Before using an AI chatbot, check what it collects, how it uses and shares your conversations, how long it keeps them, and what controls apply to your exact account and plan. Then consider security protections and AI-specific risks—especially if you will upload files or connect other services. Avoid sharing sensitive information unless you have a clear need and have verified the applicable terms and settings.
Start with the exact chatbot, plan, and account type
Privacy and security practices can differ by product, plan, account type, region, and settings. Evaluate the version you intend to use—not a provider’s general reputation or a claim that may apply only to another product. For each chatbot, inspect its current privacy notice, terms, security documentation, and in-product controls. Note when those documents were updated and whether the provider explains how it will notify users of material changes.
The Federal Trade Commission (FTC) says AI companies’ privacy commitments apply across their communications. Undisclosed secondary uses or material omissions about data practices can create legal risk. This is U.S. consumer-protection guidance, not individualized legal advice or a determination of which laws apply to your particular situation. FTC: AI Companies: Uphold Your Privacy and Confidentiality Commitments.
Find out what happens to prompts, files, and connected data
Read the privacy notice and terms for a clear account of what the service receives and why. Consider more than typed prompts: files you upload, feedback you submit, account details, and information accessed through connected services may also be involved. Look for whether conversations can be reviewed by people, processed by service providers, disclosed to other parties, or used to improve or train models.
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- Collection: Which input, account, and connected-service data does the service receive?
- Purpose: Are conversations used to provide the service, improve models, or for other purposes?
- Human and third-party access: Can staff or service providers process conversations, and under what circumstances?
- Choice: Can you disable model-improvement use or other processing? Does the setting apply to new conversations only, or to existing data too?
- Scope: Do the terms or controls change by plan, account type, geography, or product feature?
Do not assume a setting or promise for one product covers every service from the same provider. For example, OpenAI says business products do not train on organization data by default and describes encryption, audits, and product-specific certifications. These are OpenAI’s statements about its business products, not independent conclusions about every product or chatbot. Check the current OpenAI business privacy information and confirm that the claims cover the product and plan you will use.
Check retention and deletion rules separately
Look for a stated retention period, how to delete conversations or an account, and exceptions for legal, security, or operational purposes. No single retention period applies to all chatbots. A delete control may remove a conversation from your view without explaining whether related records are also deleted or whether any information already used for model improvement is affected. If those details are not clear in the notice or controls, treat them as unknown rather than assuming deletion removes every copy.
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The FTC’s general data-security guidance recommends that businesses collect only what they need, keep it safe, and dispose of it securely. That principle is useful when judging whether a service’s collection and retention practices are proportionate to its purpose. FTC: Protecting Personal Information: A Guide for Business.
Assess security claims and their scope
Security documentation should help you understand how the provider protects data and accounts, responds to incidents, and limits access. Look for information on encryption in transit and at rest, access controls, account-security options, monitoring, incident handling, and independent assessments. A certification or audit is meaningful only within its stated scope: check the covered service, features, locations, and period rather than treating a badge as proof that every part of a chatbot is secure.
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NIST frames AI security around the confidentiality, integrity, and availability of AI systems and their training and output data, as well as the software and hardware underneath them. It also notes that existing frameworks do not comprehensively address some AI-specific attacks, including evasion, model extraction, and membership inference. Its guidance is voluntary risk-management and technical framing, not a guarantee that a particular chatbot is safe. NIST: AI Research – Security and Resilience.
Account for risks from files, retrieval, and integrations
A chatbot that accepts files, retrieves external content, or connects to tools and other services has additional paths through which instructions or data may flow. Malicious instructions embedded in content can attempt to influence the chatbot or connected tools. Other concerns include data exposure, unauthorized access, and unreliable outputs. Ask what permissions integrations receive, whether you can restrict or revoke them, and what protections apply to content the chatbot retrieves or processes.
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NIST’s NCCoE chatbot case study discusses prompt injection, hallucinations, data exposure, and unauthorized access, as well as approaches such as local deployment, access controls, and validation filters. It is an initial public draft describing one prototype at a point in time, not general implementation guidance. NIST IR 8579: Cybersecurity Considerations for Generative AI and Dual-Use Foundation Models.
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Data minimization limits what could be retained, misused, disclosed, or exposed. Unless there is a clear need and you have checked the applicable terms and controls, do not submit:
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- Passwords, authentication codes, or payment credentials.
- Unnecessary health or financial details.
- Confidential work material, trade secrets, or internal documents.
- Personal information about another person without a valid reason and appropriate authorization.
When possible, remove names, account numbers, identifying details, and confidential passages before asking for help. Consider whether a generic description will answer your question just as well.
Apply extra scrutiny to chatbots used by children and teens
For youth-facing or companion-style chatbots, review age limits, what users and parents are told, how conversation data is handled, and whether safety testing and monitoring are described. In September 2025, the FTC announced an inquiry seeking information from chatbot companies about data collection and handling, use or sharing of conversation information, age restrictions, and testing and monitoring. The announcement describes an inquiry—not a finding that every named or other chatbot violated a rule. FTC: FTC Launches Inquiry into AI Chatbot Companies.
Compare services with the same checklist
Use the same questions for each service so that a polished security page or a broad privacy promise does not distract from gaps elsewhere. Record the answers and the document or control that supports each one.
| What to compare | What to verify |
|---|---|
| Data and purposes | Collected inputs and account data; model-improvement use; human review; service-provider processing; other sharing. |
| Choices and scope | Whether controls differ by plan, account, setting, feature, or region, and which data each control affects. |
| Retention and deletion | Stated retention, deletion process, exceptions, and what deletion does or does not cover. |
| Security | Encryption, access controls, account protections, monitoring, and incident information. |
| Independent assurance | Which product, feature, and period an assessment or certification actually covers. |
| AI features | File uploads, retrieval, integrations, permissions, and protections against AI-specific risks. |
| Clarity and users | Whether disclosures are understandable and accessible to the people who will use the service, including young users and parents. |
NIST’s AI Risk Management Framework is designed to help developers, users, and evaluators manage AI risks. It treats characteristics such as security, privacy, transparency, and reliability as considerations across the AI lifecycle. It can provide a broader lens for evaluation, but it does not replace reading the chatbot’s own terms and controls. NIST: AI Risk Management Framework.
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