Will your network let down your AI strategy?

Rob Quickenden, CTO at Cisilion, evaluates how businesses can ensure their network is able to safely and securely implement an AI strategy.

As companies start to evaluate how they can use their AI strategy effectively, there is a clear need to ensure your network is up to the challenges of AI first. AI applications are going to require your data to be easily accessible, and your network will need to be able to handle the huge computing needs of these new applications.

It will also need to be secure enough at all points of access for the different applications to end users’ different devices. If your network isn’t reliable, readily available and secure, it is likely going to fail.

In Cisco’s 2023 Networking Report, 41% of networking professionals across 2,500 global companies said that providing secure access to applications distributed across multiple cloud platforms is their key challenge, followed by gaining end-to-end visibility into network performance and security (37%).

What can businesses do to make their network ready for an AI strategy?

First, you need to see an AI strategy as part of your digital transformation, then you need to look at where you need it and where you don’t. Jumping on the bandwagon and implementing AI for the sake of it isn’t the way forward. You need to have a clear strategy in place about where and how you are going to use AI.

Setting up an AI taskforce to look at all aspects of your AI strategy is a good first step. They need to be able to identify how AI can help transform your business processes and free up time to focus on your core business. At the same time, they need to make sure your infrastructure can handle your AI needs.

AI strategy
© shutterstock/Stokkete

Enterprise networks and IT landscapes are growing more intricate every day. The demand for seamless connectivity has skyrocketed as businesses expand their digital footprint and hybrid working continues. The rise of cloud services, the Internet of Things (IoT), and data-intensive applications have placed immense pressure on traditional network infrastructures, and AI will only increase this burden.

AI requires much higher levels of computing power too. The challenge lies in ensuring consistent performance, security, and reliability across a dispersed network environment.

Use hybrid and multi-cloud to de-silo operations

According to Gartner’s predictions, by 2025, 51% of IT spending will shift to the cloud. Underscoring the importance of having a robust and adaptable network infrastructure that can seamlessly integrate with cloud services.

This is even more important with an AI strategy, as it needs to access data from different locations and sources across your business to be successful. For example, AI often requires data from different sources to train models and make predictions. A company that wants to develop an AI system to predict customer churn may need to access data from multiple sources, such as customer demographics, purchase history, and social media activity.

IT teams need to make sure that they are using hybrid cloud and multi-cloud to de-silo operations to bring together network and security controls and visibility and allow for easy access to data. Where businesses use multiple cloud providers or have some data on-premises, they need to review how that data will be used and how to access it across departments.

Install the best security and network monitoring

It’s clear that as we develop AI for good, there is also a darker side to weaponising AI to create more sophisticated cyber-attacks.

Businesses need end-to-end visibility into their network performance and security to be able to provide secure access to applications distributed across multiple cloud platforms. This means having effective monitoring tools in place and the right layers of security – not only at the end-user level but also across your network at all access points.

Being able to review and test the performance of your SaaS-based applications will also be key to the success of your AI solutions. AI requires apps to work harder and faster, so tasting their speed, scalability and stability, and ensuring they are up to the job and can perform well under varying workloads is important.

Secure Access Service Edge

The best way to ensure your network security is as good as it can be is to simplify your tools and create consistency by using Secure Access Service Edge (SASE).

This is an architecture that delivers converged network and security as service capabilities, including SD-WAN and cloud-native security functions such as secure web gateways, cloud access security brokers, firewall-as-a-service, and zero-trust network access.

SASE delivers wide area network and security controls as a cloud computing service directly to the source of connection rather than at the data centre, which will protect your network and users more effectively.

SD-WAN connectivity

If you haven’t already, extending your SD-WAN connectivity consistently across multiple clouds to automate cloud-agnostic connectivity and optimise the application experience is a must.

It will enable your organisation to securely connect users, applications, and data across multiple locations while providing improved performance, reliability and scalability. SD-WAN also simplifies the management of WANs by providing centralised control and visibility over the entire network.

As we head towards the new era of AI, the cloud is the new data centre, the Internet is the new network, and cloud offerings will dominate applications.

By making sure your network is AI-ready, adopting a cloud-centric operating model, and having a view of global Internet health and the performance of top SaaS applications, IT teams will be able to implement their company’s AI strategy successfully.

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