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Use sensitive work information with AI only in an organization-approved service and account whose privacy, security, and data-handling terms your organization has reviewed. Before submitting anything, classify it, remove details the task does not need, and check how the app handles prompts, files, access, retention, and administrator logging. If you cannot establish that the tool and use are approved, do not enter the information; ask your privacy, security, or data owner for guidance.
What counts as sensitive data in an AI prompt?
Sensitive data is any information that could harm a person or organization if it were exposed, misused, retained unexpectedly, or used outside its intended purpose. It includes more than obvious items such as passwords or medical records. A prompt, attachment, pasted email, or AI-generated summary can contain sensitive information even when the main question seems harmless.
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Classify the material before opening an AI feature, using your organization’s labels and rules. Common categories include:
- Personal information: names, contact details, account numbers, employee records, identifiers, or free-text notes that reveal something about an identifiable person.
- Regulated information: health, financial, or other records governed by legal or organizational requirements.
- Confidential business information: customer files, vendor and procurement details, draft policies, internal evaluations, and non-public plans.
- Legal or security-sensitive material: privileged communications, credentials, security procedures, vulnerability details, or incident records.
- Proprietary information and trade secrets: source material, processes, designs, pricing, or other information that gives an organization a competitive advantage.
Public availability does not automatically make information appropriate for an AI system. The Office of the Australian Information Commissioner (OAIC) cautions that public information is not necessarily lawful to collect or use for AI training. Whether information may be submitted depends on its context, the purpose, the applicable rules, and the specific service and account.
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If you cannot tell whether a document is public, internal, confidential, regulated, or otherwise restricted, treat it as sensitive until the data owner or privacy/security team confirms how it may be used. Do not infer that an AI button embedded in a familiar workplace app is approved for every type of information.
Can I paste confidential work data into a public AI chatbot?
Do not put confidential, personal, regulated, or proprietary work information into a publicly accessible AI service unless your organization has explicitly approved that service and use. The Centers for Medicare & Medicaid Services (CMS), in workplace guidance reviewed in 2025, says: “Never disclose or input PII, PHI, or any sensitive CMS data (including financial, health, vendor, procurement, evaluations, draft policies, or proprietary/business information) into publicly accessible AI platforms, chatbots, or prompts.” That instruction applies to CMS personnel and data; it is also a clear example of why workers should follow their own organization’s rules rather than treating public chatbots as a safe destination.
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When there is no approved tool for the task, use a safer alternative: ask an internal expert, work from a public or synthetic example, or request an approved workflow. Do not test a public chatbot with a “small” piece of confidential data; a short excerpt can still identify a person, disclose a trade secret, or reveal a security weakness.
Does an employer-approved AI app train on workplace prompts?
There is no universal answer: training and data-use terms vary by provider, product, account type, configuration, and contract. Check the current documentation and applicable agreement for the exact work service you use; do not assume that a claim about one product applies to another or to a personal account.
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Microsoft’s product-specific documentation says that Copilot Chat signed in with a work or school account provides enterprise data protection: prompts and responses are not used to train foundation models, and chat data is encrypted during the session. Microsoft also says prompts, triggered Bing queries, and responses are logged for administrator audit. These details illustrate why “not used for training” is only one part of a privacy review: administrator visibility, logging, retention, and access still matter. The claims should not be generalized to other services or to consumer accounts.
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- Are prompts, attachments, outputs, or retrieved documents used to train or improve models?
- Which account type and product edition does the answer cover?
- Who can administer or audit prompts and responses, and what can they see?
- Where is the information processed or hosted, and which subprocessors may handle it?
- How long are prompts, files, logs, and derived data retained, and what deletion options apply?
- How are incidents reported, and what contractual terms govern the provider’s handling of data?
Use the organization’s approved tenant or application, not a personal account. If documentation, configuration, or contract language does not answer a material question, treat that uncertainty as a reason to pause and escalate rather than as permission to proceed.
How should employees use AI without exposing sensitive information?
Use this sequence for each task, including tasks inside workplace apps. A tool’s approval does not eliminate the need to minimize input, respect access controls, and review the output.
- Classify the material. Identify whether the source contains personal, regulated, confidential, privileged, security-sensitive, or proprietary information. If its classification is unclear, stop and ask the data owner or privacy/security team.
- Confirm the service and account. Use the organization-approved application and authenticated work account. Confirm that the intended feature and use case are covered by approval, rather than assuming that any AI function within an approved app is covered.
- Reduce the input to what the task needs. Remove names, identifiers, account numbers, credentials, exact dates, unnecessary free-text notes, and unrelated attachments. Replace real values with placeholders or synthetic examples when that preserves the task. Submit the smallest relevant excerpt, not an entire customer or employee file.
- Check the data path and permissions. Use approved connectors and least-privilege access. Know whether the app sends material to a model provider, plugin, repository, or other service, and whether those components are included in the organization’s approval.
- Review the answer before relying on it. Treat generated text, summaries, code, and classifications as drafts. Verify factual claims against authoritative records, look for information about other people that should not have appeared, and obtain qualified review before high-impact decisions.
- Keep records when policy requires them. Record the prompt, source material, reviewer, and final decision where your organization’s policy calls for it. Follow approved retention rules for those records instead of saving copies ad hoc.
- Escalate a mistake promptly. If sensitive information reaches an unapproved tool or appears in an output, stop sharing it, preserve relevant evidence, notify the security/privacy contact, and follow the incident and breach-reporting process. Do not try to conceal the event by deleting chats or logs.
How can you minimize or de-identify information before submitting it?
Minimization means giving the AI only the information necessary for the specific task. De-identification means reducing or removing links to a real person or entity. Neither term guarantees that the remaining material is safe: distinctive details, combinations of facts, or contextual clues may still identify someone or reveal confidential information.
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Remove unnecessary identifiers and context
Before copying text or attaching a file, review it for names, email addresses, phone numbers, account and case numbers, credentials, exact dates, and incidental details in free-text notes. Remove unrelated rows, pages, comments, tracked changes, hidden spreadsheet columns, and embedded metadata where relevant. A clean excerpt is usually safer than uploading a whole record to ask a narrow question.
Use placeholders or synthetic examples
Replace identifying values with labels such as “Customer A” or with realistic but invented examples. Keep any mapping between a placeholder and the real identity outside the AI service, in an approved location. Avoid including so many unique facts that a person can be recognized even without a name.
Consider privacy-preserving methods for larger projects
The UK Information Commissioner’s Office (ICO) recommends documenting personal-data movements and storage, deleting intermediate files when they are no longer required, and considering techniques such as de-identification, synthetic data, perturbation, federated learning, and differential privacy where appropriate. These are not interchangeable options or automatic guarantees: their suitability depends on the task, data, and risk. Involve privacy and technical specialists before treating a transformed dataset as safe for a new use.
What safeguards should an approved workplace AI app have?
Safeguards should address the full path from a user’s identity and input through the provider, integrations, storage, and output. No single setting, such as a model-training exclusion, substitutes for reviewing permissions, retention, auditability, and the intended purpose.
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| Control area | What to verify |
|---|---|
| Data use and model training | Whether prompts, files, outputs, or retrieved material may be used for training or service improvement, for the relevant account and configuration. |
| Identity and permissions | Single sign-on or strong authentication, role-based access, least privilege, and limits on what users and connectors can retrieve. |
| Encryption and keys | How data is encrypted in transit and at rest, and what key-management controls are available and required. |
| Logging and administrator access | What prompts, outputs, queries, and actions are logged; who can inspect them; and how audit access is controlled. |
| Retention and deletion | How long prompts, attachments, logs, embeddings, caches, and intermediate exports remain, and how deletion is carried out. |
| Hosting and subprocessors | Processing and storage geography, provider subprocessors, and whether those arrangements meet organizational requirements. |
| Privacy and audit support | Whether the provider and configuration support the organization’s privacy-impact assessment, documentation, and audit obligations. |
| Incident response | How the provider notifies the organization of incidents and how responsibilities are allocated in the applicable terms. |
| Output quality and review | Whether outputs show sources where relevant and whether the workflow supports human review and correction. |
Verify claims against current product documentation, the actual configuration, and contract language before approval. Product features and terms can change. For government information, additional classification and authorization rules may apply: Australia’s Protective Security Policy Framework (PSPF) says generative-AI use with OFFICIAL information must go through existing authorization and security responsibilities.
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How should IT, security, and privacy teams approve an AI app?
Approval should happen before production use, not after employees have begun sending real records through a new integration. The ICO recommends a risk-based approach, privacy by design and default, and controls for lawful, fair, and transparent processing. The review should match the proposed purpose and data, rather than relying on a general statement that the tool is “enterprise” or “secure.”
- Set data boundaries. Define which data classes are allowed, restricted, or prohibited for each use case. Make those rules understandable to staff and consistent with the organization’s classification policy.
- Establish the purpose and authority. Identify the lawful purpose and processing basis where applicable, and update notices when required. Do not repurpose data simply because an AI feature can process it.
- Assess privacy and security risks. Complete a privacy impact assessment and a security threat/risk assessment before deployment. Identify possible unauthorized disclosure, inappropriate access, or other harm from the proposed data flows and outputs.
- Review the provider and terms. Verify jurisdiction, subprocessors, retention, deletion, model-training use, incident response, and contract terms for the particular product and configuration.
- Enforce identity and access controls. Require SSO and MFA where appropriate, role-based access, least privilege, approved connectors, and administrator audit logs. Ensure integrations cannot retrieve more information than the task requires.
- Map the data flow. Record what information moves between the app, model provider, plugins, and repositories, where it is stored, and who can access it. Include intermediate files and derived data, not just the original prompt.
- Test failure modes before production. Test prompt-injection, data-exfiltration, access-control, and output-leakage scenarios. Confirm that safeguards behave as intended under realistic misuse and error conditions.
- Set retention and deletion rules. Define schedules for prompts, files, embeddings, caches, logs, and intermediate exports. Include deletion responsibilities and processes for both the organization and providers.
- Train users and define escalation. Explain what they may submit, how to minimize information, how to validate outputs, and whom to contact after a suspected disclosure.
- Reassess material changes. Review controls when the model, connector, hosting region, policy, or use case changes. An earlier approval may not cover a materially different data path or purpose.
Why does AI output need a human review?
AI-generated answers are not reliable evidence merely because they sound confident or are produced inside an approved workplace app. The OAIC states that generative-AI systems can produce inaccurate or false results. CMS guidance also directs users to validate outputs using trustworthy sources, expert consultation, and independent research.
For summaries, check that important qualifications and exceptions survived compression. For code, inspect behavior and security implications before using it. For classifications or recommendations, compare the result with the underlying records and applicable policy. If the output concerns legal, HR, medical, financial, safety, or another high-impact matter, obtain review from an appropriately qualified person before acting on it.
Human review should include a disclosure check as well as an accuracy check: make sure the answer does not reveal another person’s information or repeat material that should not be shared with its intended audience. Keep the reviewer and final decision record when policy requires it. A reviewer should be able to inspect the relevant source material rather than relying on the AI summary alone.
What should you do after a sensitive-data mistake?
Act quickly and follow your organization’s incident process. A mistaken submission may require investigation even if the prompt is deleted or no one appears to have viewed it.
- Stop using the affected AI feature for the material and do not submit additional sensitive information.
- Preserve relevant evidence, such as the service, account, time, affected information, and any output or sharing that occurred, in line with internal instructions.
- Notify the designated security/privacy contact and follow the organization’s incident and breach-reporting procedure.
- Do not conceal the event by deleting chats or logs. Let the authorized team determine whether provider-side deletion, containment, or further notification is required.
CMS guidance calls for staff accountability, secure channels, output validation, and incident reporting. The precise reporting route and legal obligations depend on the organization, jurisdiction, information involved, and circumstances of the event, so use the internal process rather than assuming that a particular response fits every case.
Frequently Asked Questions
Does removing a person’s name make a document anonymous?
No. A person may still be identifiable from combinations of details, unusual events, or contextual clues. Whether data is effectively anonymous depends on the information left in it and the risk of linking it to other sources.
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Not when the original is needed to verify an important fact, qualification, or decision. A summary can omit context or introduce errors, so retain and consult the authoritative record under your organization’s access and retention rules.
Can I use public information in a workplace AI prompt?
Public availability alone does not establish that collection or processing is lawful or appropriate. Consider the purpose, the source, applicable rules, and your organization’s policy before submitting it.
Should I delete a chat after I accidentally shared sensitive data?
Do not delete chats or logs to hide the event. Preserve relevant evidence and notify the designated security/privacy contact, who can direct any authorized containment or deletion steps.
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