AI can produce convincing but false answers, expose or infer sensitive information, repeat bias, and help enable impersonation or misinformation. Users can reduce some risks by checking consequential claims, limiting what they share, and verifying urgent requests through a separate trusted channel. Other safeguards—such as system design, monitoring, and authentication—depend on AI providers and organizations, not individual users.
What counts as an AI safety risk?
AI safety risks are ways an AI system or its outputs can cause harm, whether through mistakes, misuse, or how people rely on them. NIST’s voluntary AI Risk Management Framework is intended to help organizations incorporate trustworthiness into AI design, development, use, and evaluation; it is not a consumer checklist. NIST says the AI RMF 1.0 is being revised. Its Generative Artificial Intelligence Profile (NIST AI 600-1), released July 26, 2024, describes risks including confabulation, privacy, harmful bias, information integrity, and environmental impacts. NIST’s July 2024 announcement, updated February 6, 2025, describes the profile as covering 12 risks and just over 200 developer actions—not 12 risks that every individual user will necessarily experience.
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What can go wrong when people use AI?
Plausible answers can be wrong
Generative AI can produce fluent, confident-sounding content that is false. NIST calls this kind of risk “confabulation.” Restrictions on a system’s output do not eliminate the possibility of harmful or misleading answers. Treat a polished explanation as a claim to check, not as proof.
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Private information can be exposed or inferred
Information entered into an AI service may create privacy risks, including leakage, memorization, or sensitive inferences. The terms and controls vary by provider and product; the available guidance here does not compare current services’ retention or training settings.
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Bias and harmful content can affect people
AI outputs can reflect or amplify harmful bias, stereotypes, or dangerous content. A generated summary or recommendation about a person or group is not automatically neutral, and an assessment should not be treated as authoritative when it could affect someone’s opportunities, treatment, or safety.
AI can support impersonation and misuse
AI may lower barriers to some cyber misuse and can be used to create misinformation or impersonate people, including through cloned voices. These are system-level risks described by NIST and the FTC; the sources do not establish that every user faces the same likelihood of encountering them.
How can you reduce risk in everyday use?
Verify factual answers before relying on them
- Ask the AI for sources, then open those sources and check whether they actually support the answer.
- For health, legal, financial, safety, or identity claims, look for reliable primary sources and consult an appropriate qualified person when the consequences matter.
- Do not let a confident tone substitute for evidence. Verification lowers avoidable risk; it does not guarantee that every answer is correct.
Share only the information the task needs
- Leave out unnecessary personal details and use the minimum information needed to get help.
- Avoid entering passwords, payment details, confidential work, or sensitive personal information unless you have checked and accept that service’s current privacy terms and controls.
- Do not assume that settings or protections are the same across AI products.
Use independent review for consequential judgments
Do not rely on generated assessments of a person or group as the sole basis for a consequential decision. Check evidence relevant to the decision and seek human review, especially where a mistaken or biased output could cause material harm.
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How should you handle a suspicious voice or urgent request?
If a familiar-sounding voice asks for money, credentials, or sensitive information urgently, pause. Call the person or organization back using a number already saved or obtained independently, rather than using contact details supplied in the message. A familiar voice alone is not authentication.
The FTC describes three intervention points for AI-enabled voice cloning: prevention or authentication before a clone is used, real-time detection or monitoring, and evaluation of content after use. Those measures involve providers, platforms, organizations, and other actors as well as users. The FTC warns that approaches have limitations and says “there is no silver bullet to prevent the harms posed by voice cloning.” Detection can fail or be evaded, so independently verifying the request is more dependable than trying to judge a voice by ear.
Can you tell whether text, an image, or a voice is AI-generated?
Do not treat a detector result, watermark, or an AI system’s own answer as conclusive proof of who created content. The FTC notes limitations such as watermark removal or alteration and false positives; detection effectiveness can vary. A missing watermark does not prove content is human-made, and a detector flag does not by itself establish AI authorship.
For ChatGPT specifically, OpenAI’s Help Center article, updated September 2026, says ChatGPT has no “knowledge” of what content it generated and that its responses to questions about whether it wrote an essay or whether writing could be AI-generated are “random and have no basis in fact.” Do not ask a generator to certify its own authorship, and do not generalize this product-specific statement to every authorship tool.
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| Risk or intervention | What a user can do | What depends on providers or organizations | Important limitation |
|---|---|---|---|
| Accuracy | Check consequential claims against reliable primary sources. | System design, evaluation, and output safeguards. | Fluent output and restrictions do not guarantee correctness or prevent all harmful responses. |
| Privacy | Minimize sensitive information and check current service terms and controls. | Data handling, privacy controls, and other system practices. | Settings and retention practices vary; the sources cited here do not compare current services. |
| Bias or harmful recommendations | Seek relevant evidence and human review for consequential decisions. | Risk management and evaluation across the AI lifecycle. | A generated assessment should not be presumed neutral or authoritative. |
| Voice impersonation | Verify a suspicious request through a separate, previously trusted channel. | Prevention or authentication, real-time monitoring, and post-use evaluation. | Detection and watermark approaches have limitations; no single measure resolves the risk. |
The NIST framework and FTC discussion describe categories of risk and intervention, not a measured probability that a typical individual will encounter each harm. They also do not establish that one consumer AI service is safest.
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