Private storage does not necessarily mean private AI processing. Your chats or files may be saved on your device, in a private cloud account, or on infrastructure you control, while an AI feature still sends prompts or relevant context to an external model provider. To understand what stays private, check both where data is stored and where each AI request is processed.
Storage and AI processing are separate
Storage describes where information is kept. Inference describes where a model processes the information to produce an answer. A product can keep recordings or documents locally yet send selected text, prompts, or conversation context to a cloud model when you use an AI feature.
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“Private cloud” generally describes a cloud account and its access or security controls; it does not establish that AI processing happens on your device. Self-hosting can keep an app’s workspace on infrastructure you control, but it does not prevent the app from making separate outbound model or tool requests.
- Local storage: Files are kept on your device. This can reduce cloud copies, but a cloud AI request may still transmit relevant content.
- Private cloud storage: Files are held in a cloud service with stated controls. Check provider access, subprocessors, retention, and storage region.
- Self-hosted storage: The app’s workspace may remain on infrastructure you control, while model calls or connected tools can still send data elsewhere.
- Local inference: The model processes relevant content on your device or controlled infrastructure. Do not assume it is included just because storage is local.
Trace the data flow for each feature
A product’s default storage setting does not tell you what happens when you summarize a recording, ask a question about a file, or use a connected assistant. For each feature, find out what content leaves your device or workspace, which company receives it, and whether the transfer is optional.
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For example, PLAUD’s policy says data is stored on-device by default, while also describing AI service providers for AI-powered features. Vellum says its model calls send conversation context to its model provider. Vellum also distinguishes its hosted cloud workspace from self-hosting on a Mac, but says both arrangements involve cloud model calls. These are product-policy descriptions, not independent verification of technical implementation.
Check for other outbound routes, too. Vellum identifies third-party channel messages and tool network calls as separate paths. An AI app can therefore send information through a model request, a connected service, or a tool even if the workspace itself is self-hosted.
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Compare privacy controls that answer different questions
| Control or claim | What to check |
|---|---|
| Storage location | Is content on-device, in a provider’s cloud, or self-hosted? Is syncing optional, and where is synced data stored? |
| Inference path | Does the feature call a cloud model? Which prompts, files, excerpts, or memories are sent, and to which provider? |
| Access | Can account owners, administrators, support staff, subprocessors, or the service provider access stored content? |
| Training and product improvement | Can content be used to improve models? Which setting controls this, and what data and services does it cover? |
| Retention and deletion | How long is each kind of data kept? When does deletion reach backend systems or backups, and what exceptions apply? |
| Security and control | Is data encrypted in transit and at rest? Who controls the keys? Are permissions, exports, deletion, and regional storage documented? |
| Service and account type | Do consumer, business, and API accounts have different privacy terms? |
Encryption protects data in transit or at rest; it does not, by itself, answer who can access content or whether content is sent to a model. Data minimization, access controls, purpose limits, retention schedules, and third-party terms address different parts of the data lifecycle.
Read the terms for your specific account
OpenAI consumer services
OpenAI’s U.S. privacy policy, updated May 18, 2026, covers use of its website, applications, and services. It says it does not govern content processed on behalf of business-offering customers, such as API customers; those uses are covered by customer agreements. The policy treats prompts and uploads as content and says content may be used to improve services subject to user controls. It describes deletion and Temporary Chat controls; certain deleted personal data is removed from systems within 30 days, subject to stated legal, safety, and other exceptions. Read OpenAI’s U.S. privacy policy.
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Anthropic consumer products
Anthropic’s retention guidance, dated July 1, 2026, applies to Claude Free, Pro, Max, and Claude Code when used on those accounts; it directs commercial users to separate information. It says deleted conversations disappear from chat history immediately and are deleted from backend systems within 30 days. If model improvement is enabled, eligible new or resumed conversations may be retained in de-identified form in training pipelines for up to five years. Turning the setting off or deleting a chat does not reverse training already underway or remove information from already-trained models. Read Anthropic’s retention guidance.
PLAUD device and service policy
PLAUD’s policy, updated May 20, 2026, says data is stored locally by default and that its optional Private Sync Cloud is opt-in, with regional servers in the United States, Germany, Japan, and Singapore. The policy states that it uses TLS 1.2 or higher for data in transit and AES-256 or equivalent protection at rest. It also says users can delete data and that AI providers are contractually limited to the requested task, cannot retain data beyond what is necessary for that task, and cannot use it for model training. These are PLAUD’s policy statements, not independently audited findings. Read PLAUD’s privacy policy.
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What “no training” and deletion do—and do not—promise
A no-training term concerns whether covered data may be used to train or improve a model; it does not necessarily mean the provider receives no data or retains none. A zero-retention claim also needs a defined scope: which provider, feature, data, and processing it covers, and whether exceptions apply.
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A practical checklist before sharing sensitive information
- Identify the feature. Check whether the action is a local function or an AI-powered one, such as transcription, summarization, or file question-answering.
- Find the recipient and payload. Look for the model provider and the kinds of content sent, including excerpts or conversation context.
- Check the account terms. Confirm whether your consumer, business, or API account has different training, retention, and deletion rules.
- Review controls and defaults. Find settings for model improvement, temporary chats, sync, deletion, access permissions, and connected tools.
- Minimize what you send. Remove identifiers or unrelated sensitive details when they are not needed for the task.
Huawei Consumer Business’s 2019 AI Privacy White Paper expresses a useful general principle: personal data should be kept no longer than necessary for its processing purpose. The paper also advocates device-side processing first, separating personal identifiers from cloud data, and limiting third-party processing. These are principles, not proof that a particular product follows them.
What the available product information can establish
Provider policies and product documentation can tell you what an organization says about storage, processing, and controls; they do not independently establish that the technical implementation matches those statements. The examples here do not support a universal claim that any storage arrangement is “completely private,” nor do they provide an independent comparative test across AI services. For a sensitive workflow, rely on the specific feature’s documented data flow and the terms for the account you actually use.
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