Overcoming legacy limits to deliver AI-ready networks

The data surge is relentless. An agency’s artificial intelligence model is processing terabytes of information, analyzing threats, predicting outcomes and automating decisions. But the network can’t keep up. Latency spikes. Bottlenecks form. The infrastructure wasn’t built for this level of speed, security and scale.

As AI becomes essential to government operations, traditional networks — built for static, predictable traffic — struggle. AI workloads need networks that scale dynamically and optimize data flows.

The biggest barrier to AI adoption isn’t just technical — policies and compliance take too long to catch up, says Peter Dunn, federal chief technology officer at CDW Government.

“Back in the ‘50s, AI focused on algorithms playing chess or solving problems we now consider simple,” he says. “But those early developments laid the foundation for where AI is today, especially in security.”

AI will always pose security risks, no matter how well integrated. Cybersecurity must evolve alongside AI to counter new threats.

“Realistically, as long as there's a user operating something and a capability out there, there will always be a way to hack into it, unfortunately,” Dunn says. 

To mitigate these risks and optimize performance, agencies are turning to AI-driven automation and NetDevOps. These remove bottlenecks, secure AI workloads and keep networks fast and adaptable. Traffic segmentation is key. Like VDI environments, dedicated AI network enclaves improve security and performance, providing faster access to GPUs and compute resources without straining the broader network. AI automation also prevents congestion by analyzing traffic and optimizing performance.

CDW Government delivers AI-optimized networking solutions that cut latency and bolster security and efficiency. Its expertise in high-performance networking, remote direct memory access and InfiniBand configurations helps agencies reduce latency by 50% and incident resolution times by 40%.

Before modernizing networks, agencies need a clear AI strategy and the right security and architecture, says Rob Smith, senior industry advisor at CDW Government.

“AI is not one of those things that you just want to throw in your network and let's go,” he says. “You want to make sure that you bound it, you protect it, you limit access to it and define what it is that you want it to do beyond the ChatGPTs and so on."

Networks must evolve to support AI, but outdated systems and compliance barriers slow progress. Software-Defined Networking is essential for managing AI’s data flow, but strict regulations and legacy infrastructure delay adoption. Agencies also need stronger security and compliance measures. Zero-trust security models and AI-powered threat detection are key to stopping cyber threats.

CDW works with agencies to transition from outdated, rigid networks to AI-ready architectures using SDN. In one agency, CDW integrated zero-trust security and deployed SDN with AI-powered traffic analysis, slashing response times by 60%.

The agency gained secure, seamless access from the edge to the cloud, eliminating concerns about data at rest or in transit, Dunn says.

“That's where we've significantly been able to show true value when it comes to enabling AI workloads,” he explains. “Network infrastructure improvements and protecting the entire mission critical initiative.”  

Learn more at cdwg.com/federal.