performance benchmarks for various applications and network endpoints. AI models can then be configured to identify outliers and to gauge differences between security or policy issues and normal fluctuations.

Monitoring in real time

AI models can monitor networking in real time, a big improvement from even the fastest traditional monitoring methods. This speed means that a small anomaly can be detected and dealt with immediately, before it can cause much bigger problems. In a user-facing situation, AI network monitoring can identify and fix an issue before a user even notices. Some AI-powered network tools offer self-serve to continuously discover devices, prevent network overload, and quickly facilitate user onboarding.

Alerting

With AI’s scalability, these network monitoring tools can take a big-picture view to correlate issues happening in multiple places. What may have looked like unrelated alerts previously may actually be identified by AI as stemming from one cause or issue. AI monitoring tools use predictive analytics to be proactive, rather than reactive, to potential threats. That might include alerts on repair needs, or recommendations on ways to optimize various parts of the network for better load distribution or capacity balance.

How does AI network monitoring help businesses?

AI network monitoring can be transformative for IT teams. It can remove a lot of the manual work and maintenance involved with keeping a complex, multi-endpoint enterprise network healthy and available to users. AI network monitoring is generally more proactive than traditional, reactive monitoring and management. That contributes greatly to the many benefits of AI network monitoring:

Security improvements and threat detection

If a network traffic pattern experiences unusual behavior, an AI network monitoring tool will notice immediately and alert admins. Speed is of the essence for stopping security breaches, so these tools can respond much faster than humans alone.

Speed, scale, and efficiency

AI network monitoring tools can process more data, faster, than any team of humans, and can grow and scale as networks do without much intervention. It also brings root cause analysis that’s onerous and time-consuming for human teams, so issues can get identified and solved faster.

Time savings on maintenance

IT teams that use AI network monitoring save hours of time on routine tasks that can be used for more strategic or higher-level network management. Building automation workflows can save additional time for networking teams.

Better performance

AI network monitoring tools learn a network over time, and can optimize traffic on the fly, as well as adjust configurations as necessary for better performance. Along with its abilities to detect issues early, this all contributes to faster and more reliable network performance.

Incorporating AI into IT operations management 

AI as a tool to help humans work better and smarter has already proved useful for IT operations teams. For networking teams, AI-powered monitoring can save lots of time and make scaling much faster and easier. Beyond the day-to-day productivity gains, AI network monitoring brings a powerful way to detect security threats faster, perform predictive maintenance, ensure consistent performance, and much more.

Beyond AI-driven, automated network monitoring, IT teams can automate entire operational workflows with Atera’s Copilot. The platform includes ticketing, remote monitoring and management (RMM), network detection and performance visibility, and professional services automation (PSA). Teams can see monitoring context with every ticket they handle or other technician action that’s needed to help users.

AI tools are here and ready to use, and they’re tailor-made for repeatable processes and complex environments. See how Atera’s IT operations platform can take manual tasks off your plate so you can spend more time on strategy and tasks made for humans.