The reality of sovereign AI: How to avoid dependency without building everything yourself
The reality of sovereign AI: How to avoid dependency without building everything yourself
Ben Saunders contributed a thought leadership piece to Silicon Valleys Journal on sovereign AI for enterprise leaders.
The article covers:
Why sudden changes in access to major model providers show the risk of single-vendor dependency, and why sovereignty means visibility over models, data flows, cost, and the ability to switch
Owning the AI control plane rather than every foundation model: permissions, logging, audit trails, and consistent monitoring across workloads
A multi-model approach that matches public models to lower-risk tasks and controlled deployments to sensitive or regulated work, without fragmenting adoption across separate point products
Right-sizing models for the use case, including smaller task-specific deployments, distillation, and quantisation where appropriate
Building a governed stack with data residency, policy controls, real-time monitoring, and the ability to pause agents or change providers when terms, pricing, or regulation shift
