What data residency actually requires in a government AI project
Most proposals say "data stays in the Kingdom". Few say how. Here is the checklist we use before build starts.
· 6 min read · Cozyplus Technologies
Data residency is easy to promise and hard to prove. A camera frame that leaves the country for inference, a log line shipped to a foreign monitoring service, or a model retrained on a cloud region abroad all break the promise without anyone noticing.
Four questions to settle in writing
Where does inference run? For our traffic-enforcement work the answer is the client's own GPU servers. If the answer is a cloud region, name the region and who holds the keys.
Where do logs and metrics go? Monitoring products default to their own hosted backends. We run Prometheus and Grafana inside the client network, or nothing.
Where are models trained and stored? Training data is the most sensitive artefact in the system. It never leaves the environment that produced it, and model weights are versioned in the same place.
Who can reach the system from outside? The honest answer for most enforcement deployments is nobody. Support happens on site or through the client's own remote-access tooling, with sessions recorded.
What to ask a vendor
Ask for the network diagram, not the policy statement. Ask which third-party services appear in the software bill of materials and what each one is contacted for. Ask who rotates the encryption keys and how often. A vendor that has actually delivered inside a sovereign environment will have these answers ready.