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Use only an organization-approved AI service for work data. Before entering anything, classify the information, remove details the task does not require, and confirm the exact product, plan, account settings, contract and integrations. Check whether prompts and files are used for training, how retention and deletion work, who can access the data, where processing occurs, and how outputs will be stored. These controls reduce risk, but they do not replace your employer’s policy or jurisdiction-specific legal advice.
Why ordinary prompts can expose sensitive information
An AI prompt is part of a larger data flow. Information may pass through the model service, connected applications, plug-ins, identity systems, administrator consoles, logs, backups and the employee’s own documents. The final answer can then be copied into email, a ticketing system, a report or another application. Protecting the prompt while ignoring those surrounding steps leaves important exposure paths open.
NIST’s generative-AI risk guidance warns that a system can reveal sensitive information or infer personal and sensitive details by combining apparently harmless pieces of data. A prompt therefore is not safe merely because the model is not intentionally trained on that particular prompt. Names, dates, job titles, locations, account numbers and unusual events can identify a person when combined, and a model may reproduce or infer information that was not obvious to the person writing the prompt.
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- Inference: several non-sensitive-looking details allow a person, household, customer or incident to be identified.
- Secondary access: an administrator, integration, subprocessor, log, export or compromised account can reach the data.
- Output leakage: generated text includes confidential details and is shared more widely than the original request.
Think of the AI tool as one component in your organization’s information-handling process, not as a private scratchpad.
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Start with classification and data minimisation
Identify the information category
Follow your employer’s information-classification policy before opening an AI tool. The policy may distinguish public, internal, confidential, personal, financial, health, security, export-controlled or legally privileged information. If the policy does not clearly say that a service may receive a category, treat the service as unapproved for that category and stop.
Classification is a risk-control step, not paperwork. It determines which tools, accounts, locations, approvals and retention settings are acceptable. NIST identifies classification as a way to reduce loss and mismanagement risk, while the Information Commissioner’s Office (ICO) recommends considering the complete flow of data and the purpose for which it is processed.
Ask whether the task needs real data
Before writing a prompt, define the smallest input that can produce a useful answer. Often the model needs the structure of a problem rather than the underlying record. Replace a customer name with “Customer A,” remove exact addresses, and describe a date as a month or relative period when precision is unnecessary. Exclude credentials, access tokens, private keys, payment details, medical information, employee identifiers and confidential contract language unless an approved workflow explicitly requires them.
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- Synthetic data: fictional records that preserve the shape of the problem without representing a real person or transaction.
- Redaction: deletion or masking of names, identifiers, account numbers, clauses and other unnecessary fields.
- De-identification: transformation intended to prevent identification, followed by a check that combinations of remaining details cannot reasonably point back to someone.
De-identification is not automatically safe. A rare job title plus a small town and an unusual incident may still identify an individual. Keep the original-to-replacement mapping out of the AI service, and do not paste the key that reverses your masking.
Use the minimum necessary excerpt
Do not upload a whole document when a short, abstracted excerpt answers the question. For a contract question, provide only the relevant clause with names, prices and unrelated terms removed. For a support issue, describe the error, software version and non-sensitive environment details instead of attaching a complete customer export. For writing help, provide a fictional paragraph that has the same tone and structure.
Minimisation also applies to conversation history. Start a new conversation when earlier context is no longer needed, avoid pasting the same sensitive material into multiple tools, and clear drafts or local downloads according to your organization’s rules.
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What to do before opening an AI tool
- Check the approved-tool list. Use the organization’s named service, account type and workspace. A consumer account, browser extension or personal API key may have different terms and controls from an approved business deployment.
- Confirm the data category is allowed. If policy approval covers internal information but not personal, health, financial or privileged data, do not infer that the broader categories are permitted.
- Define the task and minimum input. Write the question first, then remove every field that does not affect the answer.
- Prepare a safe substitute. Use fictional, synthetic, redacted or de-identified examples when they can produce the same result.
- Verify the account and destination. Check that you are signed in to the managed workspace, not a personal account, and that uploads will not sync to an unapproved storage location.
- Escalate uncertainty. Ask your security, privacy, legal or data-protection contact when classification, contractual terms or the intended use is unclear.
“It is important to note that local processing is not necessarily out of scope of data protection law,” the ICO states in its AI security and data-minimisation guidance. Running a model on a company device may change the data path, but it does not by itself make a use lawful or policy-compliant.
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How to write a safer prompt
Remove identifiers and confidential detail
Use role labels and broad descriptions: “an employee in the European sales team” is safer than a name and exact office when those details do not affect the answer. Replace customer IDs, invoice numbers, email addresses, phone numbers, precise locations and unique case descriptions. Never include passwords, one-time codes, private keys, session cookies or access tokens.
State constraints without exposing the source
Models can often edit, classify or summarize from a neutral description. Instead of uploading a confidential incident report, describe its sections and ask for a template. Instead of pasting a personnel file, provide fictional rows and ask for a formula or validation rule. If an exact quotation is essential, use an approved service and include only the authorized excerpt.
Separate preparation from execution
Ask the model to propose a transformation before supplying data. For example, request a regular expression that removes email addresses, then run and inspect it in an approved environment. Do not send the unredacted source merely because the model could perform both steps.
Inspect what leaves your device
Review browser extensions, document sidebars, meeting assistants and plug-ins. Some tools can automatically read the current page, mailbox, drive or code repository. Disable integrations that are not required, and treat an automatic attachment or context window as a data transfer even when you did not paste text manually.
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How to choose a secure AI service for your team
Compare the actual product and plan you will deploy, not a vendor’s general marketing statement. Terms can differ by edition, region, account configuration, API route and contract, and they can change over time.
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| Question to verify | What to check | Why it matters |
|---|---|---|
| Are prompts and files used for training or other purposes? | Default treatment, opt-out or opt-in controls, and whether business, consumer and API products differ. | Training is only one possible use; service improvement, abuse monitoring, support and logging may have separate terms. |
| How long is data retained? | Conversation, upload, log, backup and deletion periods; whether deletion is configurable; and eligibility for zero-retention processing. | A short visible history does not prove that copies, logs or backups were immediately deleted. |
| Who can access the data? | User roles, administrator visibility, provider personnel, support access, audit records and emergency access procedures. | Access controls determine who can read prompts, files and outputs during normal operation or support. |
| Where is data processed and stored? | Storage and processing regions, transfer mechanisms, regional availability and the effect of backups or subprocessors. | Location can affect contractual duties, cross-border transfers and applicable law. |
| What connects to the service? | Plugins, connectors, shared drives, email, source control, browser extensions and subprocessors. | An integration can expose more data than the prompt itself and can create additional accounts and logs. |
| What administrative and audit controls exist? | Single sign-on, multifactor authentication, role-based access, domain restrictions, data-loss controls, export, audit logs and policy settings. | Controls make it possible to limit use, investigate an event and demonstrate compliance. |
| What contractual commitments apply? | Data-processing terms, confidentiality, security measures, incident notification, deletion commitments, subprocessors and service-specific liability. | A public privacy page may not provide the commitments required for your data or jurisdiction. |
NIST highlights third-party integration and input-data risks and recommends procurement due diligence with clear controls. Record the evidence for the exact service, plan, settings and contract that your team approves. Recheck it when the provider changes features, regions, retention options or terms.
What a provider statement does—and does not—prove
OpenAI states that inputs and outputs from specified business services and its API are not used for training by default, and it describes encryption and retention controls. That is an example of provider-specific terms, not a blanket claim about every AI product and not a guarantee that every risk disappears. Your organization still has to confirm the applicable service, account configuration, integrations, user access, legal basis and output handling.
Controls for administrators and procurement teams
Approve a defined configuration
Document the permitted service, plan, regions, identity provider, enabled features, integrations, retention settings and allowed information categories. Block personal accounts for work data where your technical controls permit. Require multifactor authentication and least-privilege roles, and review administrator access regularly.
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Inventory every connector, plug-in, extension, subprocessor and automated workflow. For each one, record what it can read, what it can write, where it sends data and how it is disabled. Start with no integrations, then add only those that have a documented business need and security review.
Set retention and deletion rules
Align conversation history, uploaded files, audit logs, exports and backups with your organization’s retention schedule. Determine who can delete data, whether deletion is immediate or delayed, and what legal holds or backup copies mean in practice. A zero-retention option, when available, may have eligibility conditions and may not cover every feature or endpoint.
Prepare staff guidance
Give employees examples of permitted and prohibited inputs, a short classification decision tree, the approved-tool list and an incident-reporting path. Explain that generated output is also company information: it may contain confidential material, personal data, errors or fabricated citations and must be reviewed before distribution.
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Keep procurement evidence
Save the service terms, data-processing agreement, security documentation, subprocessor list, configuration record, risk assessment and approval date. Assign an owner to revisit those records when the product, contract, geography or organizational use changes.
Review the whole workflow, not just the prompt
The ICO recommends a holistic security view across software, data flows, organizational workflows and business processes. Map the path from source to final use:
- Source: identify the document, database, mailbox, repository or device where information originates.
- Preparation: record who extracts, redacts, summarizes or uploads it and which tools perform those steps.
- Processing: identify the AI endpoint, model features, region, integrations, logs and subprocessors involved.
- Access: list users, administrators, support personnel and automated accounts that can view prompts, files or outputs.
- Output: specify where the answer is saved, who can edit it, how it is labeled and whether it is sent to another system.
- Disposition: apply retention, deletion, legal-hold and incident-response rules to every copy.
This map catches risks that a model setting cannot fix. A correctly configured service can still leak data through a public shared folder, an overprivileged connector, a copied transcript or an employee’s personal device.
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Check accuracy and confidentiality
Read the entire response before sharing it. Remove reproduced names, identifiers, confidential quotations and unnecessary details. Verify facts, calculations, citations, code and decisions independently; a plausible answer can be wrong or can combine details from the prompt in an unsafe way.
Store outputs in approved locations
Save the final, reviewed work product only in the system authorized for that information category. Do not leave sensitive responses in a personal notes app, unmanaged download folder, public repository, shared link or chat channel with broader membership than the source document. Label drafts and generated material according to your organization’s policy.
Apply retention and sharing rules
Keep or delete the output according to the business record, privacy and legal requirements that apply to the work. The guidance does not prescribe one universal retention period; your organization must set the schedule for its data and jurisdiction. Remove temporary files and conversation exports when policy allows, and record any required evidence before deletion.
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Report mistakes promptly
If you paste unapproved information, expose an output to the wrong audience, discover an unexpected integration or lose control of a device, follow the employer’s incident-reporting process immediately. Do not attempt to hide the event by deleting the only evidence. Preserve the relevant prompt, timestamp, account and destination information as instructed by your security or privacy team.
Common questions
Can I paste confidential work information into ChatGPT?
Only when your employer has approved the specific service and account for that information category and the configured terms permit it. Otherwise, remove the confidential details or use a synthetic example. A provider’s business terms do not override your organization’s policy, contractual duties or applicable law.
Is my company data used to train AI?
It depends on the exact product, plan, endpoint, settings and contract. Verify training treatment separately from retention, abuse monitoring, support access, logging and subprocessors. Do not generalize a statement made for one business service to another service or to a personal account.
What should I avoid putting into an AI tool at work?
Unless an approved workflow specifically requires them, avoid credentials and secrets, direct personal identifiers, financial or health records, privileged advice, unreleased strategy, customer exports, security incident details and whole confidential documents. Also avoid combinations of seemingly harmless details that could identify a person or organization.
Is a local or self-hosted model automatically safe?
No. Local processing changes where computation occurs but does not automatically remove data-protection obligations, access risks, retention issues or insecure workflows. The ICO expressly notes that local processing is not necessarily outside data-protection law. Evaluate the device, model files, logs, users, backups, purpose and jurisdiction.
What is the safest default when the policy is unclear?
Do not enter the data. Ask the responsible security, privacy, legal or data-protection team which approved service and information category apply, and use a fictional or redacted example until you receive an answer.
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