When Must Your Utah Chatbot Tell People It Is AI?

What should you check before you act?

If your organization is considering Utah generative-AI disclosures, the first question is not whether a vendor says its tool is compliant. It is whether the rules actually apply to your intended use, users and decisions. A focused assessment starts with the facts that determine the relevant legal test.

This guide is intended for utah-facing consumer and regulated-service chatbots. It offers a decision framework and primary references; it does not provide an individual legal opinion or guarantee a business outcome.

What might a Utah chatbot disclosure-scope review include?

A defined documentary review, if the relevant professional capability is available and the engagement is accepted, may examine consumer requests, high-risk professional interactions and mental-health chatbot rules. The scope should be agreed from the business facts before anyone describes a legal or technical conclusion.

• Initial fact and intended-use questionnaire
• Applicable primary-source and exception mapping
• Evidence and supplier-information request list
• Decision and risk matrix with unresolved questions
• Action owners and practical next-review triggers

Have a specific business question? Describe the tool and decision you need to make. Do not send confidential records in an initial message.

From your business question to a reviewable action plan

1. Define: identify the system, owner, users and relevant location.
2. Check: match the facts against the controlling rule or published standard.
3. Document: identify evidence, supplier dependencies and unresolved questions.
4. Act: allocate proportionate changes and appropriate professional referrals.

The process is a proposed structure for assessment, not a claim that a particular service has already been commissioned, performed or validated for your organization.

What should utah-facing consumer and regulated-service chatbots verify?

This research is organized around consumer requests, high-risk professional interactions and mental-health chatbot rules. It distinguishes a legal rule or published standard from a proposed internal control, and it does not assume that the same answer applies to every organization. Each recommendation below should be tested against the actual use case, competent authority, provider arrangement and currently operative text.

Which Utah AI law is current?

Utah moved its general generative-AI consumer disclosure rules to Title 13, Chapter 77, effective in 2025 and amended in 2026. The older section 13-2-12 should not be quoted as current law. Separate provisions address mental-health chatbots. Begin by classifying the business, regulated service and technology instead of treating any AI-powered response as subject to a universal first-message disclosure.

Legal test: Identify the statutory or published-standard provision and record whether the relevant condition is satisfied, not satisfied or unclear. Describe the product configuration that supports the finding. If the cited material is guidance rather than binding law, label that distinction explicitly.

When must a consumer chatbot answer honestly?

Chapter 77 provides that a supplier using generative AI in connection with a consumer transaction must disclose that the interaction is with AI rather than a person if an individual clearly asks or prompts about that fact. Design the interface so a clear question receives a clear, accurate answer. Do not extrapolate from this rule that every consumer chat must automatically begin with the same sentence.

User decision: Record the specific decision a business owner must make, who is affected, and what changes if the answer is yes rather than no. Avoid converting a broad information query into a promise of a predetermined legal outcome.

What changes for licensed professional services?

The statute uses a separate rule for a regulated occupation where the generative-AI interaction is high risk. Those circumstances can involve sensitive information or recommendations affecting significant personal decisions. The law describes up-front verbal or written disclosure depending on communication type. Determine which service is provided, who is licensed and whether the particular interaction falls within the statutory definition.

Risk boundary: Review reasonably foreseeable failure scenarios, including inaccurate outputs, access to sensitive information and unexpected use of the tool. Determine which risks are within the narrow question addressed by this guide and which require another professional review.

Are mental-health chatbots treated differently?

Utah’s mental-health chatbot provision includes conspicuous disclosure before access, recurring disclosure after a stated period of inactivity and an answer when a user directly asks. This is not the same legal category as a general customer-support assistant. Review the statute’s scope, exact feature set and user experience; mental-health claims require particularly careful handling to avoid implying equivalence with a licensed professional.

Source evidence: Keep a dated copy of the relevant official publication or applicable rule, plus a pointer to the product record used for assessment. Record any exceptions and unresolved facts; an unattributed checklist should not be treated as proof of legal compliance.

Does an AI answer transfer responsibility to the model?

Utah’s current consumer-protection chapter rejects the idea that use of generative AI alone excuses a business from consumer-protection obligations. Identify which statements are made in sales, advice or professional contexts and who authorizes or reviews them. A transparent AI disclosure cannot cure false advertising, unauthorized professional practice or another independent legal violation.

Implementation check: Test a real user or employee workflow rather than relying only on product documentation. Note any user prompts, notice screens, approval steps, logs or fallback procedures that show how the control actually functions.

What records support a disclosure review?

Document conversational entry points, channels, regulated services, automated responses to direct AI questions, notification screenshots, human escalation and versioned prompt instructions. For high-risk interactions, record the first disclosure and process for responding to unexpected sensitive issues. Maintain test cases across text, audio and any translated interfaces rather than assuming a visual banner works everywhere.

Supplier responsibility: Where a third party provides components, map who controls the interface, model settings, operational logs and user communications. A service agreement may allocate tasks but does not in itself remove statutory duties.

What controls should be tested with vendors?

A provider may host the model while the business controls the chatbot interface. The operator should confirm whether a user’s direct question can be intercepted by a scripted response, whether a model may wrongly identify itself as human and whether fallback routing is available. Contract obligations for updates and log access can matter. Specific service design controls the appropriate solution.

Cross-border distinction: Check whether the same action reaches users in more than one jurisdiction or a regulated sector. Record each legal source separately and avoid copying a jurisdiction-specific conclusion into another country or state.

How should nationwide teams handle Utah?

A business may serve Utah users alongside customers in Colorado or the EU, where separate disclosure and chatbot rules apply. Build a state-and-channel matrix with triggers rather than issuing identical legal explanations. Keep the current Chapter 77 rule separate from the state’s mental-health chatbot provision and update the page when Utah law or official interpretation changes.

Decision record: End with a clearly owned action, the evidence required to close it, a named reviewer role and a truthful next review trigger. If a core fact is unknown, record the gap instead of concluding that the activity is automatically lawful.

Decision matrix: which facts change the answer?

The scenarios above illustrate why consumer requests, high-risk professional interactions and mental-health chatbot rules must be evaluated from actual facts. They are not universal exemptions or approvals. A change to who operates the AI system, who receives an output or how a decision is made can change the relevant law or professional obligation.

Evidence checklist for a scoped review

• Use Utah Chapter 77 rather than repealed section 13-2-12
• Record whether customer expressly asks about AI
• Identify regulated occupation and high-risk interaction
• Apply mental-health chatbot duties separately

For each checklist item, note the source, business owner, current evidence, unresolved question, action required and review trigger. This creates an auditable working list without claiming that a standard list replaces legal analysis or technical testing. Review the evidence again after a product release, change of vendor, expansion into a new market or relevant official update.

Limitations and alternatives to a standalone review

Some problems can be answered by the official source and a simple internal inventory. Others require qualified local legal counsel, specialist technical testing, independent auditing or consultation with a regulator. A documentary readiness review can identify gaps and prepare questions but should not be described as a certification, official approval or representation in a proceeding unless the actual engagement and qualifications support that description.

Start with the narrow decision in this guide, then follow its related links for adjacent questions. Recheck the current version of each source before taking action. Laws and professional obligations can change after this page is updated, and a service may have multiple functions with materially different regulatory treatment.

Related guides for the next decision

• Ai Law
• Ai Agent Customer Service Compliance
• Colorado Ai Chatbot Safety Law Readiness