--- title: "Private dictation checklist: trace audio and text through the whole workflow" description: "Audit dictation privacy across microphone, local model, cloud fallback, transcript, history, clipboard, AI formatting, destination app, logs, and deletion." language: "en" canonical_url: "https://voicetypr.com/guides/private-dictation-checklist" md_url: "https://voicetypr.com/guides/private-dictation-checklist.md" last_updated: "2026-07-31" --- # Private dictation checklist: trace audio and text through the whole workflow A private dictation claim is only as strong as its complete data map. Trace raw audio, temporary files, transcript, history, clipboard, optional formatting, diagnostics, backups, account identifiers, and the destination app. “Local transcription” answers one important question, not every privacy question. > **Last verified: 2026-07-31.** Voicetypr publishes this checklist and benefits from local-first positioning. It is based on Voicetypr’s documented data flow, NIST privacy-risk guidance, OS privacy documentation, and general inventory practice. No independent privacy, security, legal, or network audit was performed. ## Quick verdict Verify where raw audio is processed first. Then inventory transcript storage, history, clipboard, formatting providers, telemetry, crash reports, license calls, model downloads, backups, and destination applications. Disable what is unnecessary, test with non-sensitive content, and obtain qualified legal and security review for regulated or high-risk use. ## Verdict by role ### Individual user: Check the active model and optional features A local model can reduce audio transfer, but history, clipboard, and target apps still matter. ### IT or security reviewer: Require a versioned data-flow inventory Claims should map data category, recipient, purpose, retention, access, and deletion. ### Regulated organization: Treat this as intake, not approval Contracts, law, configuration, identity, endpoint security, and organizational controls need specialist review. ## Decision criteria ### Data inventory List audio, transcript, prompts, history, logs, identifiers, licenses, payment, diagnostics, and support artifacts. ### Processing and recipients Identify local processes, vendors, subprocessors, destinations, and user-enabled providers. ### Storage and deletion Record location, encryption, retention, export, backups, and whether deletion propagates. ### Controls and evidence Check defaults, opt-ins, network behavior, permissions, policy enforcement, documentation, and incident response. ## Minimum dictation data map | Data | Question | Evidence | Control | | --- | --- | --- | --- | | Raw audio | Where is inference? | Docs and observed traffic | Local model or approved endpoint | | Transcript | Where is it stored? | History and file settings | Disable, limit, delete | | Formatted text | Which provider receives it? | Provider configuration | Off by default or approved | | Destination | Where does pasted text go? | App policy | Access and retention | | Diagnostics | What leaves on failure? | Privacy and logs | Redaction and opt-out | ## Start at microphone capture Check OS microphone permission, selected input device, recording duration, temporary files, memory handling, and whether a fallback changes processing when local inference fails. Do not assume the visible model name proves which path is active. Use product documentation and safe network observation where authorized. ## Follow the transcript after recognition Inspect local history, clipboard managers, autosave, search indexes, backups, crash logs, and support bundles. The transcript may persist in more places than the audio. When text is pasted into a cloud document, chat, email, AI assistant, or CRM, that destination becomes a separate processor with its own access and retention. ## Treat optional AI as a new path Formatting, summarization, translation, and rewriting may send text to a provider even when raw audio stays local. Identify the provider, model, account, purpose, and whether content is retained or used for training. Keep optional features disabled until approved. Compare cleaned output with the raw transcript because privacy is not the only risk; meaning can change. ## Document limits and retest Record app version, model, OS, settings, network policy, date, evidence, owner, and unresolved questions. Recheck after updates or configuration changes. NIST’s Privacy Framework is a voluntary risk-management tool, not a certification. This checklist likewise does not prove compliance. ## Limitations and checks - No independent penetration test, source-code audit, or packet capture was performed. - This is not legal advice or a compliance certification. - Destination apps and operating systems can create additional copies. - Product behavior can change by version, setting, account, and platform. ## How we evaluated 1. Mapped lifecycle stages from capture through deletion. 2. Separated default local inference from optional connected features. 3. Used NIST risk-management framing and official platform/product documentation. 4. Required evidence and versioning while stating absent audit work. ## Sources - [NIST Privacy Framework](https://www.nist.gov/privacy-framework) - [Apple: control microphone access](https://support.apple.com/guide/mac-help/control-access-to-the-microphone-mchla1b1e1fe/mac) - [Microsoft: Windows microphone privacy](https://support.microsoft.com/windows/manage-app-permissions-for-your-microphone) - [Voicetypr privacy and data flow](/privacy) - [Voicetypr public desktop repository](https://github.com/ideaplexa/voicetypr) Recheck pricing, requirements, and privacy terms with each provider before buying. ## Frequently asked questions ### Does local transcription mean private dictation? It removes a raw-audio transfer from inference, but history, clipboard, optional providers, diagnostics, backups, and destination apps still need review. ### Can this checklist prove GDPR or HIPAA compliance? No. Compliance depends on facts, roles, contracts, law, configuration, controls, and specialist review. ### Should I test network traffic? It can add evidence when authorized and performed safely, but traffic observation alone does not reveal local storage, future behavior, or contractual processing. ## Related guides - [Local vs cloud speech recognition](https://voicetypr.com/guides/local-vs-cloud-speech-recognition): Define the inference boundary precisely. - [Voicetypr privacy](https://voicetypr.com/privacy): Read the product-specific documented data flow. - [Air-gapped dictation](https://voicetypr.com/air-gapped): Review stricter disconnected-environment caveats.