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Use workplace AI as a fast assistant, not as an authority. Let it draft, summarize, classify or analyze only after your employer permits the tool and the data; then verify important claims against authoritative records, restore missing context and make the final decision yourself. The higher the potential harm, the harder an error is to detect, or the less reversible the action, the more the task should remain human-led.
The safe default: AI proposes, a person disposes
Generative AI can produce fluent emails, reports, formulas and recommendations without reliably knowing whether each statement is true. Microsoft’s workplace safety guidance warns that AI can make mistakes, while its validation guidance notes that polished output may still be wrong, incomplete or risky. Treat a convincing answer as an unverified draft until you have checked it.
Responsibility does not move to the tool. The employee who uses, shares or acts on an output remains accountable for the result. That means a human must decide whether the task is appropriate for AI, whether the input is allowed, whether the answer is supported and whether the final action is safe.
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1. Check workplace permission and data boundaries first
Confirm that the tool is approved
Read the current AI, information-security and acceptable-use rules before you open a chat window. An organization may authorize one service while prohibiting consumer accounts, browser extensions, plug-ins or unapproved agents. Policy can also differ by department, customer contract, country and data classification.
Microsoft advises using company-authorized AI services and following organizational policy. That advice is product and workplace guidance, not permission to override your employer’s rules. If the policy is unclear, ask the information-security, privacy or legal contact before entering work material.
Classify everything you plan to paste
Do not assume that a prompt is private merely because the conversation is not public. Check whether it contains trade secrets, source code, credentials, unpublished financial information, customer records, health details, employee or vendor personal information, contract terms or data covered by a nondisclosure agreement. Remove identifying details when the task can be completed with a sanitized example, but do not treat redaction as automatically sufficient: combinations of dates, roles and events can still identify a person.
Use the organization’s approved data-classification labels and retention rules. Never paste passwords, access tokens, private keys or other authentication secrets. If the approved tool has a documented business-data boundary, use the account and workspace your employer specifies. For Copilot use, Microsoft’s support guidance recommends signing in with a work account to help maintain appropriate access and file protections; that does not replace an internal access review.
Separate permission to use AI from permission to use the source
An AI tool may be approved while a particular document is not yours to disclose. Check that you are authorized to view and process the source material, that sharing it with the service is allowed and that the intended audience may receive the eventual output. A customer contract can restrict automated processing even when your general AI policy permits drafting.
2. Decide how much human control the task needs
Do not apply one automation percentage to every job. Microsoft’s task-selection guidance uses four questions: Is the work repeatable? What is the impact if it is wrong? How easy would an error be to detect? Is there enough time to review before the action is taken? Add reversibility: an incorrect draft that can be edited is different from an irreversible payment, deletion or public decision.
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| Review question | Lower-risk signal | Higher-risk signal | What to do |
|---|---|---|---|
| Repeatability | A stable routine with a known format | A novel judgment problem or unusual case | Use AI for routine transformation; keep novel reasoning human-led. |
| Impact | A minor inconvenience or an easily corrected typo | Possible harm to a person, customer, business decision or legal position | Increase subject-matter review as consequences rise. |
| Error detectability | An authoritative record makes mistakes obvious | An error could look plausible or be hidden in a long analysis | Require independent checking, not a quick read. |
| Time and reversibility | There is time to verify and the action can be undone | A deadline leaves no review time or the action is difficult to reverse | Keep the decision human-led or postpone it until review is possible. |
AI-assisted with review
This mode fits low-impact, repeatable work where a reviewer can compare the result with a reliable source. Examples include turning your approved notes into a first-draft agenda, reformatting text or suggesting alternative wording for an internal message. Review every material statement and edit for audience, tone and confidentiality before sending.
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Use this mode when AI can reduce search or drafting time but a qualified person must own the reasoning. Ask it to organize evidence, identify questions, propose scenarios or expose assumptions; then perform the analysis yourself. Customer recommendations, financial interpretations, security findings and policy explanations commonly belong here because a plausible mistake may be subtle.
Fully human-led
Keep work human-led when an error could cause substantial harm, the correct answer depends on confidential context the system should not receive, detection is difficult or there is no opportunity to review. Microsoft’s Security Copilot documentation calls for particular care in consequential or sensitive areas and says users should review and verify responses before acting. Its product-specific warning illustrates a broader principle: urgency is not a reason to skip control.
3. Ask for work that can be traced
A vague prompt invites an answer that sounds complete while hiding its basis. For factual or analytical work, request the source material it used, assumptions, calculations, uncertainty and questions that remain unresolved. Give the model the authoritative documents when policy permits, rather than asking it to recall a rule from general training.
A useful prompt pattern is:
Use only the material I provide. Separate facts, calculations, assumptions and recommendations. For every factual claim, identify the supporting passage or say that the material does not establish it. Show each calculation, list uncertainties and flag information that needs independent verification. Do not invent missing names, dates, figures or citations.Ask for a claim-by-claim table when the output will support a decision. Include columns for the claim, source passage, calculation or reasoning, uncertainty and reviewer status. If the tool supplies a citation, treat it as a lead to inspect, not proof. Open the cited document, confirm that it exists, and check that it actually supports the wording and scope of the claim.
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Limit the source set
Tell the tool which documents and versions are in scope, the relevant date range and the intended audience. If several files contain conflicting figures, require the conflict to be reported rather than silently reconciled. A response based on unknown or mixed sources deserves stronger verification; NIST specifically recommends fact-checking techniques when generated information draws on multiple or unknown sources.
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Ask for uncertainty, not confidence theater
Request explicit “not established by the supplied material” labels, alternative interpretations and conditions under which the conclusion would change. Do not ask for a confidence percentage unless your organization has a validated way to interpret it. A numerical confidence score generated by the model is not evidence of accuracy.
4. Run four checks before you use an output
Source: does it match the evidence?
Compare each important statement with the original record. Look for invented citations, quotations that do not appear in the source, numbers copied from the wrong period and conclusions that go beyond what the document says. Mark unsupported, overstated and inconsistent claims instead of accepting a paragraph because most of it is correct.
Verified: are the important details independently confirmed?
Recheck names, dates, figures, units, version numbers, formulas, legal references and permissions using an authoritative record. Recalculate arithmetic in a spreadsheet or trusted calculator. For code, run it in an approved test environment and inspect what it does; do not execute unreviewed commands against production systems. For a customer-facing statement, have the person who owns the underlying data confirm it.
Context: what is missing?
Look for caveats, dependencies, exceptions, risks, eligibility conditions and time limits. A summary can be factually accurate yet misleading if it omits a restriction in the next paragraph. Check whether the answer distinguishes a general rule from an exception, a forecast from an observed result and an internal assumption from a contractual requirement.
Resilient: would it survive a changed case?
Test the answer against an exception, a different audience and a plausible change in the facts. Ask what happens if a date moves, a customer is outside the usual region, a system is unavailable or a user lacks the assumed permission. An answer that works only for the example in the prompt is not a dependable procedure. Microsoft describes feedback from its validation process as a signal rather than final authority and advises checking trusted sources when the stakes matter.
5. Match the verification method to the output
- Facts and dates: check the original policy, system record, published filing or other designated authority; confirm the applicable version and date.
- Calculations: reproduce the inputs and formula independently, verify units and rounding, and test an edge case.
- Summaries: compare them with the full document, including footnotes, exclusions and appendices; ensure the intended audience will not infer more than the source supports.
- Recommendations: list the criteria, alternatives, assumptions and potential harms; ask the accountable subject-matter owner to approve the reasoning.
- Emails and public copy: verify every factual promise, recipient, attachment, deadline and disclosure; remove confidential material and adjust tone for the relationship.
- Code, formulas and configuration: inspect line by line, test with safe sample data, review permissions and keep a rollback path before deployment.
6. Approve, edit, escalate or discard
- Preserve the input and output context. Keep the source version, prompt, generated result and review notes when your organization’s retention rules require a record. This makes later correction possible.
- Mark claims by status. Separate independently verified facts, calculations you reproduced, assumptions, unresolved questions and recommendations. Do not let a draft’s formatting blur those categories.
- Restore what the model omitted. Add conditions, exceptions, links to the approved internal record, ownership and next steps. Rewrite statements that are technically true but misleading for the recipient.
- Obtain the required approval. Route financial, personnel, legal, safety, security, compliance or customer-impacting work to the designated owner. A second person should review when policy or risk requires it.
- Choose the outcome. Approve only claims you can support, edit the rest, escalate uncertainty or discard the output. “The AI said so” is not an acceptable approval reason.
- Monitor after release. Watch for corrections, customer questions, failed assumptions and downstream effects. If an error appears, stop further distribution, notify the owner and correct the record through the organization’s incident process.
7. Practical examples
Drafting an internal email
Suppose you have approved meeting notes and need a concise follow-up. Ask the tool to use only those notes, identify decisions separately from proposed actions and leave unknown owners or dates blank rather than guessing. Compare the draft with the notes, confirm each recipient and attachment, remove sensitive details that the audience does not need and check that the tone does not turn a suggestion into a commitment. You remain responsible for sending it.
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Summarizing a policy for a team
Provide the current policy version if policy permits and request section references, effective dates, exceptions and a list of questions the policy does not answer. Check the summary against the complete document, especially definitions and scope. Ask the policy owner to review it before you present it as guidance. If the summary conflicts with an internal rule, the rule wins and the AI draft is discarded.
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Analyzing a spreadsheet
First remove identifiers that are not needed and confirm that the approved tool may process the remaining data. Ask for a description of the columns used, formulas, missing values, outliers and assumptions. Recalculate key figures independently, inspect the rows behind surprising results and test whether a different date range changes the conclusion. Present the output as analysis for a decision owner, not as an automatically generated decision.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.8. Common failure modes and their fixes
The answer is fluent but fabricated
Invented sources, case numbers, quotations and product capabilities often look plausible. Require source passages, open every citation and compare the wording. If a source cannot be verified, remove the claim.
A correct fact is applied to the wrong situation
The model may omit jurisdiction, contract scope, version, audience or eligibility. Add those constraints to the prompt, then check context and resilient behavior with an exception case.
The model fills a gap with a guess
Instruct it to label missing information and ask a question instead of completing a name, date, amount or owner. Replace guessed values with confirmed records before use.
Confidential information enters an unapproved service
Stop the transfer, follow your organization’s incident or privacy procedure and ask whether the data must be rotated, deleted or disclosed. Do not try to solve a policy breach by deleting only the visible chat; retention and logging depend on the service and organizational controls.
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Review becomes a rubber stamp
Assign a named reviewer, require evidence for material claims and give the reviewer enough time to check it. For high-impact work, use an independent source or second reviewer rather than asking the same tool to confirm itself.
Urgency removes the safety step
If there is no time to verify, do not present the output as confirmed. Escalate, use an existing approved template or complete the task manually. A deadline changes the available process; it does not change who is accountable.
9. Build a repeatable team practice
Teams get safer results when the workflow is visible rather than dependent on individual caution. Document which tools and accounts are approved, what data classes may be entered, who reviews each risk category, how prompts and outputs are retained, and where incidents are reported. Keep a short checklist with the four validation questions: source, verified, context and resilient.
Train staff to recognize fabricated citations, omitted conditions and overconfident language. Review real near misses and update prompts, templates and approval routes. Measure whether people can find and check the authoritative record, not merely how many AI drafts they produce. Do not claim that a checklist makes every output reliable; it is a control that helps people exercise judgment.
Apply the task-selection axes each time the work changes. A routine internal draft may be suitable for AI-assisted review today, while the same wording used in a safety notice, customer commitment or regulatory filing may require human-led work. There is no universal automation threshold that overrides policy and professional judgment.
10. What accountability looks like
The final reviewer should be able to answer four questions: What was the tool asked to do? What information was it allowed to use? Which claims were checked and against which records? Why was the resulting action appropriate for this audience and level of risk?
If those answers cannot be given, pause before sharing or acting. Seek the owner of the data, the relevant subject-matter expert or the security and privacy team. AI can shorten the path to a useful draft, but only a responsible person can authorize the outcome.
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