Amid the onslaught of three-letter acronyms like LLMs, GPUs, NLPs and SSDs, it’s easy to overlook one of the most critical pieces of AI infrastructure: the network that underpins it.

Two decades ago, global networks faced daunting new demands as they began migrating to the cloud. At the time, I was a network engineer building the largest IP call center in Europe, where I experienced just how challenging it was to adopt new technology while maintaining control of the network.

Now, with the advent of network AI, I can’t help but see how this problem has reared its head once again. While plenty has changed in the last 20 years, IT leaders are still looking for ways to embrace innovation without sacrificing their network’s security, stability or spend. Read on to learn about the considerations you should make before introducing AI to your network.

Refresh Your Network To Handle Increased Traffic

AI is pushing infrastructure to its limits, devouring power, cooling and storage to keep up with rising demand. Projected to drive 30% of all data center traffic this year, AI is predicted to surpass cloud computing and big data analytics in the next three years.

Most traditional switches and network devices weren’t built to handle this constant stream of traffic. They’re also not prepared to take on the high bandwidth and low latencies that AI requires to continuously train its models, with GPUs already consuming as much as 40% of an AI project’s budget.

To leverage AI’s full potential—while maintaining a reasonable cost per inference—organizations must take steps to optimize their infrastructure. Some are replacing traditional switches with those tailor-made for AI. Others are investing in cloud networking services, or Network as a Service (NaaS). But network resilience takes more than a one-time investment or upgrade.

Before implementing AI, IT teams must have a process in place to continuously map and document the connections between data centers, cloud and edge environments. Think of each of these environments as building blocks with defined interfaces, especially where AI infrastructure links to the WAN or cloud.

With this modular understanding of network behavior, your team will have the flexibility to expand capacity, isolate workloads or rearchitect paths as demand grows, all without destabilizing your digital infrastructure.

Use Your Network as Your Source of Truth

AI workloads introduce more lateral connections between systems, making networks harder to map and, by extension, manage. At the enterprise scale, it’s simply not possible for an IT team to keep up with the elevated rate of changes that comes with AI, undoubtedly contributing to the rising number of outages caused by human error.

Network automation can help here, but it can also be risky. Truly resilient automation should be autonomous, meaning it needs to be able to understand the entire network in order to take action. If it’s missing context for network behavior, it can cause more issues than it solves, leading to a cascade of costly errors.

Many IT teams will turn to their network documentation as fuel for automation and AI, but all too often, documentation diverges from the network’s reality. The first step toward building an autonomous network? Creating a reliable, shared view of network behavior. This starts by integrating discovery, change tracking and documentation into everyday operations so all teams and tools are operating from a shared source of truth. That source of truth is the key to trustworthy automated governance.

Add New Security Controls, Without Forsaking Old Ones

AI workloads create new east-west traffic patterns that traditional security controls—like segmentation and encryption—aren’t always built to handle. Instead of staying fixed to a single device or location, these controls should move with the application, uniformly enforcing policies as workloads shift between data centers, clouds and containers.

After strengthening existing defenses, IT teams should also plan to layer on application-aware technologies like microsegmentation and identity-based access controls to protect areas where dynamic AI traffic creates the most exposure.

However, all the security controls in the world won’t help if you can’t see how they’re actually behaving. Data centers and endpoints may look secure, but the middle of the network is often opaque, concealing unmonitored routing paths, asymmetric flows, firewall bypasses and other risks.

To ensure continuous security, IT teams need to be able to map the dependencies between systems and pinpoint any choke points or single points of failure. In an AI-driven environment where every component interacts with dozens of new and legacy systems, this level of visibility and control is critical for ensuring consistent governance.

Take a Calculated Look Before You Leap

The pressure to deploy network AI is at an all-time high. However, moving too quickly often means cutting corners, leading to higher costs and compounding risks.

Before jumping into the deep end, it’s vital to take the time to understand how your infrastructure really fits together. That means:

• Continuously mapping dependencies across data centers, clouds and edge sites.
• Centralizing network visibility to ensure that every team and tool is working from the same understanding of the network.
• Evaluating where your existing processes and tools fall short, and layering on new technologies as needed.

By following these steps, you’ll be taking a major step toward building a resilient network that can weather the ever-growing demands of AI.

This byline was originally published by Forbes.

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