AI code data residency beyond the repository

AI code data residency depends on the full path of source files, prompts, model inference, session records, telemetry, integrations, logs, and diagnostic outputs rather than solely on where a Git repository is hosted. Factory states that Droid works on files locally and routes inference context through the configured model path, while its cloud-managed, hybrid, and airgapped options differ in their data boundaries; organizations must also verify whether gateways, proxies, or fallback routes send data to external providers. Model hosting and control-plane placement are separate considerations, requiring review of session handling, administrative records, support diagnostics, retention, and deletion procedures. Telemetry, MCP services, hooks, and test tools can create additional data destinations, particularly during failures, and Factory notes that content logging is disabled by default but sends unredacted raw content to a customer-configured collector when enabled. The recommended approach is to use representative synthetic tasks to observe and document every data destination, operator, purpose, retention policy, and enforcement point, repeating the assessment whenever models, collectors, integrations, or execution environments change.

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