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Choose an AI tool by matching its data practices and safeguards to the information you will share and the harm a wrong answer could cause. There is no universally safest or most private AI service: policies and controls vary by product, plan, account, workspace, and jurisdiction. Check the exact terms and settings you will use, minimize sensitive inputs, and test whether the tool is reliable enough for the task.
What “safe” and “private” mean for an AI tool
Privacy is only one part of trustworthiness. The National Institute of Standards and Technology (NIST) identifies characteristics that include validity and reliability, safety, security and resilience, accountability and transparency, explainability, privacy enhancement, and management of harmful bias. A tool may have a useful privacy control and still produce unreliable or unsafe answers.
NIST’s AI Risk Management Framework (AI RMF) is voluntary guidance for managing risks during AI design, development, deployment, use, and evaluation—not a certification or guarantee that a product is safe. Its four functions are Govern, Map, Measure, and Manage. NIST released AI RMF 1.0 on January 26, 2023, and its Generative AI Profile on July 26, 2024. NIST says AI RMF 1.0 is under revision. See the NIST AI Risk Management Framework and its AI RMF FAQ.
Is it safe to put personal information into an AI chatbot?
Only share personal information if the exact service, account arrangement, and settings are suitable for that information. A prompt is not the only data to consider: uploaded files, connected apps, integrations, and related telemetry may also matter. Before sharing, ask who can access the information, how long it may be retained, what deletion means, and whether the data may be used for model improvement or reviewed for safety.
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For low-consequence brainstorming, the risk may be different from using AI with medical, employment, financial, legal, or confidential business information. Those are examples of higher-consequence contexts, not a claim that any particular provider is approved for them. Do not upload material unless the provider and your account or organizational arrangement are approved for that data.
Do AI tools use my chats to train their models?
It depends on the provider, product, account tier, settings, and sometimes workspace administration. “Training” or “model improvement” controls do not necessarily control retention, chat history, safety review, or deletion.
ChatGPT consumer settings
OpenAI says turning off “Improve the model for everyone” means new conversations are not used to train its models, but those conversations can still appear in chat history. The availability of controls depends on account, plan, and workspace settings. Consult OpenAI’s Data Controls FAQ and confirm the setting in the account you will actually use.
OpenAI business and API products
OpenAI says content from ChatGPT Business, Enterprise, Edu, ChatGPT for Healthcare workspaces, and the API Platform is not used by default to improve its models. That statement alone does not settle every question about retention, access, or contractual terms. Review the applicable documentation and agreement for the specific product. See OpenAI’s business data privacy page.
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Anthropic’s retention disclosures distinguish consumer products from organizational use and describe exceptions for flagged trust-and-safety cases. Organization policies and custom Enterprise retention controls are addressed separately. Do not transfer a consumer-plan statement to Enterprise or API use, or the reverse. Check Anthropic’s consumer data-retention information and organization data-retention information.
These are provider-authored descriptions, not independent audits. Policies and product controls can change; verify current terms for your region and account before relying on them.
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How to compare AI tools for privacy and safety
Compare the actual products and account tiers you are considering. A useful comparison records the answer, the policy or contract that supports it, and the date you checked it.
| What to check | Questions to answer |
|---|---|
| Data sent | What do prompts, files, connected apps, integrations, and telemetry send? Can you avoid sending unnecessary information? |
| Model improvement | Are chats or other inputs used to improve models? Is the setting opt-in or opt-out, and does it apply to this specific product and account? |
| Retention and deletion | How long are inputs and outputs kept? What happens after deletion? Are there exceptions for safety reviews, legal obligations, or backups? |
| Access and sharing | Who at the provider or organization can access the data? Are third parties or connected services involved? |
| Account and administration | Is this a consumer, business, education, Enterprise, or API product? Can an administrator set or restrict privacy controls? |
| Security and incidents | What access controls and security disclosures apply? How does the provider describe incident handling? |
| Reliability and impact | Does the tool perform adequately on representative tasks? What harm could follow from an incorrect, biased, or unsafe answer? |
| Transparency and oversight | What testing evidence and policy details are available? Can you monitor use, require review, and change or stop the workflow? |
The cited NIST guidance identifies these as relevant risk areas, but it does not provide a common independent scorecard for vendors. Treat a provider’s policy as a description of its practices, not proof of comparative safety.
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A practical process for choosing a tool
- Define the task and consequences. Write down what the tool will do and what could go wrong if its output is inaccurate, biased, exposed, or acted on without review.
- Classify the information. Identify sensitive or confidential details in prompts, files, and connected services. Remove identifiers and details that are not needed. Do not submit information unless the provider and account arrangement are approved for it.
- Read terms for the exact product. Check current policies and agreements for your region, plan, and workspace. Look specifically for model-improvement use, retention, deletion, safety review, and data sent to third parties or integrations.
- Verify the account settings. Confirm the controls in the account you will use; an advertised option may be unavailable or administrator-controlled. Record the policy version or date and the setting state on which your decision depends.
- Test and govern the use. Try representative tasks, verify outputs before acting, and require human review for consequential decisions. Reassess if the workflow, account configuration, or provider policy changes. NIST’s AI RMF and Generative AI Profile offer voluntary guidance for organizing this risk work; the profile discusses data protection, retention, opt-outs, third-party data risks, acceptable-use policies, and iterative testing. See the NIST Generative AI Profile.
Reduce risk by sharing less
Privacy improves when an AI tool receives less sensitive information in the first place. Remove names, contact details, account numbers, or other identifiers when they are not needed; summarize or redact confidential material; and avoid connecting data sources that the task does not require. NIST notes that privacy-enhancing technologies and approaches such as de-identification and aggregation may support privacy-enhanced AI systems. These techniques reduce exposure but do not, by themselves, establish that a particular tool or use is safe.
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