How AI, Machine Learning Will Benefit Network Management
… detecting, identifying and protecting against a range of cybersecurity threats — and will continue to do so for a long time.”
However, Mauriello goes on to state how AI and machine learning will benefit network management and network monitoring in important ways including the ability to correlate vast amounts of data from many sources — a must for informing security teams about incidents under investigation and making teams more knowledgeable and efficient at processing analytics.
“For example, AI/ML can inform you with anomalies, clustering and risk scoring of things to investigate,” Mauriello said. “This data can be used to better inform humans working to make decisions about security incidents. But AI/ML cannot make the decision for you.”
Filling Networking Monitoring Gaps with AI, Machine Learning
Modern networks are filled with mountains of information making it impossible to manually make heads or tails of most of it. But after learning basic patterns of IT traffic, AI and machine learning will benefit network management and network monitoring by cutting out the noise and exposing the signal for human decision makers — for everything from enterprise network bottlenecks to 4G and 5G service edge cases.
“In addition to monitoring and predicting network performance, AI and ML can help analyze variables such as signal strength when deploying new technologies, identify and fix equipment issues on devices trying to attach to the network, and help evaluate and draw conclusions for configurations of new cell sites,” said Matt Tegerdine, director of predictive analytics at Verizon. “All these examples require analysis of terabytes of data, and the patterns found using AI and ML on that data can add significant efficiencies.”
And it is vital for decision makers to distinguish between secure networks and safe networks when evaluating network security solutions. The industry has focused primarily on securing networks by preventing breaches: firewalls, passwords, two-factor authentication, phishing protection, endpoint security, and so on, according to Laurent Zimmerli, VP of product marketing at Open Systems, a provider of a secure SD-WAN. “AI and ML monitoring tools may help identify breaches before they happen, but no network will ever be 100 percent secure,” Zimmerli said.
Overall, MSPs and the customer networks they operate are exposed to a wealth of operational and network data and yet only leverage a part of it. This is where analytics, automation, and AI kicks in.
“Combined with telecom domain expertise, we will now be able to make use of, learn from and benefit as a result of all the data networks that we operate,” said Jonas Åkeson, head of automation and AI, managed services at Ericsson. “As a result, operations and services will become fully data-driven. And data-driven will give us truly predictive, proactive and preventive operations, moving us from cost-centric to fast and value-centric.”
And because AI continuously leverages the most recent data points to make predictions, new patterns in data emerge constantly and must be taken into consideration when planning for future network needs.
“Traditional methods may take several weeks or months to identify a new pattern in data,” said Greg Cox, CTO architect at Sungard Availability Services. “AI can recognize the new pattern by the second or third recurrence in the same data flow and adjust future predictions accordingly.”