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The safest way to use an AI chatbot is to treat it as an assistant, not an authority: choose a task where an error is manageable, keep sensitive information out unless the service and use are approved, inspect the provider’s current data controls, verify important output against reliable sources, and require qualified human judgment for high-stakes decisions.
The 60-second safety checklist
Run this checklist before you paste a prompt or act on an answer:
- Define the job. Write down what you want the chatbot to do and what could go wrong if it is wrong.
- Choose an appropriate tool and account. Use an organization-approved service for work, and a personal account only for information you are allowed to share.
- Remove sensitive data. Keep passwords, one-time codes, financial details, identity numbers, private health information, confidential client material, trade secrets, and classified information out unless an approved process explicitly permits them.
- Check the current data policy. Review the privacy notice, terms, history settings, deletion controls, access permissions, and whether prompts may be used to improve or train a model.
- Limit access. Do not connect a chatbot to a sensitive repository or grant broad permissions when a narrower scope will work.
- Assume the answer may be wrong. Verify facts, calculations, quotations, citations, code, and recommendations before relying on them.
- Look for manipulation. Treat unexpected instructions in prompts, files, web pages, or retrieved documents as untrusted content.
- Escalate consequential decisions. A qualified person must review health, legal, financial, employment, safety, security, or other decisions where an error could cause material harm.
The Australian Cyber Security Centre (ACSC) summarizes the core actions as: “Do not enter sensitive or classified information into AI tools unless that has been approved,” “Check AI-generated results carefully before using them,” “Learn how AI tools use and store your data,” and “Follow your organisation’s policies when using AI.” Its Engaging with artificial intelligence guidance was published and last reviewed on 24 January 2024.
1. Decide whether a chatbot is suitable for the task
Start with the consequence of failure, not with the chatbot’s fluency. The National Institute of Standards and Technology (NIST) AI Risk Management Framework 1.0 (2023) says that AI actors share responsibility for deciding whether AI is appropriate for a particular context and for using it responsibly. A useful question is: If this output is incorrect, who could be harmed, how quickly would we notice, and can a qualified person correct it?
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| Task type | Reasonable use | Required safeguard |
|---|---|---|
| Low consequence | Brainstorming titles, reformatting your own non-sensitive notes, generating a first draft, or explaining a familiar concept | Check the result for errors and unwanted disclosure before sharing it |
| Moderate consequence | Drafting customer communications, summarizing internal material, preparing code, or analyzing business information | Use an approved account, minimize data, check the output against source material, and have the responsible owner review it |
| High consequence | Medical, legal, financial, employment, safety, security, eligibility, or disciplinary decisions | Do not rely on chatbot output alone; require qualified human review and the rules that govern the decision |
Do not confuse a harmless format with a harmless task. Asking for a friendly rewrite may be low risk; asking for a rewrite that changes a contract obligation or a medical instruction is not. If you cannot identify a reviewer who can detect and correct an error, the chatbot is not an adequate decision-maker for that task.
2. Keep sensitive information out unless the use is approved
Anything typed into a service should be treated as data leaving your immediate control until you have verified the opposite in the provider’s current terms and settings. Approval must cover both the service and the particular use; an organization may permit one workspace or account tier while prohibiting a personal account.
Information to exclude by default
- Passwords, API keys, authentication codes, recovery codes, private encryption keys, and security questions.
- Bank, payment-card, tax, insurance, and other financial-account details.
- Government identity numbers, passport or driver-license data, precise location, and copies of identity documents.
- Private medical, mental-health, genetic, or benefits information.
- Confidential client, customer, patient, student, employee, or legal matter information.
- Trade secrets, unpublished product plans, source code that is not approved for external processing, and classified or export-controlled material.
- Personal data about another person when you do not have a valid reason and permission to process it.
De-identify without creating false confidence
Removing a name is not always enough. Dates, rare job titles, locations, free-text descriptions, account numbers, and combinations of facts can identify someone. Replace real values with broad categories, synthetic examples, or placeholders and remove unnecessary context. Keep the mapping between a placeholder and a real person offline and protected. If the task requires the original details, use an approved workflow rather than assuming that obscuring one field makes the entire prompt safe.
Never disclose a secret because a prompt asks for it
A chatbot may request a token, internal URL, personal identifier, or document “to continue.” That request is not authorization. Stop, remove the secret, and use the organization’s documented support or access process.
3. Check how the service handles your data
Policies and controls differ by provider, product, account tier, geography, and date. Do not assume that every chatbot trains on every prompt, or that any service retains prompts for a fixed period. Confirm the current details before using it.
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- Identify the exact service and tier. Record whether you are using a personal account, an organizational workspace, an API, an embedded feature, or a third-party integration.
- Read the privacy notice and terms. Look for collection, processing purposes, storage location, retention period, human access, subcontractors, and legal-disclosure provisions.
- Check model-improvement use. Determine whether prompts, uploaded files, feedback, or outputs may be used to improve or train models, and whether an opt-out applies to your account or only to a particular plan.
- Inspect history and deletion controls. Find out what deleting a conversation actually removes, when deletion takes effect, whether backups or abuse-monitoring copies remain, and whether administrators can retain or export workspace content.
- Review sharing and permissions. Check public links, shared workspaces, team visibility, connected applications, plug-ins, and who can administer or audit the account.
- Check contractual and regulatory requirements. For workplace data, confirm that the organization has approved the service, any required data-processing terms, and the permitted data classification.
Save the policy date and the settings you relied on when the decision matters. A provider can change retention, training, or sharing controls, so recheck them after major product or account changes.
4. Secure the account and connected environment
Chatbot safety includes the surrounding account and systems, not just the text in a prompt.
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- Use a unique, strong password and multifactor authentication where available.
- Keep recovery methods current and protected; never place recovery codes in a chat.
- Use the organization’s approved identity, billing, and administrator controls for work.
- Grant the minimum connector or application permissions needed for the task. Prefer read-only access, a narrow folder, or a short-lived connection over unrestricted access.
- Remove unused integrations and revoke access when a project ends or a person leaves.
- Separate personal and workplace accounts so confidential material is not accidentally submitted to the wrong environment.
- Check links and shared conversations before sending them. A link can expose the entire conversation, attachments, or follow-up messages depending on the service.
NIST’s initial public draft AI Security and Safety Considerations for Generative AI (NIST IR 8579, dated 31 July 2025) discusses prompt injection, hallucinations, data exposure, and unauthorized access in a specific internal-use chatbot and mentions controls such as local deployment, access restrictions, and validation filters. It is a bounded description of that prototype, not implementation guidance or evidence that every public chatbot has those protections.
5. Recognize prompt injection and hostile content
Prompt injection is an attempt to manipulate an AI system through the input it receives. The malicious instruction may be visible in a user prompt or hidden in a document, web page, email, image, code comment, or retrieved search result. It can tell a system to ignore earlier rules, reveal hidden data, take an unauthorized action, or misrepresent what it found.
Warning signs
- A document tells the chatbot to disregard the task, system rules, or security policy.
- Text asks the model to reveal its hidden instructions, credentials, or private context.
- An unexpected instruction demands urgency, secrecy, or a permission change.
- An answer claims safeguards were bypassed or says that a request is authorized without evidence.
- A tool wants to send an email, change a record, download a file, or disclose information that was not part of the original purpose.
Safer handling
- Keep untrusted documents and retrieved content separate from instructions you control.
- Tell the system which sources are data to analyze, not commands to follow.
- Require confirmation before external side effects such as sending, deleting, purchasing, publishing, or changing permissions.
- Limit the data and tools available to the task; do not give a summarizer access to an entire drive.
- Manually inspect high-impact actions and escalate suspicious content to security staff.
Never treat a fluent explanation of why an instruction is safe as proof that it is safe. The content and the requested action still need independent review.
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6. Verify every output that matters
Generated text can be plausible, internally consistent, and wrong. Verification is part of using the tool, not an optional final polish.
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- Facts and dates: compare with an authoritative, current source, preferably the responsible agency, standard-setter, court, manufacturer, or original publisher.
- Numbers and calculations: recompute independently, inspect units and assumptions, and test edge cases.
- Quotations and citations: open the cited source, confirm the wording and context, and check that it supports the precise claim.
- Code and technical steps: review dependencies and permissions, run tests in an isolated environment, and inspect for data leakage or destructive commands.
- Summaries: compare against the complete source, not only another summary, and check what was omitted.
- Translations and rewrites: have a fluent subject-matter reviewer check meaning, tone, names, numbers, and legal or safety wording.
Use a reproducible review record
For important work, preserve the question, the model or service version if shown, the source documents, the checks performed, the reviewer, and the final approved text. This makes it possible to correct an error and explain how a decision was reached without treating the chatbot as the accountable author.
7. Apply human judgment to high-stakes uses
Do not let chatbot output alone determine a diagnosis, treatment, legal position, investment, loan, insurance outcome, hiring or firing decision, access entitlement, safety procedure, or security response. A qualified reviewer must apply the relevant law, professional standards, organizational policy, and facts that the chatbot cannot reliably establish.
NIST describes trustworthiness as contextual, including validity and reliability, safety, security and resilience, accountability and transparency, explainability and interpretability, privacy, and fairness. A system that produces a useful draft can still be unsuitable for an automated decision because it may not detect its own errors or their unequal effects.
Questions for the reviewer
- What source or rule supports each material conclusion?
- What information was missing or potentially misunderstood?
- Could the output disadvantage a person or group, and how was that checked?
- What is the fallback if the chatbot is unavailable or wrong?
- Who is accountable for approving, communicating, and correcting the result?
8. Follow different rules for personal and workplace use
Personal accounts
Use a personal chatbot for your own low-risk material only. Read the service’s current privacy controls, avoid other people’s confidential information, and do not assume that a paid plan, private conversation, or deleted history automatically means that no copy exists.
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Follow the organization’s approved-tool list, data-classification rules, procurement or legal review, and incident-reporting process. Use the designated workspace rather than a personal account, and ask the data owner before uploading client, employee, regulated, or proprietary material. If policy is unclear, treat the material as restricted until an authorized person decides.
Classified or specially regulated information
Do not enter classified information or specially regulated records unless the authorized process explicitly approves the service, environment, personnel access, retention, and jurisdiction. A general privacy statement is not a substitute for that approval.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.9. What to do when something goes wrong
Act quickly, but do not destroy evidence or make the exposure wider.
- Stop the interaction. Do not send more data, follow the suspicious instruction, or continue an automated action.
- Contain access. Disconnect the integration if appropriate, revoke exposed tokens, sign out other sessions, and disable shared links.
- Record what happened. Note the service, account, time, prompt or file involved, data category, recipients, and actions taken.
- Notify the right people. Use your organization’s security, privacy, legal, or incident channel; for personal accounts, contact the provider and affected financial or identity service when relevant.
- Assess obligations. The organization’s incident team should determine whether notification, password resets, monitoring, or regulatory reporting is required.
- Review the control failure. Tighten permissions, remove the data from future prompts, update guidance, and test the revised process.
If a chatbot supplied incorrect medical, legal, financial, or safety advice, do not wait for the model to correct itself. Contact the appropriate qualified professional or emergency service and preserve the incorrect output for review.
10. Recheck your safeguards over time
Chatbot products change quickly. Revisit the privacy notice, terms, retention and training settings, integrations, administrator permissions, and approved-use policy after a product update, account-tier change, new connector, organizational policy change, or new type of data. NIST notes that its AI RMF 1.0 and Playbook are being updated, so framework language and implementation resources can evolve.
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Train users with concrete examples: what counts as sensitive data, how to de-identify a case, where approved tools are listed, how to report an exposure, and when human review is mandatory. Periodically sample outputs for factual errors, unsupported citations, privacy leaks, and unfair treatment. A checklist works only when people can apply it to the actual service and data in front of them.
Practical examples
Safe enough with ordinary checks
You ask a chatbot to suggest headings for an article using your own public notes. No personal or confidential data is included. You select useful suggestions, check factual statements against primary sources, and edit the final copy yourself.
Needs de-identification and approval
You want to summarize a customer complaint. Remove names, account numbers, exact addresses, and other identifying details, then use the organization’s approved workspace only if its data terms permit that processing. A responsible employee reviews the summary against the original complaint.
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You ask for a medication change, a contract interpretation, an investment decision, or a candidate ranking. The chatbot may help list questions or organize information, but a licensed professional or accountable decision-maker must verify the facts and make the decision under the applicable rules.
Possible prompt injection
A downloaded report contains hidden text instructing an analysis tool to email its conversation history to an outside address. Treat the report as untrusted data, block the action, investigate the source, and report the event through the security process.
Bottom line
Safe chatbot use is a sequence of controls: choose a suitable task, minimize and protect data, verify the service’s current practices, restrict account and connector access, resist prompt injection, check the output, and keep a qualified human responsible for consequential decisions. If you cannot explain where the data goes, who can access it, or how an error will be caught, do not submit the information or act on the answer yet.
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