Private AI Assistant or Public AI Persona?

Private Productivity Assistant or Public AI Persona?

Keep an AI assistant private unless a real user need justifies a wider audience and the system has identity, consent, disclosure, data, moderation, support, incident, and shutdown controls for that audience. Public reach is not the natural next step after a successful private test.

A public persona may use the same model as a private assistant. It is still a different product because strangers and bystanders enter the system.

flowchart TD
    A["Define the job"] --> B["Choose the smallest useful audience"]
    B --> C{"Does it represent a person or organization?"}
    C -->|No| D["Review data, access, and support"]
    C -->|Yes| E["Add consent, identity, and authorized-speech rules"]
    D --> F{"Can strangers interact?"}
    E --> F
    F -->|No| G["Private or limited pilot"]
    F -->|Yes| H["Moderation, abuse, reporting, incident, shutdown"]
    H --> I{"Controls tested and owned?"}
    I -->|No| J["Do not launch"]
    I -->|Yes| K["Bounded public release"]

Audience changes the work

A private productivity assistant serves one person or a controlled group. Its main concerns may be source quality, confidentiality, access, and review.

A public AI persona faces people the creator does not know. Users may be young, distressed, hostile, confused, or seeking advice. They may mention other people who never agreed to the interaction. They may treat an avatar or creator link as evidence that the response is personal, official, or expert.

The public system therefore needs more than a share link. It needs an operating model.

Start with the smallest useful audience

The available choices are not simply private or public.

A system can remain personal, be shared with named testers, be limited to a team or organization, be visible to a restricted social audience, be open to everyone, or not launch.

Current Google Gem sharing documentation describes several access levels that may include specific people, an organization, anyone with a link, public access, or private access, depending on the account. Current Meta help describes AI Studio audience choices that may include Only Me, Close Friends, and Everyone.

Choose the narrowest audience that accomplishes the job. Expansion should require new evidence.

Decide who is speaking

A private outline assistant does not need to perform a personality. A public persona often has a name, avatar, biography, conversational style, and relationship to a creator or company.

That creates identity questions. Who authorized the representation? Which facts about the person may be used? Can the system speak in the first person? Can it make commitments? What happens when the human changes their mind or leaves the company?

Meta's 2024 AI Studio launch post described creator AIs based on Instagram content and topics to avoid. It also said creator AI responses were labeled. The current label, placement, surface, and user understanding still need live review.

A disclosure should be visible before or at the interaction, not hidden in a policy page. It should explain that the response is generated, identify the owner, and distinguish the persona from the represented human.

Obtain consent that matches the representation

If the AI represents a living person, employee, customer, guest, or recognizable community member, consent must cover the actual identity, content sources, voice, image, audience, and duration.

Permission to publish an interview is not automatically permission to train or configure a persona from it. Permission to use a photo is not automatically permission to imitate a voice. Employment does not automatically authorize a permanent synthetic version of a worker.

Record who approved the representation and how it can be withdrawn. Build removal and shutdown into the release plan.

Map the data path

Identify what users submit, what the platform collects, which account attributes are used, where conversations are stored, whether people review them, how they may improve models, whether partners receive data, and how a user can export or delete information.

Meta's current custom-AI help states that Meta uses AI interactions to improve AI at Meta. It also describes location and profile-related personalization and a possible partner path when an AI cannot answer. It says creators generally do not have access to other people's chats with their custom AI, while certain creator auto-reply uses are treated separately.

Google's Gemini Apps Privacy Hub describes activity settings, temporary chats, retention, human review, connected data, and work or school differences.

These are vendor statements and can change. They do not establish privacy compliance for a particular audience or purpose.

The data map must also include bystanders. A user may paste a private message, describe a coworker, upload a photo, or ask the persona to judge someone who never opted in.

Treat professional and companionship framing carefully

A public persona can sound confident, intimate, or caring. That tone may lead users to rely on it more than the owner intended.

Current Meta help warns that custom AIs are not licensed professionals and should not replace medical, psychological, financial, legal, or other professional advice. A label alone may not control reliance if the persona is designed to appear authoritative or emotionally reciprocal.

Do not create a fictional expert and assume the model will supply the missing qualification. Do not market simulated intimacy without reviewing age, dependency, crisis, abuse, and escalation risks.

If the concept depends on users believing a human is present, the concept needs redesign.

Build moderation before discoverability

A public system needs clear prohibited uses and a response process.

Define how the platform and owner detect harassment, sexual content, self-harm, threats, impersonation, fraud, hateful conduct, unsafe advice, prompt injection, and attempts to expose private configuration. Decide which events receive an automated block, human review, user notice, account restriction, evidence preservation, or external escalation.

Document reporting and appeal. Set a response time. Name the person or team who receives incidents. Test what happens outside business hours.

The owner should be able to limit discoverability, pause interaction, update instructions, remove source material, and shut the system down.

Plan support and accountability

Someone must own incorrect or harmful output. "The model said it" is not an operating answer.

The owner needs a way to receive user reports, inspect enough evidence to investigate, correct public information, notify affected people when appropriate, and decide whether the system can return.

If the platform does not provide the evidence or controls the owner needs, the public design may not be supportable.

NIST's Generative AI Profile treats governance, content provenance, pre-deployment testing, and incident disclosure as major risk-management considerations. Those functions become more important as the audience and consequences expand.

Compare the operating requirements

The public column is not a checklist that guarantees safety. It shows why audience is an architecture decision.

Make a documented scope choice

Write the job, smallest audience, represented identity, consent record, data map, disclosure, moderation plan, support owner, incident path, deletion method, and shutdown test.

Then choose private, named testers, organization, restricted audience, public, or do not launch.

Run the test set from [[How to Test an AI Assistant on a Bounded Task]] at the chosen audience level. A private test cannot establish that public moderation works.

E033's contrast between Curio as a productivity configuration and Curio as a social character was directionally useful. The durable lesson is not that one vendor is for work and the other is for play. It is that the audience changes identity, data, consequence, and accountability.

Use [[Gemini Gems and Meta AI Studio Product Record]] for the maintained product state. Use [[How to Design a Reusable AI Assistant]] if the actual job can remain private and bounded.

This decision guide was developed with AI assistance from E033, current Google and Meta records, the linked governance framework, and NIST guidance. Dalton Anderson remains the author. Privacy, security, safety, identity, legal, platform-policy, current-source, and founder review are mandatory before publication. Publication is not authorized.