The surveillance industry has operated under a fundamental constraint for decades: meaningful intelligence required centralized processing. Video was captured at the edge and shipped elsewhere for analysis, introducing latency, bandwidth costs, and an ever-growing dependency on cloud infrastructure.
That constraint no longer holds.
Advances in edge inference have made it possible to run continuous, real-time AI directly where video is captured. This is not a marginal improvement on existing architectures. It changes what is operationally possible: intelligence that is generated in milliseconds rather than minutes, that operates without network connectivity, and that never requires video to leave the premises.
For organizations operating critical infrastructure, this shift has implications beyond performance. When intelligence is generated locally, the question of data sovereignty resolves itself. There is no data to exfiltrate because there is no external system. Ownership is architectural, not contractual.
The industry is moving toward this model. The question is no longer whether edge intelligence is viable, but how quickly organizations will transition from legacy architectures that were designed around constraints that no longer exist.