The internet was a massive, revolutionary invention. A once-in-a-lifetime breakthrough. And yet, it was not an overnight sensation in terms of consumer adoption. This may surprise some people today. From the early web browsers in 1992 to the explosion of dot-coms in 1998, it took roughly six years for the general public to truly embrace the world wide web. Fast forward to today, and the landscape has dramatically shifted.
Consider the recent phenomenon of ChatGPT, the large language model chatbot launched by OpenAI in late 2022. Within a year, consumer adoption of this AI technology reached a fever pitch. For a while, it was all anyone in tech and business circles could talk about. In fact, they still are. This highlights a critical difference in our current technological era, which is that innovation is happening and being adopted at an unprecedented pace.
This rapid advancement, particularly in the field of AI, presents a unique challenge for businesses: our networks are struggling to keep up with the ever-growing volume of data that we’re producing, which is expected to reach 147 zettabytes (ZB) globally in 2024 and nearly double to 291 ZB by 2027, according to research firm IDC. To put this into perspective, 147 ZB of data would be enough to store all the movies ever made, all the books ever written, and all the music ever composed, with plenty of room to spare. As such, the way we manage our networks today needs to be fundamentally different from how we managed them even a couple of years ago. This is only becoming more apparent for enterprises with each passing day.
The Rise of AI and Its Impact on the Network
LLMs and AI applications, such as AI-powered chatbots and real-time facial recognition, are inherently data-hungry. They require constant access to vast amounts of information for training, processing, and real-time decision making. This creates a significant strain on legacy networks, which weren’t built and equipped to handle this type of surge in data traffic.
The integration of AI also introduces a new frontier in network security. While AI can be a powerful tool for enhancing security through advanced threat detection and prevention, it also creates new vulnerabilities. Threat actors can use AI to launch new, sophisticated attacks, leveraging deepfake technology and machine learning. Additionally, the vast amounts of data processed by AI systems expand the attack surface, making data breaches and unauthorized access more likely.
Beyond the sheer volume of data and network security challenges, AI applications also introduce a new layer of complexity to network infrastructure. These applications often demand specialized network architectures, such as low-latency connections for real-time processing or high-bandwidth links for data-intensive tasks. Moreover, managing and securing AI workloads presents unique challenges, as these systems handle sensitive data and require robust protection against cyber threats.
The consequences of outdated network approaches are real. Slow response times, data bottlenecks, security challenges, and system outages can cripple AI implementations, hindering their effectiveness and ultimately impacting business productivity. If you have the fastest car in the world, but you don’t know how to drive it and protect it, what’s the value in having it at all? It’s time we take our networks to the next level. Today.
The Evolution Imperative: Rethinking Networking Strategies
For enterprises looking to capitalize on the power of AI, a fundamental shift in networking strategy is essential. We must move beyond the “good enough” approach of the past and embrace new technologies that can meet the demands of the AI revolution. The increasing complexity of managing IT network infrastructure, especially in multi-vendor and multi-cloud environments, has highlighted the need for more advanced automation solutions.
Automating network tasks frees up IT resources, allowing IT teams to focus on more strategic initiatives to help grow a business. It also dramatically reduces human errors like misconfigurations, which lead to the vast majority of cyber breaches. Additionally, automation can ensure real-time network optimization, dynamically allocating bandwidth and resources to meet the ever-changing needs of AI applications.
Today’s networks need to be lightning fast, but more importantly, they need to be flawless in execution, especially as mission critical apps are now dependent on a stable and secure connection to LLMs. If you make a change to your network, will everything around you crash, or will the system be intelligent enough to adjust on the fly, in all the places it needs to? Relying solely on humans to keep our networks up and running smoothly is both unrealistic and unfair, especially at the pace that we’re progressing. For businesses to keep up with the speed, resources, and security that AI applications demand, automation must play a role in their networks to some extent. The world’s largest enterprises have begun to embrace this idea, and it’s only a matter of time before this approach becomes commonplace.
Beyond network automation directly, the goal for a next-generation NetOps platform is to handle the demands of AI. The platform must intelligently discover devices and configurations, creating a digital twin of the network for improved visibility and control. Additionally, the platform must ensure that network configurations align with desired policies and intentions, regardless of device type or vendor. This helps to maintain consistency and prevent errors. Moreover, the platform must enable the network to automatically respond to events and changes, reducing the need for human intervention and minimizing downtime. This can significantly improve network reliability and efficiency. Finally, the platform must provide pre-deployment validation and auto-remediation of non-compliant devices, strengthening the network’s security posture and reducing the risk of vulnerabilities.
The Road Ahead
The rapid adoption of AI is a testament to its transformative potential. However, to truly unlock this potential, businesses must consider their foundation — their networks — and if they’re adequately prepared to go full speed ahead. The challenges are multifaceted, with increased data volume, complex network architectures, and a rapidly evolving threat landscape. To navigate these complexities, enterprises must embrace an approach that includes network automation and robust security.
Network automation is essential for delivering performance, scalability, and efficiency. Simultaneously, a fortified security posture that provides advanced threat detection, prevention, and response capabilities is critical in the fight against emerging threats.
A comprehensive NetOps platform built to handle the demands of AI is also paramount for businesses aiming to future-proof their infrastructure. Organizations can gain a deeper understanding of their network infrastructure, identifying potential issues before they escalate. Moreover, these platforms enable proactive identification and address security vulnerabilities, strengthening the network’s posture.
The time for complacency and maintaining the status quo is officially over now that AI is moving into production. Today’s business and the future of business is intelligent, and the foundation for that is a robust, adaptable, automated, and secure network.
Image credit: Wayne Williams
Jeff Gray is CEO and Co-Founder, Gluware.
