Traditional networks were built for one reason: to optimize performance. Latency and throughput were differentiators that were considered essential to innovation. This approach made sense back when most data was confined to a few core locations; a time when a single point-to-point connection was sufficient, cost effective, and timely to implement. But now, inference at the edge and agentic AI are disrupting enterprise networking strategy. In today’s increasingly sensitive geopolitical landscape, distributed AI data runs the risk of colliding with emerging data sovereignty regulations and data compliance requirements. Networks designed only for performance are becoming increasingly difficult to govern in a…

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