Privacy Boundaries in Local Transcription

Privacy statements are easy to write. Privacy boundaries are harder to design.

The useful question is not only "do you have a policy?" It is "where does sensitive data actually flow while work is happening?"

Boundary-first privacy model

In dictation workflows, the primary boundary is simple: does raw audio need to leave the endpoint to become text?

If yes, external transfer risk exists by design. If no, the exposure surface is smaller by design.

Why local processing changes risk

• No audio upload in the main dictation path.
• Fewer external systems in scope for sensitive content.
• Clearer incident boundaries for security teams.

This does not remove all risk, but it changes the risk profile in ways teams can reason about.

Questions security teams should ask

1. Where does raw voice data travel during capture?
2. Where is transcript data processed before insertion?
3. What network calls are required for basic operation?
4. Can the product function in restricted network environments?

These questions cut through marketing language quickly.

Operational trust and adoption

Teams adopt tools faster when architecture aligns with policy requirements. Local-first dictation typically shortens review cycles because assumptions are clearer and easier to validate.

A practical rollout checklist

• Pilot in one team with known sensitive workflows.
• Document approved use cases and blocked contexts.
• Run speed and reliability tests in real daily apps.
• Review support burden after two weeks.

Further reading

For policy detail, review Almond Privacy. For team deployment context, see Secure Dictation for Teams.