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Before you use an AI tool for work, answer three questions: is this exact service and account approved, is the information permitted in it, and who will check and own the result? If any answer is unclear, stop and ask your manager, IT, security, privacy or compliance contact. Use AI as a supervised drafting and analysis aid—not as an unreviewed decision-maker.
Start with the approved tool, account and task
A public chatbot account is not automatically an approved workplace service. Approval can depend on the exact product, subscription, account configuration, connected features, data permissions and contract. Microsoft’s employee guidance puts the basic rule plainly: “Use only company-authorized AI services.”
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Before opening a prompt box, locate your employer’s acceptable-use or AI policy. Confirm all of the following:
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- You are signed in through the company-approved account, not a personal account.
- Your task is an allowed use case, rather than a prohibited or high-risk one.
- The information you plan to provide is allowed for that service and account.
- You know where to report a suspected leak, harmful output, security issue or incorrect decision.
Approval is task-specific. A tool permitted for rewriting public copy might not be permitted for employee records, customer complaints, source code, merger information or health data. If the policy does not answer your question, do not infer permission from the fact that the tool is free, popular or already installed.
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Use a quick pre-prompt decision
- Identify the task. Write down what the system will do: summarize, draft, classify, extract, translate, calculate or recommend.
- Identify the data. Separate public information from internal, confidential, personal, regulated or proprietary information.
- Check the exact service. Verify the approved account, configuration and connected applications.
- Check the owner. Establish who will review the result and who has authority to approve or act on it.
- Escalate uncertainty. Ask the designated manager, IT, security, privacy or compliance contact before entering the data.
Can I paste this into an AI tool?
Ask whether the prompt contains information about a colleague, customer, vendor, applicant or member of the public. Microsoft advises employees not to disclose sensitive or confidential company details, including personal information about employees or vendors, to AI services. That warning applies even when the request seems routine, such as “summarize these interview notes” or “rewrite this customer email.”
Keep these categories out unless explicitly approved
- Passwords, authentication codes, private keys, API keys and security vulnerabilities.
- Personal data about employees, customers, applicants, patients, vendors or other identifiable people.
- Confidential contracts, pricing, legal advice, acquisition plans, unreleased results and trade secrets.
- Source code, architecture diagrams, incident details and internal security reports.
- Information subject to a confidentiality agreement, professional secrecy obligation or data-sharing restriction.
- Worker health information or other sensitive records requiring a defined legal and organizational process.
Do not assume that removing a name makes data anonymous. A combination of job title, date, location, project and unusual circumstances may identify someone. Redaction can also remove context needed for a safe answer. Use approved minimization or anonymization procedures, and confirm that they are acceptable for the particular tool.
When a task is safe to proceed
A lower-risk prompt normally uses information that is already public or deliberately prepared for the approved system, contains no unnecessary personal or confidential details, and has a named human reviewer. Even then, check the result before publishing or acting on it. “Low risk” does not mean “no risk.”
What happens to information after I submit it?
Data handling varies by product, plan, settings, connected services and contract. It is not accurate to claim that every prompt is stored forever or used to train a model. Instead, check the employer-approved configuration and the provider’s applicable terms, then ask your organization how those settings are governed.
NIST’s Generative AI Profile notes that third-party integrations can increase intellectual-property, data-privacy and information-security risks. It calls for clear guidance about the collection and use of data supplied to providers. The UK Information Commissioner’s Office (ICO) likewise recommends a documented privacy framework, named responsibilities, procedures and staff guidance when AI processes personal information.
Questions to take to your organization
- What data categories may be entered, and which are prohibited?
- Are prompts and uploaded files retained, exported or accessible to administrators?
- Which connected applications, plugins or retrieval sources are enabled?
- What logging, access controls and deletion process apply?
- Does the approved account have contractual restrictions that differ from a personal account?
- Who handles a data-subject request, suspected disclosure or provider incident?
If nobody can answer these questions, treat the service as unapproved for sensitive work. A convenient interface is not evidence of an adequate privacy control.
How do I check whether an AI answer is right?
Generated material is a draft until a person verifies it. Microsoft’s employee guidance states: “AI can make mistakes.” NIST’s AI Risk Management Framework treats validity and reliability as trustworthiness characteristics, alongside accountability and transparency, privacy enhancement, and fairness with harmful bias managed. The framework is voluntary risk-management guidance, not a guarantee that a particular system or answer is correct.
A repeatable verification pass
- Define the claim. Break the output into individual factual statements, recommendations, calculations or code changes.
- Check primary records. Compare names, dates, figures, contract terms, policies and status information with the authoritative internal record or source document.
- Validate external facts. Use a trusted, current source appropriate to the subject. A citation-shaped string is not proof; open the cited source and confirm that it actually supports the claim.
- Recalculate. Reperform arithmetic, formulas, unit conversions and financial assumptions independently. Do not rely on an answer merely because it displays a confident explanation.
- Test code and procedures. Run code in an approved, isolated environment, review dependencies and permissions, and test expected as well as failure cases. Never execute unreviewed code against production systems.
- Check dates and scope. Look for outdated policy versions, wrong jurisdictions, missing eligibility conditions or conclusions that are broader than the evidence.
- Record the review. For consequential work, retain the prompt or input description, sources checked, corrections made, reviewer and final approver according to your organization’s retention rules.
Warning signs of a plausible but unsafe answer
- Specific statistics, quotations or legal references that you cannot locate in the named source.
- Confident language without assumptions, uncertainty or a date.
- Several paragraphs that repeat the question but never identify evidence.
- A recommendation that ignores a policy exception, safety constraint or affected group.
- Code that uses insecure defaults, broad permissions or libraries you cannot verify.
- A summary that omits a qualification, dissenting view or negative result present in the original material.
Could the output be biased or incomplete?
Accuracy checking is more than correcting spelling and obvious factual errors. Microsoft tells employees to be aware of potential bias. NIST identifies fairness and the management of harmful bias as risk concerns. Ask whose interests, language, history or circumstances may be missing.
Questions that expose omissions
- Which people or cases are not represented in the source material?
- Would the recommendation change for a different location, disability, language, age, job type or contract status?
- Did the system treat a proxy—such as postcode, school, employment gap or accent—as if it were a legitimate criterion?
- What uncertainty or exception did the output leave out?
- Would an affected person consider the explanation understandable and contestable?
Compare the result with a sample of known cases, not just the easiest examples. Look for different error rates or quality across relevant groups. If a result could disadvantage someone, pause the workflow and escalate it rather than “fixing” a single sentence while keeping the underlying process unchanged.
Who is responsible for the final decision?
A human reviewer is meaningful only when that person understands the task, has enough context and authority to challenge the system, and remains accountable for the work or decision. Clicking “approve” without reading is approval theater.
Design a real review, not a rubber stamp
- Give the reviewer context: the purpose, inputs, limitations and acceptable criteria.
- Give the reviewer time: rushed sign-off encourages automation bias—the tendency to accept a machine suggestion because it appears objective or efficient.
- Give the reviewer authority: they must be able to reject, edit, rerun or escalate the output without penalty for disagreement.
- Give the reviewer evidence: source links, records, confidence information where available and a way to inspect important inputs.
- Define the handoff: specify who makes the final decision and how corrections are recorded.
The ICO warns that automation bias and poor interpretability can make nominal human review ineffective. A person cannot meaningfully challenge an output they cannot understand, explain or trace to relevant evidence.
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Do not apply a single universal rule to every workplace use. Employment decisions, worker monitoring, profiling and worker-health information can trigger additional legal and organizational requirements. The ICO describes UK GDPR restrictions on solely automated decisions that have legal or similarly significant effects, together with specific conditions and safeguards. The exact rule depends on the jurisdiction, data, decision and system design.
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UK-specific point: if your organization operates in the UK, involve its privacy or data-protection lead before using AI for recruitment, promotion, dismissal, performance monitoring, access decisions or health-related processing. Do not assume that adding a nominal human click satisfies the requirement for meaningful intervention.
Stop and escalate when
- The output could determine someone’s employment, pay, access, eligibility, safety or care.
- The system uses personal data you cannot lawfully or fairly use for that purpose.
- An affected person cannot obtain an understandable explanation or challenge the result.
- The reviewer lacks authority to override the recommendation.
- The tool produces a harmful pattern, unexplained disparity or suspected privacy incident.
What training should employees receive?
Tool demonstrations alone are not enough. Effective training should connect to real work and answer four practical questions: which tool should I use, what information may I enter, how do I check the output, and when must I stop or escalate?
A Department for Work and Pensions and Skills England employer guide, updated 27 July 2026 and applying to England, recommends practical, inclusive and continuing learning integrated into normal workflows. Its findings are England-specific: more than 44% of organizations reported using AI tools daily; the evidence came from 23 workshops, 10 case studies and a survey with 536 responses. Employers reported AI skills challenges involving technical skills (67%), responsible AI (32%) and non-technical skills (10%). These figures are not worldwide prevalence estimates.
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- Map common tasks by role and classify their data and consequence level.
- Provide approved prompt patterns using sanitized, realistic examples.
- Practice verification against the records employees actually use.
- Run bias, privacy and incident scenarios, including when to refuse a request.
- Publish an escalation route and update it when tools, policies or laws change.
- Check competence through a supervised task, not attendance alone.
Microsoft Learn lists a free beginner module, “Prepare to use generative AI at work,” estimated at 31 minutes. It covers how generative AI works, responsible practices, sensitive-data protection and checking results. It is one general learning option and does not replace your employer’s rules or role-specific training.
A practical adoption checklist
- Before use: confirm the approved service, account, task, data category, reviewer and escalation contact.
- During prompting: minimize data, remove unnecessary identifiers, state the task and constraints, and avoid secrets or confidential material unless explicitly authorized.
- During review: verify facts, sources, calculations, dates, code behavior, omissions and disparate impacts.
- Before release: obtain the required human approval, correct errors, disclose AI assistance where policy requires it and preserve the review record when required.
- After an incident: stop further sharing, preserve relevant evidence, notify the designated security/privacy channel promptly and follow the organization’s containment instructions.
How to evaluate an approved workplace AI tool
If your organization is choosing among tools, compare capabilities that affect the whole workflow rather than marketing claims. The relevant questions are:
| Comparison area | What to verify |
|---|---|
| Account and data permissions | Which identities, files and applications can the service access, and can access be limited by role? |
| Retention and provider terms | What do the approved plan and contract say about collection, retention, deletion and provider access? |
| Controls and logging | Can administrators enforce settings, review use and investigate an incident? |
| Source traceability | Can a reviewer inspect supporting documents and distinguish sourced material from generated text? |
| Review and escalation | Can a person edit, reject, override and document the result before action? |
| Task fit | Does the service solve the intended task without requiring data or permissions that create disproportionate risk? |
Choose the least-privileged configuration that supports the task. A more capable system is not automatically a safer or better workplace system.
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