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Use AI at work only through an employer-approved service, follow your organization’s data-classification rules, and confirm how that exact account handles prompts and files before entering work information. Then provide the minimum data needed, remove identifying details where possible, and have a human review every output. If a tool, data category, contract restriction, or privacy question is not clearly covered, stop and ask security, privacy, legal, or the data owner.
The safe default: approved tool, approved data, minimum necessary
There is no universal “private AI” switch that makes workplace disclosure safe. Two services with similar names can have different retention periods, model-improvement practices, administrator access, encryption, contracts, and account controls. The same service can also behave differently for a consumer account, an organization-managed account, an API deployment, or a separately configured workspace.
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Start with three checks:
- Tool: Is this exact service and account on your employer’s approved list?
- Data: Does policy permit the classification of information you plan to enter?
- Purpose: Is using an AI system necessary for this particular task?
If any answer is unknown, use a public or synthetic example instead of the real material and ask the responsible internal team. Do not treat a paid plan, a “business” label, an opt-out button, or a vendor’s general privacy statement as proof that your organization’s information is covered.
Check policy and classification before you paste anything
Find the employer’s approved-tool list
Look in the security handbook, acceptable-use policy, intranet software catalogue, procurement portal, or help-desk documentation. Confirm the product name, account type, login method, and whether browser use, mobile use, plug-ins, extensions, APIs, and file uploads are all covered. An approved application may still be prohibited through an unmanaged personal account or an unapproved extension.
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Approval is usually conditional. A policy might allow public information in a general chatbot, permit internal material only in a managed workspace, and prohibit regulated records or source code altogether. Read the rule for the data class, not just the tool name.
Classify the material you want to use
Use your organization’s labels, such as public, internal, confidential, restricted, or regulated. Typical confidential material includes unpublished financial results, customer lists, pricing, deal terms, product road maps, credentials, private keys, source code, security findings, employee records, health information, and documents received under a nondisclosure agreement. A document can contain several classes; the most sensitive element determines how it should be handled.
Classification is about the content, not the format. A screenshot, pasted paragraph, spreadsheet cell, audio transcript, image, or prompt can carry the same obligations as the original file. Generated summaries and copied responses become part of the information-handling process too.
Check contractual and legal constraints
Client contracts, nondisclosure agreements, employment duties, export controls, professional rules, and local privacy law may impose requirements that an internal AI policy does not repeat. A customer’s data may be confidential even when your own classification system calls it “internal.” When obligations conflict or are unclear, ask the data owner or legal contact before processing it.
Verify what the specific account does with prompts and uploads
Read the current terms, privacy notice, security documentation, and organization agreement that apply to your logged-in account. Record the answer to each question below; if documentation is vague, treat the answer as unknown rather than assuming the most favorable interpretation.
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| Question | What to verify | Why it matters |
|---|---|---|
| Retention | How long prompts, files, outputs, logs, backups, and deleted items are kept; whether retention differs by account or feature | Information can remain accessible after you close a chat or delete a document |
| Model improvement | Whether customer content is used to train, fine-tune, evaluate, or otherwise improve models; whether an opt-out is available and effective for this account | A “no training” statement may apply only to a particular product or setting |
| Human and administrator access | Who can view content for support, abuse review, security, legal requests, or administration, and what audit controls exist | Confidentiality depends on people and systems with access, not only the model |
| Subprocessors and transfers | Which providers process the data, where processing occurs, and what notice or contract controls apply | Location and onward processing can trigger contractual or legal duties |
| Security controls | Encryption, single sign-on, multifactor authentication, role-based access, logging, deletion controls, and tenant separation | A model policy cannot compensate for a poorly managed account |
| Confidentiality commitments | What the provider promises about customer content, incident notification, and use outside the requested service | Promises in a marketing page may not be the commitments in your organization’s contract |
Save the applicable version or link in your organization’s approved-tool record, because terms and defaults can change. Ask procurement, security, or privacy staff to interpret contract language; do not make a legal conclusion from a settings screen.
Minimize and de-identify every prompt
Data minimization means sending only what the task requires. Before submitting, separate the useful facts from details that merely make the example look realistic.
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Remove direct identifiers
- Replace names with roles such as customer A or employee 17.
- Remove email addresses, phone numbers, postal addresses, account numbers, ticket IDs, and government identifiers.
- Delete passwords, access tokens, private keys, recovery codes, and connection strings. Never use an AI chat as a secret-storage location.
Reduce indirect identification
Several harmless-looking details can identify a person or deal when combined. Generalize an exact date to a month, an unusual job title to a department, a small location to a region, or a precise transaction value to a range. Remove rare medical, employment, customer, or incident details unless they are essential to the answer. Test whether a colleague who knows the project could reconstruct the person from the remaining context.
Use synthetic or public substitutes
For writing, formatting, brainstorming, or code-pattern questions, create fictional names, values, schemas, and sample records. Replace a real contract with a public template. Replace production logs with a short, redacted excerpt that preserves the error pattern. If the tool cannot solve the task without real confidential content, that is a signal to use an approved specialist workflow or not use AI.
Keep prompts narrow
Ask for one transformation at a time. Instead of uploading an entire customer file and asking for “insights,” provide an approved aggregate such as counts by month and ask for a chart description. Instead of pasting a whole repository, include the smallest function and a fabricated input that demonstrates the bug. Less context reduces exposure and makes review easier.
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A step-by-step workflow for everyday use
- Define the outcome. Write down what you need: a plain-language explanation, a draft outline, a code review of a non-sensitive snippet, or a classification of public text. If the outcome can be produced without confidential data, do not introduce it.
- Identify the data involved. List every prompt, attachment, image, transcript, spreadsheet, and connected source. Mark personal, client, regulated, secret, and contract-protected elements.
- Check authorization. Confirm the tool, account, feature, and data class are allowed. Check whether a plug-in, browser extension, connector, or API sends content to an additional service.
- Verify handling terms. Confirm retention, training or improvement use, access, deletion, transfers, and security controls for that account. Record exceptions and expiration dates for temporary approvals.
- Prepare a minimized copy. Remove identifiers and secrets, aggregate values, shorten the excerpt, and substitute synthetic examples. Keep the original in the approved repository rather than attaching it to the chat.
- Submit the smallest useful request. State the task, constraints, desired format, and assumptions. Do not include background that the model does not need.
- Inspect the response. Check for invented facts, copied confidential details, personal information, unsafe code, hidden instructions in source material, and conclusions that exceed the evidence.
- Handle the output according to its classification. A response that contains or reveals internal information is not public merely because an AI generated it. Store, share, and delete it under the same or stricter rules as the input.
- Document and report. For sensitive workflows, record the tool, account, purpose, data category, approval, and deletion action. Report suspected exposure or an unexpected retention or access behavior through the incident channel immediately.
Understand the privacy risks AI controls cannot eliminate
Disclosure and leakage
A prompt or uploaded file can be exposed through account compromise, an overly broad administrator role, a connected application, a support process, a legal request, a misconfigured storage system, or an error in a provider’s isolation controls. The Federal Trade Commission warned in January 2024 that customers may reveal “sensitive or confidential information when using a company’s models, such as internal documents and even their own users’ data.” It also said providers can be liable when they fail to honor commitments about customer data, including promises not to use it to train or update models.
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Models may retain or reproduce portions of data encountered during training, fine-tuning, evaluation, or operation. The National Institute of Standards and Technology’s Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile (NIST AI 600-1, 2024) identifies leakage and memorization as privacy risks. A setting that suppresses one use of prompts does not prove that every other path is impossible.
Inference
An output can reveal a sensitive fact without quoting the input. Combining several attributes may allow a model or a reader to infer a person’s health, employment status, identity, or business strategy. Removing a name is not enough when the remaining combination is unique.
Personal data in prompts and outputs
The UK Information Commissioner’s Office explains that personal information can appear in user queries, training or fine-tuning data, and model outputs, and that data-protection rights can apply in those contexts. Treat a prompt containing an identifiable person’s information as processing personal data, even if the question is only “summarize this.”
Review outputs before anyone relies on or shares them
Confidentiality is not the only risk. A model can fabricate a citation, alter a number, expose text from the prompt, or produce code with a security flaw. Before forwarding an answer:
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- Compare names, dates, amounts, clauses, and technical parameters with the source of truth.
- Remove any personal or confidential detail that is not necessary for the recipient.
- Check whether the output accidentally combines information from different clients, projects, or environments.
- Run generated code through your normal review, testing, dependency, and secret-scanning processes.
- Label drafts and AI assistance where your policy or contract requires disclosure.
Do not paste the model’s response into a public ticket, presentation, repository, or customer message until it has the classification and approval appropriate to its contents.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.When not to use a general AI tool
Decline or escalate the task when any of these conditions apply:
- The tool or account is not approved, or you cannot establish its retention and access terms.
- The material contains credentials, private keys, unredacted identity records, protected health information, payment data, export-controlled information, or high-impact employment decisions.
- A client or partner contract restricts automated processing, cross-border transfers, or subcontractors.
- The answer would determine a person’s eligibility, employment status, credit, insurance, safety, or access to essential services without a reviewed governance process.
- You would need to upload an entire confidential corpus when a small redacted sample would not work.
Use the organization’s approved internal system, a purpose-built processing pipeline, or a human specialist instead. A privacy accessory, generic “secure” label, or personal subscription cannot authorize disclosure.
Escalate uncertainty instead of guessing
Contact security for access, configuration, logging, incident response, and technical controls. Contact privacy for personal-data purposes, minimization, rights requests, retention, and transfers. Contact legal for contracts, regulated information, nondisclosure duties, and jurisdiction-specific obligations. Contact the data owner when you are unsure whether a document may be shared. Include the proposed task, data category, service and account, intended recipients, retention requirement, and deadline; do not send the sensitive file merely to ask whether it is safe.
Keep a written decision for recurring workflows. An approval should identify the permitted tool and account, allowed data classes, purpose, retention and deletion settings, reviewer, and conditions that would trigger reapproval. Recheck it when the service, contract, model, connector, or organization policy changes.
What regulators and standards emphasize
Federal Trade Commission
The FTC’s January 2024 guidance, “AI Companies: Uphold Your Privacy and Confidentiality Commitments,” focuses on providers’ promises to customers. Its warning is practical for employees: customers may put internal documents and their users’ data into model services, so a provider must honor commitments about confidentiality and model training or improvement. Your employer still has to choose an appropriate service and configure it correctly.
NIST
NIST AI 600-1 (2024) treats leakage, memorization, and inference as generative-AI privacy risks. It also connects privacy protection to the system’s design, deployment, data, and governance rather than to a single product setting.
UK Information Commissioner’s Office
ICO guidance states that data-protection rights can apply to personal data in prompts, model-development data, and outputs. Its security and minimization guidance says risk depends on the AI system and deployment, the organization’s risk-management maturity, and the nature and purpose of processing. Its purpose-limitation work recommends establishing a specified, explicit, legitimate purpose and considering each stage of the generative-AI lifecycle separately.
These materials do not create one legal rule for every employee or country. Follow the law, contracts, and policy that apply to your organization and the people whose data you handle.
Common situations and the safer response
| Situation | Safer approach |
|---|---|
| “Summarize this customer complaint” | Remove names, contact details, order numbers, and distinctive facts; use an approved account and retain only the reviewed summary in the authorized case system. |
| “Fix this production error” | Reproduce the error with synthetic values and a minimal code excerpt; strip tokens, hostnames, customer data, and proprietary algorithms. |
| “Draft a response to an acquisition partner” | Use a public or approved template and insert deal-specific terms only in a contractually authorized environment after legal approval. |
| “Analyze employee survey comments” | Confirm the purpose and lawful basis, aggregate or anonymize comments where possible, and use a privacy-approved workflow rather than a personal chatbot. |
| “Translate a confidential document” | Use the organization’s approved translation or AI service after verifying retention, human access, transfers, and deletion; otherwise use a qualified internal or contracted translator. |
A short pre-send checklist
- Is this exact service, account, feature, and connector approved?
- What is the highest classification in the prompt or attachment?
- Can the task be completed with public, synthetic, aggregated, or redacted data?
- Have names, identifiers, secrets, rare details, and unnecessary context been removed?
- Do current terms state how prompts, files, outputs, logs, and backups are retained and used?
- Who can access the content, and where can it be processed or transferred?
- What will happen to the output, and who must review it before sharing?
- If any answer is uncertain, have you stopped and asked the right internal contact?
The practical rule is simple: approved service, approved account, minimum necessary information, verified handling terms, and human review. That approach reduces exposure without pretending that any AI tool or subscription removes the duties imposed by your employer, your contracts, or applicable privacy law.
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