Designing a closed environment for AI systems
A closed AI environment is a verifiable data-exchange boundary, not merely a server in an office.
Map every flow
Show user devices, ingestion, model, retrieval, tools, storage, logs, backups, administration, development and the update path, with identity and allowed data on every connection.
The boundary diagram should include development, administration, artifact import and backup paths, not only the production inference box.
Authorise every action
Give users, the agent and each tool separate identities and least privilege. Keep secrets outside prompts, separate reading from writing, validate fields and protect against replay.
Use separate identities and least privilege for users, agents and tools, keeping credentials out of prompts, documents and application logs.
Govern the supply chain
Approve model, package and image sources; verify origin, licences, composition and hashes; test in staging; and retain a compatible rollback build.
Approve origins, licences, composition and hashes before import, then test the complete compatible release in an equivalent environment.
Manage all data forms
Set retention for source, extracted text, index, conversations, logs and backups, propagate deletion and verify restoration without crossing the declared boundary.
Deletion must propagate from source files into extracted text, search indexes and backups according to documented retention rules.
Continue with the underlying material
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