Choose an AI tool by matching its data practices and safeguards to your specific task—not by looking for a single “safest” service. Before sharing anything, identify what information the tool will receive, what could go wrong if its answer is wrong, and what the exact product, account tier, settings, and contract say about use and retention.
What does “safe” mean for an AI tool?
Privacy is only one part of trustworthiness. The National Institute of Standards and Technology (NIST) identifies validity and reliability, safety, security and resilience, accountability and transparency, explainability, privacy enhancement, and management of harmful bias as relevant characteristics. A service could protect submitted data yet still produce unreliable or biased output; strong answers do not, by themselves, show that your data is handled appropriately.
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NIST’s voluntary AI Risk Management Framework (AI RMF) gives organizations a way to consider risk across AI design, development, deployment, use, and evaluation. Its four functions are Govern, Map, Measure, and Manage. NIST released the framework’s Generative AI Profile on July 26, 2024; the profile discusses issues including data protection, retention, opt-outs, third-party data risks, acceptable-use policies, and iterative testing. The framework is guidance, not a certification or guarantee that a product is safe. NIST AI Risk Management Framework · NIST Generative AI Profile
Is it safe to put personal information into an AI chatbot?
There is no blanket yes or no. The answer depends on the information, the task, the service’s current terms, and the settings and agreement attached to your account. Avoid entering personal, medical, financial, employment, legal, or confidential business information unless you have confirmed that the specific product and account arrangement are approved for it.
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Start by minimizing what you share. Remove names, account numbers, addresses, and other identifiers when they are not needed; replace confidential details with general descriptions; and avoid uploading a whole document when a short excerpt will do. NIST notes that privacy-enhancing technologies and approaches such as de-identification and aggregation can support privacy-enhanced AI systems. These steps reduce exposure, but they are not a substitute for checking the provider’s terms or following your organization’s rules. NIST AI RMF trustworthiness characteristics
Do AI tools use your chats to train their models?
Sometimes, and the answer can change with the provider, product, account type, settings, or workspace. “Training” or “model improvement” is also distinct from storage: opting out of training does not necessarily remove a conversation from history or prevent it from being retained for other purposes.
ChatGPT consumer accounts
OpenAI says that 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 available controls can depend on the account, plan, and workspace settings. Check the current setting and applicable policy for the account you actually use. OpenAI Data Controls FAQ
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OpenAI business and API services
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, deletion, or contractual protections; consult the documentation and agreement that apply to your service. OpenAI Enterprise Privacy
Claude consumer and organizational services
Anthropic documents consumer-product retention separately from organization policies, and describes exceptions for flagged trust-and-safety cases. It also addresses custom Enterprise retention controls separately. Do not assume a consumer-product statement applies to an organization’s Enterprise or API use, or the reverse; check the policy and configuration for the exact service. Anthropic consumer data retention · Anthropic organization data retention
These are provider-authored disclosures, not independent audits. Policies and controls can change, and details may vary by geography, integrations, workspace settings, and contract. Treat each statement as specific to the named service and confirm its current wording before relying on it.
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What should you compare between AI tools?
When choosing between services, compare the product versions and account tiers you would actually use. A provider’s consumer policy cannot stand in for its business or API terms. There is no common independent scorecard in the cited sources that ranks these providers, so use the same questions for each candidate:
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- Model improvement: Check whether inputs or outputs may be used to improve models, whether the setting is opt-in or opt-out, and whether an administrator controls it.
- Retention and deletion: Find the applicable retention period, deletion process, and any safety-review or legal exceptions. Do not assume that deleting a visible chat removes every copy, backup, or safety record unless the applicable terms say so.
- Access: Check who can access content, including provider personnel, workspace administrators, and integration partners, and under what conditions.
- Account and contract: Confirm whether the offer is consumer, business, education, healthcare, or API access; review workspace controls and the agreement that applies. A business or enterprise label alone does not establish compliance with a particular law or policy.
- Security and incident handling: Review available information about access controls, security practices, and how incidents are handled.
- Reliability for your task: Test representative prompts and files. Consider whether the tool makes errors in the cases that matter to you and what harm an incorrect answer could cause.
- Transparency and oversight: Look for evidence about testing, limitations, monitoring, and the ability to govern use. Decide who will review outputs and how often the tool and its policies will be reassessed.
How to make the decision
- Define the task and the cost of failure. Brainstorming usually has different consequences from using an answer in a medical, employment, financial, legal, or confidential business context. These are examples of higher-consequence uses, not endorsements or approvals of any provider for them.
- Classify the information before entering it. Decide whether prompts, files, and connected services could expose personal or confidential data. Remove identifiers and unnecessary details; do not submit information unless the provider and account arrangement are approved for that data.
- Read the current terms for the exact service. Check the product, region, plan, workspace, and contract. Look for model-improvement use, retention, deletion, human or third-party access, and exceptions—including for integrations.
- Verify the setting in the account. Confirm that the control is available and enabled as intended; an advertised setting may be unavailable or managed by an administrator. Record the policy version or date and the setting state used for your decision.
- Test and govern the use. Try representative tasks, verify outputs before acting, and require human review where errors could have serious consequences. Reassess if the task, account configuration, or provider policy changes.
When is a tool the wrong choice?
Do not use a service for sensitive information if you cannot establish what it receives, how the applicable account handles it, or whether that use is permitted by your organization or agreement. Likewise, do not rely on an AI answer as the sole basis for a consequential decision when you cannot validate its accuracy or provide appropriate human oversight. A less capable tool with suitable controls may be a better fit than a more capable one whose data handling or failure risks you cannot accept.
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