AI is the biggest change we’ve seen so far this century – considered by some organisations as the fourth industrial revolution.

Machine learning and data science intelligence have transformed many industries, and their powers continue to support a diverse selection of businesses across the globe. It would be very remiss not to mention that AI has already started pioneering new methods and processes in the technology space, after all, it’s the very space in which it was born. However, the main focus is data analysis, and many other corners of the sector haven’t yet realised the competitive edge of AI and ML.

One area we are sure could benefit is network management. Organisations spend too much time managing their network. That’s a fact. 42% of Network professionals say they spend too much time troubleshooting and managing the enterprise network… and that’s just the ones that admit it!
How can you best alleviate the anomalies and issues in your network, if you don’t yet have full visibility, still rely on manual support tasks, and don’t discover an issue before it is too late?

More things than ever before are connected to our Enterprise Networks. Different network topologies across different geographic centres, remote collaboration and file sharing, greater demand for mission critical cloud-based communication. It’s all a burden to network managers across the globe.

Ideally, every organisation that truly understands it’s network, needs to manage it centrally, and utilise the power of AI and ML. In the future, networks will be self-managed, and so network managers will have more time to focus on the things that matter – the innovation of the network.


So, what does AI bring to network management?

Network visibility tools have rapidly developed over recent years, enabling you to gain full visibility from edge-to-core, even when your network consists of multiple vendor solutions.

Artificial intelligence and machine learning are allowing network managers to train their visibility tools to spot anomalies, start collecting diagnostic data, and to effectively direct your team to the root cause of issues.
Today you are able to set service-level thresholds such as time to connect, capacity, coverage and throughput, and leverage advanced technology in the process to proactively manage and optimise these benchmarks.

By leveraging AI and ML to detect anomalies on your network, you will be able to perform dynamic packet capture. When an issue is detected, your team will need to effectively rewind and replay what is happening on your network during the outlier timeframe, in order to identify the root cause of issues and quickly take remedial action. Shorter downtime, faster fixes, happier staff.

70% of IT’s time is spent trying to identify and diagnose issues. This stat needs to change. AI really can facilitate exponential network improvement, while easing the burden for network staff.


Can AI support cyber security?

Absolutely. You can no longer rely on basic firewalls blocking nefarious cyber security attacks from entering your network. The attack surface is too great, and attackers are smarter and more persistent than ever before.

How to best protect yourself against these persistent attacks? Invest in a network that is secure by design, which quickly detects, isolates, and remediates attacks through automation.

Organisations using AI & ML in a significant cyber security role, are noticing that they can spot abnormal activity on the network quicker than humans and automate a trigger in milliseconds to isolate threats before they have time to do harm.

Nine out of ten leading businesses have investments in AI technologies, but less than 15% deploy AI capabilities in their work. Are you ready to reap the rewards?