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Choose an AI chatbot by matching its data handling, security controls, access permissions, reliability safeguards and human-review options to the sensitivity of your information and the consequences of an error. Do not enter confidential work data into an unapproved public service. For personal use, inspect the privacy notice and settings for the exact account you have, minimize what you disclose and verify important answers. No single chatbot is safest for every person or task.
Start with the information and the consequence
Safety is not a badge attached to a chatbot. It is a decision about a particular prompt, account, provider and task. The same service might be acceptable for rewriting a public announcement but unsuitable for a personnel case, medical detail or unpublished product plan.
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Classify the information before you type
Ask what would happen if the text, attachment or conversation were exposed, retained, reused or shown to another person. A practical classification is:
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- Internal: ordinary workplace information that is not public but has limited impact if disclosed.
- Confidential or regulated: customer records, employee information, credentials, financial details, health information, legal advice, security findings, source code or trade secrets.
- Highly sensitive: information where disclosure could create serious personal, legal, safety or competitive harm.
Use an organization-approved service for internal or confidential work information and follow the employer’s data-classification rules. The UK Government AI Playbook advises that official information should not be entered directly into public AI applications or APIs unless it is already public or cleared for publication; a commercial licence with different data-handling terms may create an exception. For a private employer, that is a control principle rather than an automatic legal rule, so ask the security, privacy or IT team which tools and data categories are approved.
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Match the task to the cost of being wrong
Separate low-impact assistance from consequential decisions. Brainstorming headings, transforming text you wrote yourself or explaining a public document generally allows more room for error. A recommendation affecting money, health, employment, legal rights, safety, access to services or a person’s reputation requires authoritative checking and an accountable human decision-maker. If foreseeable harm cannot be reduced enough for the task, do not use a chatbot for it; choose a safer process.
Check the service’s actual data practices
Read the privacy notice, product terms and settings for the precise account and plan you intend to use. A personal account can have different conditions from a paid business subscription or a managed workspace, and terms can vary by region and change over time.
Questions the privacy documentation must answer
- What is collected? Check prompts, uploaded files, account details, device information, diagnostics, payment data and conversation metadata.
- Why is it collected? Distinguish providing the service from analytics, personalization, advertising, fraud prevention, safety monitoring or model development.
- How long is it kept? Look for retention periods, backup copies, deletion queues and exceptions for legal or security reasons.
- Is it used to improve models? Identify whether conversations are used for training or evaluation, whether that use is opt-in or opt-out, and whether the setting applies to old conversations as well as new ones.
- Who can receive it? Check processors, affiliates, contractors, human reviewers, connected applications and international transfers.
- What control do you have? Confirm export, deletion, history controls, training controls, access requests and a route to report a problem.
Do not treat a marketing label such as private, enterprise or secure as a substitute for these answers. Save the date and version of the terms you relied on when the information is important, because a later update can change the risk assessment.
Use the least disclosure that still solves the problem
Remove names, account numbers, exact addresses, credentials and unnecessary background. Replace them with neutral labels such as Employee A or Customer 12 and include only the fields needed for the task. Pseudonymisation reduces exposure but does not automatically remove privacy obligations: the UK Information Commissioner’s Office (ICO) notes that pseudonymised data can still be personal data.
Never paste passwords, API keys, private encryption keys, authentication codes or full identity documents into a chatbot. If a prompt needs a document, consider extracting the relevant passage yourself instead of uploading the complete file.
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For workplace use, verify governance and access
A workplace chatbot can inherit the permissions of the systems it connects to. The central question is not whether the model can answer, but whether it can access only information the requesting user is entitled to see.
Confirm organizational approval
- Find the employer’s AI, information-security and acceptable-use policies.
- Check the data classification of the material you want to use.
- Ask the security, privacy or IT owner whether the exact product, plan, region and integration are approved.
- Confirm contractual terms for confidentiality, retention, deletion, incident notification, subprocessors and use for model improvement.
- Record an owner who can answer questions and suspend the service if its risk changes.
Approval should cover the service and the use case. A tool approved for public marketing copy is not automatically approved for employee records or customer support transcripts.
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- Require strong authentication and, where available, organization-managed sign-in and multifactor authentication.
- Review administrator controls for user provisioning, logging, retention and disabling accounts.
- List every connected drive, mailbox, database, plug-in or external service.
- Use least privilege: grant only the folders, records and actions required for the task.
- Test with a non-sensitive account to confirm that the chatbot cannot retrieve another team’s files or an administrator-only record.
- Separate read access from write or send access. A summarizer rarely needs permission to delete records or send messages.
The UK Government AI Playbook warns: “When experimenting with AI tools, pay attention to security and never assume default configurations are secure.” Change permissive defaults, review inherited permissions and recheck them after upgrades or connector changes.
Assess the provider and supply chain
For procurement, evaluate the provider’s security posture, hosting and deployment environment, model and service supply chain, incident history supplied under contract, data movement and responsibilities between your organization and the vendor. The UK National Cyber Security Centre (NCSC) recommends assessing whether the service fits your threat model rather than accepting a generic security claim. Ask who operates each component, where data is processed, how subcontractors are controlled and how access is logged.
For personal use, inspect the exact account and settings
Before using a consumer chatbot, open its current privacy notice and settings while signed in to the account you will actually use. Check conversation history, retention, deletion, sharing, model-improvement controls and connected applications. Do not assume that a setting on one account or device applies to another.
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Keep sensitive personal details out
Avoid entering information whose exposure would create meaningful harm: identity numbers, financial account details, private correspondence, precise location history, intimate images, children’s identifiable information, medical records or legal strategy. If you need general guidance, describe the situation without identifying details and verify the response with a qualified professional.
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Consider age and interaction safeguards
For children or companion-style use, inspect age requirements, parental controls, content filters, reporting channels and how the service handles emotionally dependent or manipulative interactions. A US Federal Trade Commission inquiry discusses these topics as areas for examination; it is an inquiry, not a finding that any particular service is unsafe or unlawful.
Evaluate accuracy, bias and human oversight
A confident tone is not evidence of correctness. Chatbots can produce fabricated citations, omit relevant facts, reflect bias or misunderstand an ambiguous request.
Build verification into the task
- Ask the chatbot to show assumptions, uncertainty and the source passages it relied on, but do not treat a generated citation as proof.
- Check factual claims against authoritative primary sources, original records or a qualified professional.
- Use a second person to review outputs that affect another person or a material entitlement.
- Keep the original prompt, output, sources checked and final decision when an audit trail is needed.
For money, health, employment, legal rights or safety, corroboration is mandatory rather than optional. The UK Government Data and AI Ethics Framework states: “Data-driven systems such as AI should be robust, secure and safe at every stage of their life cycle.” Its current page was updated 18 December 2025.
Make the reviewer able to reject the model
Meaningful oversight requires a reviewer with sufficient context, competence and authority to challenge or override the output. A person who can only click approve, who cannot see the relevant evidence or who is measured solely on speed is not providing effective review.
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Block irreversible automation
Do not let an unchecked answer automatically send an external email, publish content, modify a customer or financial record, approve a transaction, disable an account or delete data. Use a human confirmation step that displays the proposed action, its inputs and the target system before execution.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Compare chatbots with a consistent checklist
When two or more services appear suitable, compare them against the same questions. Record evidence for the exact plan, region and date rather than ranking products by reputation.
| Area | What to verify | Safer decision signal |
|---|---|---|
| Data handling | Collection, purpose, retention, deletion, model-improvement use, sharing and privacy settings | Plain-language terms and controls that match your classification and required retention |
| Identity and access | Authentication, administrator controls, user roles and permissions for connected files and services | Organization-managed identity, least privilege and separate approval for write actions |
| Provider security | Hosting, data movement, subprocessors, incident processes, logging and supply-chain responsibilities | Evidence detailed enough to assess your threat model and contractual obligations |
| Task fit | Known limitations, ability to check outputs and suitability for the proposed use | Low-impact use or a documented verification process for consequential work |
| Human oversight | Who reviews, what they can see, and whether they can reject or override results | Competent, accountable reviewer with authority before a consequential action |
| Transparency and accountability | Identifiable owner, change notices, support and routes to raise concerns | Clear responsibility across your organization and the supplier relationship |
| Risk to people | Privacy loss, bias, harmful interactions and age-related protections | Controls proportionate to the people affected and the likely harm |
A practical workplace rollout
Use a staged rollout instead of giving every user unrestricted access.
- Define the use case. Write down the task, users, data classes, connected systems, expected benefit and worst credible failure.
- Choose a low-risk pilot. Start with public or synthetic data and outputs that a person can check quickly.
- Configure controls. Set identity, retention, logging, connector permissions and model-improvement options before inviting users.
- Create operating rules. Specify prohibited data, required review, approved sources, escalation paths and how to report an incident.
- Test failure modes. Try ambiguous prompts, malicious instructions in documents, permission changes, inaccurate answers and connector outages.
- Train users. Demonstrate classification, redaction, verification and the difference between drafting and decision-making.
- Monitor and review. Reassess after provider or policy changes, security incidents, new connectors or a change in the task’s impact.
A safer personal-use routine
- Open the current privacy notice and settings for your actual account.
- Turn off conversation use for model improvement when that control is available and consistent with your needs.
- Set the shortest practical retention and delete old conversations you no longer need.
- Disable integrations that are not essential and review permissions for those you keep.
- Remove identifying details and secrets before submitting a prompt.
- Label the output as a draft until you have checked important facts.
- Use a qualified person or authoritative source for high-impact decisions.
Common warning signs and what to do
The terms are vague or contradictory
Do not upload sensitive information while the provider cannot explain collection, retention, sharing and deletion. Ask for written clarification or select a service with clearer controls.
The chatbot can see more than the user needs
Disconnect broad folders and connectors, create a restricted account or use a separate workflow that supplies only the necessary text. Recheck access after a permission or software change.
An answer contains a citation that does not support it
Open the original source, check the relevant passage and replace the claim with verified wording. If the claim cannot be verified, omit it.
The system proposes an action affecting a person or record
Pause automation, require review by an authorized person and preserve the evidence used for the decision. If the reviewer cannot understand or challenge the recommendation, do not use the system for that decision.
A provider changes its terms or suffers an incident
Stop new sensitive processing, notify the organizational owner, review what data was exposed or retained and reassess whether the service remains approved. Personal users should delete unnecessary history and revisit account controls.
Understand the legal and jurisdiction limits
Safety guidance is not a universal legal determination. Ofcom explains that UK Online Safety Act requirements depend on whether a chatbot meets the Act’s definitions of a covered service, and that some standalone chatbot services may fall outside those definitions. That UK explanation does not determine obligations in another country or under another law. Employers should obtain jurisdiction-specific legal and privacy advice for regulated processing.
The ICO also cautions that local processing is not automatically outside data-protection responsibilities. Processing on a device may still involve the organization that created or distributed the model, and third-party access to personal data on that device is processing. Security expectations and regulatory guidance evolve; the ICO notes that parts of its guidance are under review following the Data (Use and Access) Act.
Final decision rule
Use a chatbot only when you can explain what data it receives, why it receives it, how long it is retained, who can access it, what permissions it has, how its answers will be checked and who is accountable for the result. If any of those answers is unclear, reduce the data, reduce the permissions, add human review or choose a different process. The safest choice is the one whose residual risk is acceptable for this specific task—not the one with the most confident marketing.
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