08 February 2024
Rob Quickenden, CTO, Cisilion
A s companies start to evaluate how they can use AI effectively, there is a clear need for the network engineering teams to first ensure the network is up to the challenges of AI. AI applications are going to require data to be easily accessible and the network will need to be able to handle the huge compute 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 the 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%).
So, what can you do to make your organisation’s network AI ready?
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 compute 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 AI as it needs to access data from different locations and sources across the 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.
Network engineering 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. When businesses use multiple cloud providers or have some data on-premise, 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 weaponizing AI to create more sophisticated cyber-attacks. There needs to be end-to-end visibility into the 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 network security is as good as it can be is to simplify the 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 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, cloud is the new data centre, internet is the new network, and cloud offerings will dominate applications. By making your network AI ready, by adopting a cloud-centric operating model, having a view of global Internet health and the performance of top SaaS applications, it will mean you will be able to implement your company’s AI strategy successfully.
