Before using an AI tool for work, check that your employer approves both the tool and the task, keep restricted information out unless the approved setup permits it, verify anything the tool produces, and take responsibility for consequential decisions. Permission, data handling and legal obligations depend on your employer’s policy, the tool’s configuration and the jurisdiction.
Can you use an AI tool for work?
Only your employer can tell you which tools and uses are approved in your workplace. A tool being publicly available—or useful for a task—does not mean it is permitted for company work. Check the current acceptable-use, security, privacy or AI policy, and confirm that it covers the specific task you have in mind.
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This distinction matters because an organization may allow a tool for low-risk drafting but restrict it for customer records, confidential documents, code, hiring or other consequential work. NIST’s Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile (NIST AI 600-1, 2024) treats acceptable-use guidance, education, data protection and risk controls as part of organizational governance. It is risk-management guidance, not a rule that authorizes any particular employee or product.
- Check whether the tool is approved for your workplace, not just whether it is familiar or popular.
- Check whether the intended use is allowed; approval of a tool does not necessarily approve every task.
- If the policy is unclear, ask your manager, IT or security team, privacy or legal contact, or AI-governance lead before proceeding.
What information can you enter?
Treat every prompt, pasted passage and uploaded file as an information disclosure. Before supplying personal data, customer information, confidential material, credentials, source code or other restricted content, check your employer’s rules and the exact configuration approved for that work.
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NIST identifies data protection and data retention as governance considerations, but those points do not establish what a particular service stores, how long it keeps information, whether it uses it for training, or who can access it. Those details depend on the product, contract and employer configuration. Do not assume that a workplace account or a setting with a privacy-related name makes every category of information safe to submit; confirm the applicable rules with your employer.
Can your employer see what you type?
There is no universal answer. The reviewed guidance does not establish whether a particular employer can view prompts or how a particular AI service logs or retains them. Visibility and retention depend on the employer’s systems and policies, the service configuration and relevant agreements. Check your workplace notice or ask the appropriate IT, security or privacy contact rather than assuming either that prompts are private or that every prompt is monitored.
In the UK, Information Commissioner’s Office guidance on monitoring workers says employers should have a clear purpose, use the least intrusive means, and explain monitoring to workers. It also notes that homeworking raises additional privacy considerations and that an employer remains responsible for its processing when a third party supplies a tool. The ICO page reviewed on 7 October 2026 says the guidance is under review following the Data (Use and Access) Act; it is UK-specific and should not be treated as a statement of the law elsewhere.
How should you check AI-generated work?
Fluent wording is not proof that an answer is correct. Check generated work against reliable source material before relying on it, sending it to someone else or presenting it as fact. NIST notes that generative AI risks and long-term performance may be less understood than those of non-generative AI, supporting a cautious approach to review rather than automatic trust.
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- Facts and citations: confirm claims in authoritative source material; verify that cited sources exist and actually support the claim.
- Numbers and calculations: recalculate important figures and check assumptions, units and dates.
- Summaries and translations: compare them with the original, especially for omissions, qualifications, tone or meaning that could affect a decision.
- Code and technical instructions: inspect, test and review them under your team’s normal security and quality process before use.
- Fit for purpose: check that the result follows the task, protects confidential details and is appropriate for the intended audience.
These are practical review habits, not a guarantee that every error can be caught. For material you cannot confidently verify, do not pass it on as established fact; ask a qualified colleague or use a more reliable source.
When does a person need to make the decision?
For decisions that affect people or other important outcomes, human review must be a real assessment, not a rubber stamp. The reviewer needs enough understanding and competence to assess the output, access to relevant information, and authority to disagree or override it. The ICO’s guidance on individual rights in AI systems says reviewers should understand system limitations, anticipate when outputs may mislead, maintain scepticism and receive training. It also says reviewers should have authority to override an AI output and confidence they will not be penalized for doing so.
Under the UK GDPR, the ICO warns that routine agreement with recommendations without genuine assessment may make a decision effectively solely automated. The legal assessment depends on the facts and applicable law; the ICO guidance is UK-specific. In practice, follow your employer’s review process and do not let an AI recommendation substitute for your own accountable judgment where a person or important outcome is at stake.
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If you are choosing between tools for an approved task, compare them against the work and your employer’s controls rather than assuming that a particular brand or feature makes one safe.
- Approval: Is the tool and this use case approved by your employer?
- Information handling: What may be entered, and what do the employer’s configuration and rules say about access, retention and protection?
- Verifiability: Can you check the output against reliable evidence or test it appropriately?
- Human review: Can a competent person meaningfully assess and override the output when consequences matter?
- Task fit and accessibility: Does the tool suit the actual task and the people who need to use or review its output?
These checks are more useful than assuming that policies, data practices or capabilities are identical across tools. The cited guidance does not compare named products or vendors.
Quick Recap
A quick check before you start
- Confirm permission: check your employer’s current policy for both the tool and the task.
- Check the data: identify whether your prompt or files contain personal, confidential or otherwise restricted information, and confirm that the approved configuration allows it.
- Plan verification: know what source, test or qualified reviewer can validate the result before you rely on it.
- Keep responsibility clear: for consequential work, make sure a capable human can assess and override the output.
- Escalate uncertainty: ask your manager or the relevant IT, security, privacy, legal or AI-governance contact if any of those checks is unresolved.
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