Meta Muse is not simply a chatbot with a privacy toggle: Meta describes it as an agent that can work inside a virtual machine, connect to services you choose and take actions on your behalf. Meta says Muse conversations and virtual-machine data are not shared with its ad systems, but also says sanitized interaction trajectories are used to train new model checkpoints by default unless you opt out. ChatGPT and Claude offer their own consumer model-improvement controls, but those controls are not promises that nothing is stored. There is not enough comparable evidence to name one assistant the most private.
This comparison reflects information available on October 4, 2026. It focuses on consumer services and distinguishes company statements about policy and architecture from independent reporting. It is not a technical audit of what each service actually collects, retains, or exposes.
What makes Muse’s privacy question different?
Meta announced Muse on September 8, 2026, describing it as a personal AI agent available through its own app or WhatsApp. Unlike a chat assistant limited to the text or files a user submits in a conversation, Muse is designed to work across connected services and continue tasks after the app is closed. Meta says it runs in a dedicated virtual machine (VM), can use a browser, and keeps the agent and user data in that environment.
That distinction matters: connecting a service may give an agent access to information beyond a single prompt, and authorizing it to act creates a separate risk from allowing it to answer questions. Meta says users choose which services to connect and the access to grant; sensitive actions, such as sending email or making a purchase, require approval. Meta also says users can change or disconnect service access. These are Meta’s descriptions of Muse’s design, not independent verification of its security.
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How the services compare on the evidence available
The table compares only what is established for the consumer services in this snapshot. “Not established” means the available evidence does not support a reliable, current comparison on that point; it does not mean a company has no policy or control.
| Service | Data access and permissions | Model training controls | Retention, review, and advertising |
|---|---|---|---|
| Meta Muse | Meta says Muse runs in a dedicated VM, can use a browser, and can connect to services selected by the user. Meta says sensitive actions need approval and connections can be changed or disconnected. | Meta says sanitized interaction trajectories—including conversations, tool calls, and subagent handoffs—help train new model checkpoints by default. A setting lets users opt out. | Meta says conversations and VM data are not shared with Meta ad systems. The available materials do not establish a complete retention, deletion, or human-review comparison. The training opt-out is not a statement that data is never stored or that already-trained models are undone. |
| ChatGPT, consumer | Connected-service access and permission granularity are not established in this comparison. | OpenAI’s consumer documentation describes a setting to turn off model improvement for new conversations. Temporary Chat is not used to train models. | The training controls do not establish that chats are never stored or reviewed. OpenAI documentation describes special treatment for feedback users submit; a full retention and advertising comparison is not established here. |
| Claude, consumer | Connected-service access and permission granularity are not established in this comparison. | Anthropic says users can adjust whether data may be used to improve Claude, and that Incognito chats are not used to improve Claude. | Anthropic says changing a preference stops future use of previously stored chats and coding sessions in future training runs, but does not remove data from training already started or models already trained. A full retention, human-review, and advertising comparison is not established here. |
| Gemini | Not established in this comparison. | Not established in this comparison. | Not established in this comparison. The App Store disclosure count below is not a substitute for these details. |
These are consumer-service findings. They do not establish that business or enterprise accounts follow identical settings, data handling, or terms, nor do they resolve every regional difference.
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Does Muse use conversations to train its AI?
According to Meta’s safety and security explanation, yes by default: sanitized Muse interaction trajectories can be used to train new model checkpoints. Meta says it removes key personally identifiable information before using them and provides an opt-out switch in Muse settings. A trajectory can include more than the words in a chat: Meta’s description includes tool calls and handoffs between subagents.
Training use is distinct from advertising use. Meta says, “Muse doesn’t share your conversations or the data in your Virtual Machine with Meta ad systems.” The statement does not say that the information is never stored, that no human can review it, or that opting out deletes material already used to train a model. The available materials do not establish those broader conclusions.
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What does Meta’s broader AI policy add?
Meta’s general generative-AI privacy explainer says publicly shared Instagram and Facebook posts, including photos and text, were used to train the models underlying its generative-AI features. It says private posts were not used, and private messages with friends and family are not used unless someone in the conversation chooses to share them with Meta AI. Meta also says information people share while interacting with generative-AI features may be used to improve products and for other purposes.
Those broad statements concern Meta’s generative-AI features generally; they are not interchangeable with the more specific Muse statements about VM data, Muse conversations, and Meta’s ad systems. Treat them as separate data-use questions rather than assuming one answer resolves the others.
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What do App Store privacy-label counts show?
Surfshark’s comparison, as reported by TechRadar, counted categories disclosed in Apple App Store privacy labels for 13 AI chatbots. The reported figures were:
| App | Disclosed data categories counted |
|---|---|
| Meta Muse | 31 of 35 — Surfshark, 2026 |
| Meta AI | 33 of 35 — Surfshark, 2026 |
| Google Gemini | 24 of 35 — Surfshark, 2026 |
| ChatGPT | 17 of 35 — Surfshark, 2026 |
These are counts of disclosed categories, not measures of how much data an app collects, how sensitive it is, how long it is retained, whether it is used for training, or how well access controls work. They are not a complete privacy score, and the count alone cannot show which service is safest for a particular person.
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What the available evidence does not establish
- One overall privacy winner: The evidence does not provide a like-for-like audit of actual data flows across all services.
- A complete ranking of competitors: Current, comparable details for Gemini, Copilot, Perplexity, and other assistants—including collection categories, retention, human review, and account-tier differences—are not established here.
- That a training opt-out means “nothing is kept”: The controls described concern model improvement, not every form of storage, review, or deletion.
- That a company’s security description is independently verified: Product and policy statements are not the same as an independent technical assessment.
What reported Muse access incident should users know about?
Tom’s Hardware reported that journalist Jason Aten said Muse appeared to sync a local Messages database and surfaced message context, although he said he had not granted permission. That is a reported incident, not proof that all users or devices have the same exposure, and it does not by itself establish the technical cause. It is a reason to scrutinize connected-service permissions and observe what an agent can access, rather than a basis for a universal claim.
Meta’s own safety article also acknowledges a broader agent risk: “Prompt injection remains an open problem in the industry — and Muse will sometimes make mistakes.” This matters when an assistant can browse or act on information encountered outside the conversation: approval prompts and permission settings reduce some risk, but they do not make agent behavior infallible.
How to reduce exposure before connecting an assistant
- Start with the service’s current controls. In Muse, review the connected services and permissions, and find the setting for whether interactions can be used to train Meta AI models. In ChatGPT or Claude, check the current model-improvement and temporary or Incognito chat controls; interface names can change.
- Connect only what the task requires. Avoid granting email, calendar, social, or payment access until you have checked the requested permissions and considered whether the task can be done without that connection.
- Test access with low-risk information. Before relying on an agent for sensitive work, check what it can see and what it asks you to approve. Do not use sensitive content as a test.
- Review and revoke access you no longer need. Check the connected-app list after setup and disconnect services that are unnecessary. For Muse, Meta says users can change access or disconnect a service.
- Keep training, deletion, and storage separate in your decision. Check each control independently. An opt-out from future model improvement does not establish that stored data has been deleted or that a model already trained on data has been reversed.
Availability and account scope
The Associated Press described Muse at launch as available in the United States to adults 18 and older. That is a dated launch snapshot, not a guarantee of current availability elsewhere or current eligibility. Availability can change; check Meta’s current product information and in-app access for your location. The evidence summarized here does not establish a full regional or business-account policy comparison.
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