Don’t treat a chatbot as simply “safe” or “unsafe.” Decide whether its evidence, privacy terms, and safeguards fit the specific task and the consequences of an error. Look for documented testing and limits—not just confident assurances, polished demonstrations, or a few successful prompts.
Start by defining what “safe” means for your use
A chatbot may be suitable for brainstorming but not for making a medical decision, handling confidential work, or taking an action with legal or financial consequences. Safety depends on the task, the people using the system, and what could happen if it fails.
Make the provider’s claim concrete. Ask: safe against what risk, for which users, in which product version, and under what conditions? Also distinguish the underlying model from the deployed service. A chatbot can combine a model with a user interface, moderation, retrieval from external sources, tools, and third-party components; a claim about one part does not automatically cover the whole service.
NIST’s AI Risk Management Framework is voluntary guidance, not a certification or proof that a chatbot is safe. NIST says the framework is being revised. Its approach emphasizes considering risk in the context of the system and its components.
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Look for evidence behind the claim
Prefer published evaluation methods and results over marketing language or a product demonstration. NIST’s 2024 Generative AI Profile advises: “Evaluate claims of model capabilities using empirically validated methods.” This is risk-management guidance, not a consumer product rating or guarantee.
When reviewing a test, look for enough detail to judge what it establishes:
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- Scope and version: Which model or deployed service was tested, and when?
- Test conditions: What prompts, tasks, languages, users, and operating conditions were included?
- Measures and comparison: What outcomes or metrics were measured, and what baseline was used?
- Uncertainty and limitations: How variable were the results, and what cases were excluded or performed poorly?
- Review and repeatability: Can others examine the method or reproduce the evaluation? Was it reviewed by independent assessors or relevant domain experts?
A result is useful only within its tested scope. A benchmark, red-team exercise, or handful of prompts cannot establish how a different version will perform with different users or in a different domain. NIST warns against extrapolating from narrow, non-systematic, anecdotal assessments. Its AI RMF Core calls for evaluation before deployment and during operation, documentation of performance limits, and tracking of errors and emerging risks. NIST’s ARIA program combines model testing, red-teaming, and field testing to assess technical and contextual robustness, alongside accuracy and performance.
Check how the system handles failure
A chatbot can sound certain and still be wrong. Look for clear information about known limitations and what happens when the system is uncertain, outside its intended scope, or produces a harmful answer.
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- Does it make uncertainty and limitations understandable to users?
- Can a user reach a qualified person or another appropriate source when the stakes require it?
- Does the service have a way to monitor problems, report harmful errors, and respond to them?
- Is the system evaluated after changes, rather than relying only on testing before release?
- Are safety and privacy risks considered alongside reliability, security, transparency, and fairness?
NIST’s AI RMF Core treats these as contextual risk-management questions. For consequential uses, general assurances or a strong benchmark score are not substitutes for domain-specific safeguards and qualified human judgment.
Read the privacy and data-use terms before sharing information
Check the chatbot’s current privacy policy, terms, and in-product settings. “Private,” “secure,” or “safe” does not tell you what happens to your conversation data.
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- What information does the service collect, and how long is it retained?
- Can employees, contractors, or other people review conversations?
- Is data shared with third parties or used to train or improve models?
- Can you opt out, delete data, or control these uses—and do the controls apply to the information you plan to enter?
- Can the provider change these terms, and how will it communicate material changes?
In January 2024, the FTC reminded AI companies to honor their consumer data commitments, including promises about using data for training. In February 2024, it warned that quietly changing terms can raise unfairness or deception concerns. See the FTC’s guidance on privacy and confidentiality commitments and changes to terms of service.
Unless the current terms and settings clearly support the use—and you are authorized to share the information—avoid entering confidential work material, identifying details, passwords, health data, or other sensitive content.
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Be cautious with companion-style chatbots
A human-like tone does not show that a system understands, cares, or can reliably protect someone. NIST’s Generative AI Profile identifies anthropomorphization in interfaces as a human-AI configuration issue to track.
In September 2025, the FTC announced an information inquiry into AI chatbots acting as companions. It asked companies about testing and monitoring for negative effects, disclosures, age-related controls, and data use. This was an inquiry—not a finding that every chatbot causes harm. See the FTC announcement.
Compare chatbots using the same task and criteria
If you are choosing between services, use the same realistic task and assess each against the same questions. Do not rank them from one benchmark or a few prompts: results can vary by version, prompt, domain, and the system around the model.
| What to compare | Questions to ask |
|---|---|
| Claim and scope | What risk or capability is claimed? Does the claim cover this service version and your intended task? |
| Evidence quality | Are methods, test cases, metrics, uncertainty, and limitations disclosed? Was the evaluation independently reviewed? |
| Context fit | Were realistic users, languages, and conditions represented? Do the tested failure modes matter for your task? |
| Safety response | Does the service monitor problems, communicate limits, fail safely, or provide human oversight where needed? |
| Privacy and control | What data is collected, retained, shared, reviewed by people, or used for training? Can you control or delete it? |
| Change and accountability | Does the provider explain system or terms changes and offer a way to report harmful errors? |
Use a higher bar when an error could cause serious harm
For health, legal, financial, safety-critical, or similarly consequential decisions, look for evidence and safeguards specific to that use. A provider’s own assurance, a general-purpose benchmark, or an informal trial is not enough to establish that a chatbot is suitable. Use qualified human judgment and domain-specific controls where the decision warrants them.
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