Use workplace AI as an assistant, not an authority. Before you enter any information, check that the service is approved and that your employer permits that data. Define what could go wrong, ask the tool to show sources and assumptions, verify important claims against authoritative records, restore missing context, and keep a person accountable for the final decision or message.
A fluent response is only a draft until someone with the right knowledge checks it. The amount of checking should rise with the potential harm, the difficulty of detecting an error, and the difficulty of undoing the action.
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Start with your employer’s rules and the data boundary
Your first question is not “Which prompt should I use?” It is “Am I allowed to use this service for this task and this information?” Microsoft’s workplace safety guidance advises following organizational policy and using company-authorized AI services. That policy may specify approved products, retention settings, account types, confidential-data rules, and review requirements.
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Check the tool before you type
- Confirm that the service is on your organization’s approved list and that you are signed in through the permitted work account.
- Check the classification of every item you plan to paste or upload. Personal information about employees or vendors, customer records, unpublished financial results, credentials, security details, contracts, and trade secrets may be restricted.
- Use the minimum information needed. Remove names, account numbers, direct identifiers, and irrelevant attachments when a task can be completed without them.
- Know where the output will go. A draft copied into a public channel, customer email, ticket, or document may become a business record.
Microsoft recommends signing in with a work account for its Copilot experiences to help maintain appropriate access and file protections. That is product guidance, not a replacement for your employer’s data-classification policy, a contract, or applicable law. If the policy is unclear, pause and ask your manager, security team, privacy officer, or records owner before submitting the information.
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Decide how much human control the task needs
Do not apply one universal “AI is safe” or “AI is unsafe” rule. Classify the task before choosing an AI role. Microsoft’s task-selection guidance uses four practical dimensions: repeatability, impact, error detectability, and time sensitivity.
| Question | Lower-risk pattern | Higher-risk pattern | Default approach |
|---|---|---|---|
| How repeatable is it? | A stable format such as turning approved notes into a first-draft agenda | A novel judgment, investigation, or exception case | AI can assist routine work; keep novel judgment human-led |
| What is the impact of an error? | Minor rework or an internal wording change | Customer harm, safety consequences, financial loss, discrimination, or a major business decision | Increase human review as consequences rise |
| How easy is an error to detect? | You can compare every item with a complete authoritative list | The mistake is subtle, plausible, or hidden in an interpretation | Require subject-matter review when errors may evade casual reading |
| How much time and reversibility do you have? | There is time to check and the action can be undone | An urgent, irreversible, or externally binding action | Do not let urgency remove required approval |
AI-assisted with review
Use this mode for repeatable, bounded work where a reviewer can check the result against clear source material. Examples include reorganizing an approved policy into a checklist, proposing headings for a report, or drafting an internal email from facts you provide. The AI produces a starting point; the reviewer verifies it before anyone relies on it.
AI-supported but human-led
Use AI for search terms, alternative explanations, summaries, or questions to investigate while a qualified person owns the reasoning. This fits ambiguous analysis, planning, and recommendations where the tool can accelerate preparation but cannot establish the answer.
Fully human-led
Keep the work human-led when an error could cause substantial harm, when reliable verification is unavailable, when the task involves sensitive information that is not approved for the tool, or when there is no realistic opportunity to review before action. A tool may still help with low-risk administrative support around the work, but it should not make or silently determine the consequential judgment.
Ask for work you can inspect, not just a polished answer
A prompt that requests a confident conclusion encourages an answer that is difficult to audit. Ask for an inspectable work product instead. State the task, the permitted source material, the audience, the date or version that matters, and the limits on inference.
Useful instructions to include
- “Use only the policy text below. If it does not answer a question, say ‘not stated’.”
- “List each factual claim, the supporting passage or record, and any assumption you made.”
- “Separate quoted facts from your interpretation and label uncertainty.”
- “Show the calculation, units, dates, and rounding.”
- “Identify exceptions, dependencies, missing inputs, and cases in which this recommendation would fail.”
- “Draft for this audience and flag language that needs approval rather than inventing a commitment.”
For a factual or analytical task, request the source title, version, date, and relevant section. Treat a citation or explanation generated by the tool as a lead to inspect, not as proof that the cited source supports the claim. The model may misquote, cite a non-existent passage, or attach a real source to an unsupported conclusion.
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Verify every important claim against an authoritative source
NIST’s Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile (NIST AI 600-1, published July 26, 2024) recommends: “Deploy and document fact-checking techniques to verify the accuracy and veracity of information generated by GAI systems, especially when the information comes from multiple (or unknown) sources.” In practice, verification means opening the underlying record and checking what it actually says.
Build a claim-to-source check
- Extract the claims. Mark names, dates, quantities, legal or policy statements, causal explanations, quotations, and recommendations. Do not let a paragraph’s smooth style hide several separate assertions.
- Find the controlling record. Use the current contract, approved policy, official technical documentation, transaction record, dataset, or named subject-matter owner. Prefer the source that governs the decision, not a summary of it.
- Compare meaning, not just keywords. Check whether the source supports the same scope, conditions, population, time period, units, and exceptions. A source can contain the right number while the AI has applied it to the wrong scenario.
- Record the result. Note which claims are supported, unsupported, overstated, outdated, or still unresolved. Preserve this trail when the work affects a customer, regulated process, financial decision, or internal control.
- Correct or remove. Rewrite the claim to match the evidence. If you cannot establish it, omit it or label the uncertainty plainly; do not fill the gap with a plausible guess.
When a response combines multiple or unknown sources, increase the checking effort. NIST’s recommendation is especially relevant there because a model can blend incompatible definitions, dates, or levels of authority into one confident paragraph.
Run the four checks before you send, publish, or act
Microsoft’s Copilot validation guidance provides a compact review aid. Use it as a disciplined sequence, not a guarantee that an answer is correct.
1. Source
Does the output match the supplied or cited material? Identify unsupported statements, changed meanings, invented quotations, and conclusions that go beyond the record. If the tool names a source, inspect the original rather than trusting the reference.
2. Verified
Independently confirm important names, dates, figures, formulas, version numbers, and factual claims. Check spelling of people and organizations, currency and units, time zones, and whether a policy was current when the answer was produced. A second AI response is not independent confirmation.
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3. Context
Look for omitted caveats, dependencies, risks, definitions, eligibility limits, and background. Ask who is affected, what assumptions the answer makes, what would change the result, and whether the audience might interpret a conditional statement as a promise.
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4. Resilient
Test the answer against exceptions and changed conditions. Would it still be safe for a different customer, jurisdiction, product version, data range, or audience? What happens at the boundary of the rule? Could a malicious, unusual, or incomplete input make the recommendation misleading?
Microsoft describes feedback from its systems as a signal rather than final authority and advises checking trusted sources when the stakes matter. A response that passes a quick read can still fail one of these four tests.
Use a practical review record for consequential work
For work that will be shared externally, drive a decision, or enter a controlled process, keep a short record. It does not need to reproduce every prompt, but it should let another reviewer understand what happened.
- Task and owner: what the AI was asked to do and who is accountable.
- Tool and account: the approved service and work account used.
- Inputs: the source documents or data categories, with sensitive details handled according to policy.
- Output date and version: when the response was generated and which source versions were available.
- Checks performed: the authoritative records compared, calculations rerun, and exceptions tested.
- Disposition: approved, edited, escalated, or discarded, with the reason.
This record helps a colleague reproduce the check and makes it easier to correct an error later. It also prevents a common failure mode: assuming that because someone read the draft once, no one needs to know what was verified.
Examples: choosing the right level of reliance
Drafting an internal project update
Provide approved notes and ask for a concise draft with unresolved items marked. Compare every date, owner, milestone, and dependency with the project tracker. Remove invented progress language and have the project owner approve the final version before distribution.
Summarizing a contract or policy
Use the current document and request section references, definitions, exceptions, and items not stated. Read the cited clauses yourself. Do not treat the summary as legal advice or as a substitute for the person authorized to interpret or approve the agreement.
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Explaining a technical incident
Ask the tool to organize logs and propose hypotheses, but preserve the raw evidence and distinguish observed events from inferred causes. A human incident lead should validate the timeline, test competing explanations, and approve remediation. Do not paste secrets, credentials, or restricted customer data into an unapproved service.
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Keep the decision human-led. Check the governing policy, relevant records, and fairness or privacy requirements. Do not use a generated score or narrative as the sole basis for employment, access, eligibility, disciplinary, health, safety, or financial action. Escalate when the rules or evidence are unclear.
Responding during an urgent outage
AI can help format a status update from verified facts, but urgency does not make an unverified cause true. Assign one person to maintain the factual timeline and another, where possible, to review external wording. Record corrections as the incident develops.
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The answer sounds right but has no support
Ask for the exact source passage, then open the source. If no authoritative support exists, remove the statement or present it as an unverified hypothesis for investigation.
The citation is real but the conclusion is wrong
Compare scope, date, definitions, and exceptions. Rewrite the claim to what the source establishes rather than what the model inferred.
The summary omits a condition
Return to the full document and search for “except,” “unless,” eligibility limits, dependencies, effective dates, and footnotes. Add the condition to the output or route the matter to a subject-matter owner.
The model invents a name, number, or quotation
Verify it in the original record. If it cannot be verified, delete it. Never preserve an invented detail because it makes the document look complete.
The input may violate policy
Stop using the tool for that material. Follow your organization’s incident or privacy-reporting procedure if restricted information was already submitted, and ask the authorized team how to proceed.
There is no time to review
Reduce the scope, delay the action, or keep it human-led. If the task is consequential and an error would be hard to detect or reverse, lack of review time is a reason not to rely on the generated answer.
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Keep accountability with the person using the output
AI can draft, classify, summarize, calculate, or suggest. It cannot accept responsibility for a customer commitment, a policy interpretation, a security action, or a decision affecting a person. The worker who shares or acts on the result remains responsible for checking it and for escalating uncertainty.
Microsoft’s Security Copilot documentation calls for particular care in consequential or sensitive areas and says users should review and verify responses before acting. That product-specific guidance illustrates a broader rule: automation may change who performs a step, but it does not transfer accountability away from the organization and its authorized decision-makers.
A repeatable pre-use checklist
- Is this tool and account authorized for the task?
- Is every input permitted, minimized, and handled under the applicable data rules?
- What is the impact if the answer is wrong, and can the action be undone?
- How easy would a subtle error be to detect?
- What source material should control the answer?
- Have I requested assumptions, calculations, uncertainty, and exceptions?
- Did I check source, verified facts, context, and resilience?
- Has the right person approved the final wording or action?
- Do I need to preserve a review record or escalate the task?
If any answer is unknown, treat the output as a draft for investigation rather than a result you can send or act on.
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