Before entering work information into a generative AI tool, check that your employer approves the service for that type of data. Treat prompts, pasted text, uploaded files, and connected data sources as information you are sending to a service—not as private notes. If the tool and workflow are not explicitly approved for sensitive information, do not put that information into them.
Why a prompt can expose workplace information
A prompt can disclose information even when it is phrased as a question. A pasted email, spreadsheet excerpt, code sample, or uploaded document may contain personal data, customer details, credentials, confidential plans, or other restricted material. Depending on the service and configuration, that information may be processed or stored outside your organization’s network, or be accessible to people or connected services you did not intend.
The UK Government’s 2023 generative AI framework says organizations should know where data is processed, whether it is stored or accessible to others, and ensure a tool can access only the organizational data needed for its task. See the UK Government generative AI framework.
Check your employer’s rules before using work data
Use your organization’s approved-tool list and data-classification rules first. Approval for one service, account, or task does not automatically mean that every version of the service—or every kind of information—is approved. Employer policies vary, so do not treat another organization’s rule as your own.
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For its own civil-service context, UK Government guidance says: “You should never put sensitive information or personal data into these tools.” The UK Home Office engineering guidance is similarly explicit for its context: “Sensitive or restricted information must not be shared with AI tools unless explicit approval has been granted through appropriate governance and assurance processes.” These are official UK institutional instructions, not universal law. See the UK Government framework and UK Home Office engineering guidance.
- Check whether the specific AI service is approved, including whether approval applies to your account or work tenant.
- Check the classification of the information and whether that category may be used with the tool.
- If the rules are unclear, ask your security, privacy, or IT team before sharing the information.
- If sensitive information is necessary for the task, use only a workflow explicitly approved for that information.
Minimize what you put into prompts and uploads
Give the AI only the information it needs to complete the task. Often you can preserve the structure of a problem while removing the sensitive details.
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- Replace names, email addresses, phone numbers, account numbers, and customer identifiers with neutral labels such as “Customer A.”
- Remove credentials, access tokens, private keys, and other secrets; do not paste them into a prompt.
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- For a document, share only the relevant excerpt rather than uploading the entire file or folder.
Redaction should remove the sensitive value itself, not merely hide it visually while leaving it selectable or embedded in the file. Microsoft’s guidance on sensitive information disclosure recommends approaches such as masking or using synthetic or redacted data where possible.
Verify the service and workflow when sensitive data is approved
A service’s name or “enterprise” positioning is not enough to establish that a particular use is safe. Before using a protected workflow with sensitive information, confirm the actual terms and configuration for your organization, subscription, and task. The UK framework calls for understanding where data is processed and whether it is stored or accessible to others; Microsoft’s statements about Copilot protections are specific to its products and subject to service and subscription differences.
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- Data use and training: Check contractual terms describing whether prompts, files, or outputs may be used to improve or train models.
- Retention and deletion: Find out what is retained, for how long, and what deletion controls apply.
- Processing location: Confirm where data is processed and what residency boundaries apply.
- Isolation and permissions: Check how the service separates your organization’s data and enforces user access.
- Labels and access controls: Confirm whether existing sensitivity labels and permissions are respected in the relevant AI experience.
- DLP and monitoring: Check whether prompts and uploads are covered by data loss prevention (DLP), warnings, blocking, or audit controls in your configuration.
- Connected agents and services: Review their separate terms and data handling. A connected agent may not have the same protections as the main AI service.
Microsoft’s Microsoft 365 Copilot data-protection documentation, last updated August 18, 2026, describes product-specific protections and cautions that behavior depends on subscription and service. For example, web-search queries and agent services can have distinct data handling. Check the current documentation and your tenant’s configuration rather than assuming protections apply uniformly.
What organizations can do to reduce accidental disclosure
Employee caution is only one layer. Organizations can combine clear data rules with technical controls, while verifying that each control is available and active in the relevant product, license, integration, and configuration.
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- Classify and label data: Make it clear which information may be used with approved AI tools and which requires additional approval or must not be shared.
- Limit access: Give AI tools and connected agents access only to the data needed for their tasks, and preserve user permissions.
- Apply DLP to AI interactions: In supported configurations, policies can inspect prompts and uploads and warn or block sharing that matches sensitive-data rules.
- Audit usage: Use available audit and eDiscovery controls to help investigate how information was handled.
- Test the controls: Confirm that policies cover the relevant AI app, prompt, file-upload path, and connected services. A control described in product documentation is not necessarily enabled for every user or workflow.
Microsoft documents AI-related DLP and sensitivity-label capabilities in supported configurations in its Microsoft Purview documentation for DLP with Microsoft Copilot. Its coverage depends on the product and setup.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Review AI outputs and handle them carefully
Protecting information does not end when a response appears. Generative AI can omit context or produce convincing errors. Check facts, calculations, summaries, and conclusions against the original material before relying on them, and follow workplace rules when handling the output. An answer may itself reproduce sensitive information from the prompt or connected sources.
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The UK Government framework recommends human quality assurance and safeguards to check accuracy and detect data leakage. UK civil-service guidance also advises verifying outputs and taking care with code and data. See the framework and the UK civil-service generative AI guidance.
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