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, ..
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