local-first-legal-workspace

Auditing what data leaves your legal AI workspace involves mapping local storage boundaries and inventorying external network calls for model providers, citation lookups, and telemetry. This produces a structured map showing payload details, credential handling, and retention posture.

Local-first boundary mapping for legal AI applications is the process of identifying where documents, chat history, logs, and credentials reside on your machine. It produces a structured local state map to verify privacy boundaries and user-control claims.

Yes, you can generate a user-facing privacy disclosure note for a BYOK legal app. The output includes a disclosure note with confidence bands and a controllability checklist explaining backup, deletion, and BYOK scoping for users.

To inventory external network calls for document conversion and OCR services, you enumerate each external service interaction. This tracks the payload, triggers, credentials, and evidence status of outbound requests from your local web or desktop app.

Yes, this approach works for evaluating desktop legal AI tools with uncertain privacy boundaries. It explicitly labels unknown or not-observed evidence, providing confidence bands for areas requiring verification rather than assuming total privacy.