The biggest question surrounding personal AI agents may not be what they can do.

It may be what they need access to in order to do it.

Meta's Muse is designed to perform tasks across a user's digital life, including email, travel, shopping and other connected services. That naturally creates privacy questions.

How does Meta say Muse protects privacy?

Meta says Muse runs inside a dedicated Muse Secure VM, where the agent and user data are isolated.

The company says credentials are stored securely so Muse cannot directly see passwords or payment information. It also says a separate Sentinel agent controls internet access and that sensitive actions require user approval.

Meta also says users can control which applications Muse connects to and whether interactions are used to train its AI models.

Why privacy is different with agents

A chatbot might see one question.

An AI agent could potentially interact with dozens of applications over time.

That creates a much richer picture of a person's life.

An agent connected to email, calendar, shopping and travel data could infer preferences, relationships, routines and priorities even when the user never explicitly provides that information.

This makes permission architecture critical.

Users should be able to see:

What the agent can access

What it has accessed

What actions it has taken

Which permissions can be revoked

What information is retained

When human approval is required

The bigger issue

Privacy for AI agents cannot depend only on a company's privacy policy.

It needs to be reflected in the technical architecture.

Isolation, least-privilege access, credential vaults, audit logs and explicit user approvals should be part of the system itself.

Muse shows where personal AI is heading.

The more useful an agent becomes, the more important it becomes to control exactly what it knows and what it can do.