Air-gapped AI development that works offline

Factory’s air-gapped deployment guidance argues that offline AI development must be validated as a complete workflow, since tools such as test runners, dependency resolvers, model gateways, and plugins may otherwise attempt external connections. Its enterprise airgap build of Droid disables Factory services including sign-in, updates, telemetry, synchronization, hosted models, and routing, requiring organizations to configure and test approved internal model endpoints using representative repositories and realistic development tasks. Network and infrastructure controls should independently restrict all destinations and local access, while approved internal artifact processes must supply source code, runtimes, packages, tools, fixtures, policies, and credentials. Organizations are advised to test model failures and credential expiry, account for unavailable cloud-based collaboration and analytics features, preserve evidence through internal source control and validation records, and rehearse updates and rollbacks. A successful pilot should demonstrate that teams can consistently build, secure, review, and recover within the offline environment rather than merely complete a one-time isolated test.

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