A Practical Privacy Checklist for AI Voice Tools

Before deploying voice AI tools across your organization or installing them on your personal development machine, use this auditable checklist:

1.Where does transcription computation execute? (Is it processed in local RAM or streamed across the internet?)

2.What outbound network requests does the binary make? (Does it query outside servers during voice capture?)

3.Can the tool function in true Air-Gap mode? (Does dictation work with Wi-Fi completely disabled?)

4.Does it require an account or cloud identity? (Can it operate anonymously without user logins or authentication tokens?)

5.Is telemetry opt-in or disabled by default? (Are words spoken, session durations, or app titles tracked?)

6.How is local transcript history stored? (Is it in an unencrypted SQLite file, encrypted via OS keychains, or ephemeral RAM-only?)

7.Is the codebase open-source and auditable? (Can third-party security researchers inspect the network and audio boundaries?)

8.Are model weights stored permanently offline? (Does it download weights once and never phone home?)

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