The Tool Desk
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These practices reduce risk but do not replace your employer’s policy, a security review or jurisdiction-specific legal advice.
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Why workplace AI can expose sensitive information
Generative AI systems process the text, files, images or code you send through a service. The risk is not limited to a provider deliberately training a model on your prompt. Sensitive information can be revealed in logs, support processes, connected applications, shared conversation histories, misconfigured permissions or generated outputs. Information that looks harmless in isolation can also be combined with other details to infer a person’s identity, health, finances, location or employment status.
NIST describes both direct disclosure and inference as risks. A prompt can therefore be unsafe even when a vendor says that the prompt is not used for model training. Training is only one part of the data lifecycle.
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Training is not the same as every other use
“Not used to train” may address one specific purpose while leaving other questions unanswered. You still need to know whether the service retains prompts for abuse monitoring, how long administrators can retrieve conversations, whether human reviewers or subprocessors can access content, and whether an integration copies data into another system. Ask about the named product, plan, account settings and contract rather than relying on a general statement about a company’s consumer or business offerings.
Connected tools create additional paths
An AI assistant connected to a document drive, ticketing system, email account or code repository may retrieve more information than the text you typed. Plugins, extensions and subprocessors can add their own storage and access rules. The Information Commissioner’s Office recommends looking at the complete flow across software, data, organizational workflows and business processes.
Four rules that prevent most avoidable mistakes
1. Use the approved-tool list
Start with your employer’s approved services and the information categories each one may handle. If a service is not approved for the category of data you have, do not paste that data into it while waiting for a policy exception. A free consumer account and a paid organizational account may have different controls, contracts and retention behavior.
2. Classify the information first
Apply your organization’s labels, such as public, internal, confidential, personal data, regulated data or trade secret. Classification should happen before you open the prompt box. If the label is unclear, ask the data owner or security team; do not treat uncertainty as permission.
Classification is useful only if it changes the handling decision. A confidential customer list, for example, should not be sent to a service authorized only for public or internal material.
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3. Minimise what you disclose
Send only the fields and passage needed to complete the task. Remove names, email addresses, account numbers, exact dates, unique case references, credentials, secrets and unnecessary free-text notes. A narrow excerpt or an abstracted description is safer than a complete document.
Use fictional or synthetic examples when they can answer the same question. Redaction is not automatically anonymisation: a rare job title, combination of dates or distinctive event may still identify someone when combined with other information. Consider whether a recipient could reconstruct the person or organization from the remaining details.
4. Check the actual service terms
Before approving a workflow, verify the product and plan’s terms for:
- whether prompts, uploaded files and outputs are used for training or other purposes;
- default and configurable retention periods, deletion behavior and any eligibility for zero-retention processing;
- access by end users, administrators, provider personnel and support teams;
- processing and storage locations, including cross-border transfers;
- connected applications, plugins, subprocessors and permission scopes;
- audit logs, administrator controls, encryption and identity-management options; and
- contractual, confidentiality, security and incident-notification commitments.
Terms can change, and a setting may apply only to a particular plan or API configuration. Record the date and version of the terms you relied on and review them when the service, contract or workflow changes.
A safe workflow from request to deletion
Before using an AI tool
- Define the task. Write down the outcome you need—such as a summary, outline, classification or code explanation—without starting with the whole source document.
- Identify the data owner and classification. Decide whether the information includes personal, confidential, regulated or secret material, and check the policy for that category.
- Confirm the service and account. Use the organization’s approved workspace, not a personal account or an unapproved browser extension. Confirm that the feature you plan to use is covered by the organization’s review.
- Reduce the input. Remove identifiers and irrelevant sections. Replace real values with consistent placeholders or synthetic values if the task does not require the originals.
- Check integrations. See which folders, repositories, calendars, mailboxes or applications the assistant can read. Disable broad connectors when a narrow scope is sufficient.
At prompt time
- Describe the context at the lowest useful level. For example, state “a European employee in a 200-person company” instead of supplying a name, exact location and personnel number when those details do not affect the answer.
- Provide only the necessary excerpt. Ask for a summary of a selected paragraph rather than uploading an entire contract containing unrelated parties and pricing.
- State handling limits in the instruction. Tell the model to omit personal identifiers from its response and to mark uncertain conclusions for human review. This does not create a security boundary, but it can reduce accidental reproduction.
- Do not enter secrets. Never paste passwords, private keys, session tokens, unreleased credentials or authentication codes. Use an approved secrets-management or testing process instead.
- Inspect the destination. Check whether the conversation, attachment or generated file will be visible to a team, retained in a shared workspace or synchronized to another application.
After the model responds
- Review factual and privacy content. Remove copied identifiers, confidential passages and unsupported claims before forwarding the output.
- Check for inference. A response can combine harmless-looking clues into a sensitive conclusion. Treat inferred health, financial, disciplinary or identity information as sensitive even if you never typed the conclusion directly.
- Store only in approved locations. Move the final, reviewed artifact to the approved document system or repository. Do not leave sensitive drafts in personal downloads, unmanaged chat histories or public links.
- Apply the normal retention rule. Follow the organization’s records schedule and deletion process. Do not invent a universal period for AI conversations; the correct period depends on the business purpose, legal obligations and policy.
- Report mistakes promptly. If restricted data went to an unapproved service or an output was shared incorrectly, preserve the relevant facts and use your organization’s incident-reporting channel.
How to compare AI services for a team
A procurement or security review should compare the actual deployment options, not just model quality. The following questions expose differences that marketing summaries often omit.
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| Comparison area | Questions to ask | Why it matters |
|---|---|---|
| Training and secondary use | Are prompts, files or outputs used for model training, product improvement, abuse monitoring or human review? Is the setting different for business, consumer and API offerings? | A “no training” promise may not cover every other processing purpose. |
| Retention and deletion | How long are inputs, outputs, logs and backups retained? Can administrators delete them? Is zero-retention available, and under what eligibility or feature restrictions? | Shorter, controllable retention limits the window for unauthorized access. |
| Access | Can end users, workspace administrators, provider staff or support personnel view content? Are roles, single sign-on, multifactor authentication and least-privilege permissions supported? | Every additional viewer or broad permission increases exposure. |
| Location and transfers | Where is data processed and stored? Which regions and transfer mechanisms apply to your organization? | Location can affect contractual duties and data-protection obligations. |
| Integrations and subprocessors | Which connectors, plugins and subprocessors receive data? Can each connection be restricted, logged or disabled? | A connected system may copy or expose information outside the original workspace. |
| Audit and administration | Are access, export, deletion, configuration and sharing events logged? Can administrators set retention, block features and investigate incidents? | Controls are difficult to enforce when activity cannot be seen or changed. |
| Contract and legal terms | Do the agreement and data-processing terms cover confidentiality, security measures, breach notification, subcontractors, deletion at termination and permitted purposes? | Contractual commitments define obligations beyond a product webpage. |
NIST’s guidance emphasizes third-party integration, input-data risk and procurement due diligence. Record the answers, the responsible approver and any compensating controls. If a provider cannot answer a question material to your data category, treat that uncertainty as a reason to restrict the use case rather than assume the most favorable interpretation.
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Examples of safer prompt design
Customer-support analysis
Risky approach: Paste a full customer email containing the person’s name, address, order number, payment details and a long history of unrelated correspondence.
Safer approach: Replace the name and order number with stable placeholders, remove the address and payment information, and provide only the paragraphs needed to categorize the issue. Ask for a category and a draft response that contains no personal data. A human agent can consult the approved customer system for the missing details.
Human-resources writing
Risky approach: Upload an entire performance file to ask for a short review summary.
Safer approach: Use an approved service authorized for the relevant employee-data category, or create a synthetic record with the same structure. Exclude medical information, protected characteristics, names and dates that are not needed for the writing task. Have the responsible HR professional verify tone and facts before anything is stored or sent.
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Software development
Risky approach: Paste a production log containing user identifiers, IP addresses, authentication tokens or proprietary source code into a public tool.
Safer approach: Reproduce the error with synthetic values, remove tokens and identifiers, and share the smallest code fragment that demonstrates the issue through an approved developer workspace. Rotate any credential that may have been exposed, even if the model did not appear to repeat it.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What administrators should configure
Limit who can use which data
Map information classifications to approved features and groups. Block personal accounts for company work, require organizational identity controls, and use least-privilege access for connectors. A user who needs help drafting public documentation does not automatically need access to an internal repository or mailbox.
Control integrations and exports
Enable only the connectors required for a documented business purpose. Review permission scopes, service accounts and subprocessors. Restrict public sharing and downloads where the platform allows it, and monitor unusual bulk retrieval or export activity.
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Set retention and deletion controls
Choose retention settings that match the purpose and records policy. Confirm what deletion removes immediately, what remains in backups or security logs, and how deletion works when an employee leaves or a contract ends. Test the process with non-sensitive data before relying on it for a regulated workflow.
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Document guidance and training
Give staff a short decision rule: approved service, correct classification, minimum necessary input, no secrets, human review and approved storage. Include examples for common departments and explain how to report an accidental disclosure. Update the guidance when a model, plan, connector or contract changes.
Is local or on-device AI automatically safer?
Local processing can reduce some transfers to an external provider, but it is not a blanket exemption. The Information Commissioner’s Office states: “It is important to note that local processing is not necessarily out of scope of data protection law.” The organization’s jurisdiction, purpose, data and deployment still determine applicable duties.
An on-device model can expose data through an infected endpoint, weak file permissions, unencrypted backups, telemetry, synchronization or an output copied into an unapproved system. Evaluate local deployments with the same questions about access, retention, integrations, updates, logging and deletion. Treat “runs locally” as one architectural fact, not as a complete risk assessment.
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- Stop further sharing. Do not copy the conversation into additional systems or ask the model to reproduce the material.
- Record the facts. Note the service, account, time, data category, approximate content, recipients and any connected applications involved.
- Use approved deletion controls. Remove the conversation or file if policy permits, but do not assume deletion erases provider logs or backups.
- Notify the designated team. Contact security, privacy, legal or your incident channel according to the organization’s procedure. Prompt reporting allows the organization to assess notification and containment duties.
- Contain related credentials. Revoke or rotate passwords, tokens or keys that may have been included. Check access logs for downstream systems.
- Preserve evidence and follow instructions. Keep the relevant timestamps and configuration details, and do not independently alter records that an investigation may need.
Questions employees commonly ask
Can I paste confidential work information into ChatGPT?
Only when your employer has approved the specific ChatGPT service, account and data category, and the task complies with its policy. If approval is absent or unclear, use a redacted or synthetic example and ask the data owner before submitting real information.
Is company data used to train AI?
There is no universal answer. OpenAI says inputs and outputs from specified business services and its API are not used for training by default, and describes encryption and retention controls. That statement is provider- and product-specific, can change, and does not remove risks from access, integrations, retention, configuration or output handling. Verify the current terms for your exact plan and contract.
What should never go into a workplace AI tool?
Never enter passwords, private keys, session tokens or authentication codes. Also withhold personal, regulated, confidential or trade-secret information unless the service and workflow are explicitly approved for that category and the minimum necessary data has been prepared.
Can redaction make any document safe?
No. Remaining combinations of dates, roles, locations, events or rare attributes may identify a person or organization. Test whether someone with access to other records could infer the identity, and prefer synthetic data when the original details are not essential.
Who is responsible for checking the output?
The employee or team using the output remains responsible for reviewing accuracy, confidentiality and appropriate sharing. AI-generated text can contain invented facts or repeat sensitive details, so it should not bypass normal subject-matter, privacy or records controls.
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