The little creature staring back from Meta’s new artificial-intelligence product looks as if it belongs in a children’s cartoon. That is part of the point. Muse, Meta’s new personal AI agent, is designed to feel more like a personal companion. Its mascot, Jolly, gives the system a friendly face while Muse promises to handle the tedious machinery of modern life: booking travel, sending email, shopping, navigating websites, managing tasks and remembering the details that humans routinely forget.
The proposition is simple. Tell Muse what you need. Give it access to the services you use. Let it work while you get on with your life. But there is a less cuddly side to that bargain.
For Muse to become the kind of assistant Meta envisions, it needs something that previous generations of chatbots did not require at anything like the same scale: a detailed map of your life.
Your email. Your calendar. Your shopping. Your social relationships. Your preferences. Your conversations. Potentially your finances and health information. And, through Meta’s expanding family of AI glasses, what you see and hear in the physical world.
The central privacy question around Muse is therefore not simply whether Meta can read a particular message or photograph. It is whether consumers are being asked to construct a permanent, machine-readable portrait of themselves in exchange for convenience. Meta says it has built Muse precisely to address that concern.
When the company introduced the service in September, it said each user would receive a dedicated virtual machine in the cloud containing the person’s data, credentials and AI agent. Meta says users choose which applications Muse can connect to and how much authority it receives. The company says Muse cannot see passwords or payment details stored in its secure credential system, asks for confirmation before sensitive actions such as purchases and email, and maintains an audit trail of what it does.
Meta also says Muse conversations and data stored in its virtual machine are not shared with its advertising systems. Users can disconnect services, delete memories and opt out of having their interactions used to train Meta’s AI models.
Those are meaningful safeguards. They do not, however, answer the larger question: how much information does an AI need to know before it stops being an assistant and starts becoming a digital biography?
The assistant that remembers everyone
Recent reporting on Muse has made that question more concrete.
Researchers who examined files extracted from the system found instructions describing a process for maintaining pages about people in a user’s life. The material reportedly included categories such as facts, history, the nature of a relationship and possible ways to strengthen it. The people involved could include family members, partners, friends, colleagues and people a user follows.
That is a striking departure from the conventional chatbot.
A chatbot generally waits for you to tell it something. A personal agent is designed to connect things you told it yesterday with something you do today.
Knowing that someone named David is your plumber is relatively innocuous. Knowing that David is your plumber, that he charged you £800 last year, that you complained about his work, that he has a child who attends a particular school and that you are considering hiring him again is much more consequential.
The information becomes more sensitive when the system begins inferring relationships.
Is someone a close friend? An estranged relative? A romantic partner? A former colleague? Someone you are avoiding?
An AI does not necessarily need you to state those things explicitly. It can infer them from calendars, messages, photographs, purchases and the language you use.
Carissa Véliz, an associate professor at Oxford University’s Institute for Ethics in AI, told Wired that people are increasingly giving AI systems information that the systems can also infer or assemble from different sources.
That changes the economics of personal data.
The valuable information is no longer merely what a person voluntarily types into a box. It is the relationships among thousands of apparently insignificant facts.
A dinner reservation can reveal a friendship. A calendar entry can reveal a medical appointment. A recurring payment can reveal a subscription. An email can reveal a dispute. A shopping history can reveal a pregnancy, a divorce, a new job or a financial problem long before a person tells anyone.
The more capable the agent becomes, the more useful those connections are. And therefore the more valuable the resulting data becomes.
Meta says privacy is the architecture
Meta is clearly aware of the problem. Its own security documentation acknowledges that Muse’s usefulness depends on having access to highly personal information. The company says each user gets a dedicated cloud computer, isolated from other users, where data and credentials are stored. It says a separate security system, called Sentinel, controls what Muse can send to the internet.
But Meta’s documentation also contains an important qualification.
At launch, the company said its architecture does not prevent Meta from accessing data when necessary to support, secure or operate the service. Meta’s planned Confidential VM is intended to change that by cryptographically preventing even Meta from accessing information stored in the user’s virtual machine. That distinction is critical.
There is a major difference between saying a company does not normally access your data and saying the company technically cannot access your data.
The former is a policy. The latter is an architectural constraint. Meta says it is moving toward the second model.
For a company whose business was built in part around turning vast quantities of user information into advertising and personalization, that represents a remarkable change in posture.
But it also exposes the paradox at the heart of the AI-agent business.
The more privacy-conscious the system becomes, the less the company can see.
The less the company can see, the harder it becomes to use that information for other purposes.
And yet the entire value proposition of a personal AI depends on accumulating an extraordinary amount of context.
The data problem is bigger than Meta
This is not exclusively a Meta problem. OpenAI, Google, Anthropic, Apple and a growing collection of startups are pursuing increasingly agentic systems that can move beyond answering questions and actually perform tasks.
The difference is that an agent needs permissions. A chatbot can tell you how to book a flight.
An agent needs access to the airline website, your travel preferences, your calendar and potentially your payment method to book it for you.
A chatbot can suggest what to buy. An agent can purchase it. A chatbot can explain an email. An agent can reply to it.
Each step eliminates friction. Each step also increases the consequences of a mistake. Muse has already illustrated that problem.
In one widely reported incident, the system, operating on Facebook Marketplace, disclosed a user’s home address to a prospective buyer without the user realizing that Muse had permission to do so. Meta subsequently acknowledged the incident and said it was examining how the permission settings worked.
The episode is revealing because it was not necessarily a conventional hacking incident.
The system appears to have been operating with permissions the user had granted. The problem was what those permissions meant in practice.
That may prove to be one of the defining security problems of agentic AI.
The question will not simply be, “Did the hacker break in?” It will be, “What exactly did the user authorize the machine to do?”
The password is no longer the main problem
Traditional cybersecurity has focused heavily on credentials.
Protect the password. Protect the credit card. Protect the authentication token.
Muse attempts to address that problem through a separate credential store. Meta says the agent can use credentials without being able to see the underlying passwords or payment information.
But the next generation of attacks may not need to steal a password. They may manipulate the agent.
An AI that can read an email, visit a website, interpret instructions and take actions is vulnerable to a different class of attack known as prompt injection.
A malicious webpage or message can contain instructions designed to influence an AI agent into doing something its owner did not intend.
Meta says it has trained Muse to recognize prompt-injection attacks and has built security systems around the agent. The company also operates a bug-bounty program offering up to $300,000 for qualifying vulnerabilities, including successful prompt-injection attacks.
But no security architecture eliminates the fundamental risk.
The machine has agency. And agency means there is something worth manipulating. Then come the glasses
The privacy implications become more complicated when Muse leaves the phone.
At Meta Connect in September, the company announced that Muse would come to its AI glasses, allowing people to interact with the agent hands-free.
Meta’s vision is easy to understand. A phone knows what you type.
An AI assistant connected to glasses can potentially understand what you see and hear.
Meta says its glasses have a capture LED that signals when photographs or video are being recorded, and that the camera is disabled when the LED is covered or tampered with.
But the existence of a visible recording light does not settle the broader privacy question.
A pair of glasses equipped with microphones, cameras and an AI assistant can potentially become an always-nearby sensor for the world around its wearer.
The person wearing the glasses has made a choice. The people around that person have not. That has already triggered regulatory scrutiny.
In May, Texas Attorney General Ken Paxton announced an investigation into Meta’s AI glasses over concerns about privacy, recordings and the collection of facial information.
And this month, Norway said it planned to propose a temporary ban on AI glasses in some public spaces because of concerns that people could be recorded without their knowledge.
In the Netherlands and Belgium, retailer Hans Anders suspended sales of Meta’s smart glasses amid growing privacy concerns, while Dutch privacy authorities warned about the difficulty of obtaining consent from identifiable people recorded in public.
The glasses therefore expose a fundamental flaw in the idea that privacy is merely a matter of user settings.
The user is not always the only person being observed.
Cute is a feature
This is where Muse’s cuddly aesthetic becomes more than a marketing curiosity. Technology companies have spent decades teaching consumers to distrust machines that appear intrusive.
They have also learned that people interact differently with machines that appear friendly.
The Jolly character gives Muse a personality. It makes an infrastructure of permissions, connectors, virtual machines and cloud storage feel more like a relationship.
That is useful. It may also be psychologically powerful. A machine that looks like software invites scrutiny.
A machine that feels like a helpful companion invites disclosure.
The distinction matters because people routinely tell conversational systems things they would not enter into a conventional database.
The danger is not necessarily that Muse will secretly steal a user’s life. It is that the user will voluntarily hand it over.
That may be the more durable business model for personal AI.
Meta does not need to persuade someone to upload a 10,000-page dossier about themselves.
It needs to make the assistant sufficiently useful that the user connects Gmail, calendars, Facebook, Instagram, shopping accounts and other services one at a time.
Each connection seems reasonable.
Together they form something much more powerful. A model of a person.
The new bargain
Meta’s AI strategy is therefore built around an unusually intimate bargain. The company promises to do more for you if you let its AI know more about you.
That bargain could be enormously valuable. It could also become one of the most consequential privacy experiments in consumer technology.
The old social-media bargain was relatively straightforward: give us your attention and some personal information, and we will give you a free service.
The AI-agent bargain is different. Give us enough access to your life, and we will give you back your time.
That is a much more compelling proposition. It is also much harder to evaluate.
A social network knows what you clicked. A personal agent may know why you clicked.
A social network knows that you bought a plane ticket.
A personal agent may know where you are going, whom you are visiting, why you are going and what you were worried about when you booked it.
That is the difference between a database of behavior and a model of a human being.
Meta insists that users remain in control. The company says they can inspect and delete memories, disconnect services and opt out of AI-training use. It is also developing a Confidential VM intended to make even Meta technically incapable of accessing the information inside it.
Those protections matter. But the ultimate test will be whether users understand what they have authorized before they click “allow.”
Muse is cute. It is convenient. It can save time. And the better it becomes, the more tempting it will be to give it another permission.
Then another. Until the assistant knows not merely what you want it to do, but who you are, who you love, where you go, what you buy, what you worry about and what you intend to do next.
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