Generative AI can be useful, but it is not automatically safe for every task. The main questions are what information you share, whether the service is approved for that use, and what could happen if its answer is wrong or disclosed. Keep sensitive information out unless the use is authorized and you understand the service’s relevant terms and settings; verify consequential answers independently.
What “safe to use” depends on
There is no single safety rating that applies to every AI service and task. Risk changes with the sensitivity of the information you provide, the service and account settings, your organization’s rules, and the consequences of relying on the output. UNESCO recommends proportionate use and risk assessment, while NIST’s AI Risk Management Framework treats trustworthiness as something to consider throughout the design, development, use, and evaluation of AI systems. UNESCO’s Recommendation on the Ethics of Artificial Intelligence was adopted by UNESCO Member States in November 2021; it states that “Privacy must be protected and promoted throughout the AI lifecycle.” The NIST framework page notes that the framework is being revised, so its revision status should be checked for current details.
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These principles do not establish that every service has the same weaknesses or that a particular user will be harmed. They are a basis for deciding how much caution a specific use requires.
What to check before sharing information
Typing a prompt or uploading a file means disclosing that information to a service. The terms for retention, access, or use to improve models can vary by provider, account tier, and settings; there is no provider-specific answer without checking the particular service.
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- Minimize what you share. Include only the information needed for the task. Avoid names, account numbers, confidential documents, or other sensitive details unless you have a clear reason and authorization.
- Check the service’s terms and controls. Look for how prompts and uploads are handled, whether they may be used to improve models, available privacy controls, and who can access shared conversations or files.
- Consider what remains stored. General FTC data-security guidance for businesses recommends collecting only needed information, limiting access, and securing information that is retained. These are useful data-minimization principles, not an AI-specific rule imposed on every consumer. See the FTC guide to protecting personal information.
Using generative AI at work
Check your employer’s policy and get authorization before entering internal, customer, personal, or regulated information. Use only tools and configurations approved for the task. CISA specifically recommends following corporate policies for handling and storing work-related information in its Safeguarding Your Data guidance. What is permitted depends on your employer and the information involved; a tool being available to you does not by itself make it approved for company data.
Using generative AI for personal tasks
Apply the same data-minimization habit to personal use: do not submit personal, financial, or account details unless they are genuinely needed and you understand the service’s relevant terms and settings. For a low-sensitivity task, such as brainstorming or rephrasing text without private details, the disclosure risk may be lower. That does not establish how any particular provider handles a prompt; check the provider’s current information for the account you use.
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How to judge the reliability of an answer
Fluent wording is not proof that an answer is correct. NIST identifies information integrity as a generative AI risk, alongside data privacy and information security, in its Generative Artificial Intelligence Profile, published July 26, 2024. This identifies a class of risk; it does not establish that all outputs are wrong or give an error rate for a particular model.
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- Check important factual claims against dependable, independent sources.
- Use qualified human review when an error could have significant consequences.
- Treat an answer as a starting point, not as the sole basis for a consequential decision.
A practical risk check for any task
- Identify the input. Is it public, ordinary personal information, confidential work material, or sensitive data?
- Confirm authorization. For work information, check your organization’s policy and use an approved service and configuration.
- Assess the downside. Consider the harm from either exposing the information or acting on an incorrect answer.
- Reduce exposure and verify. Remove unnecessary details, check the service’s controls, and independently review any output that matters.
Increase scrutiny as the sensitivity of the data or the impact of a mistake rises. UNESCO’s recommendation and NIST’s risk-management materials support this proportionate approach; they do not provide a universal list of tasks that are always safe or unsafe. UNESCO Recommendation · NIST AI Risk Management Framework
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How to evaluate a specific AI service
Before using a particular service for a task, compare the details that determine its suitability:
- What information it collects and retains, and for how long.
- Whether prompts or uploads may be used to improve models.
- What privacy, access, sharing, and account-tier controls are available.
- Whether your organization has approved the service for the information involved.
- How you will verify outputs before acting on them.
These details are service- and account-specific and cannot be determined from the general question alone. Provider terms and features can change, and workplace rules and legal duties vary by organization, sector, and jurisdiction.
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