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Do not paste company information into an AI model until you know exactly what the data contains, whether that specific service and account are approved, and what the provider may do with your prompt or files. Remove unnecessary details, check retention and training terms, verify security and incident commitments, and escalate any uncertainty to your privacy, security, legal, or records-management owner.
Start with the data, not the AI tool
The same AI feature can be acceptable for a public press release and prohibited for an employee investigation. Your first decision is therefore about the information you plan to submit.
Identify sensitive categories
Review the complete prompt, attachment, pasted conversation, code sample, metadata, and surrounding context. Look for:
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- Customer or employee information received under a privacy notice, contract, or confidentiality obligation.
- Credentials, API keys, access tokens, private certificates, security configurations, or other secrets.
- Trade secrets, source code, product designs, unreleased financial results, acquisition plans, pricing strategy, or other confidential business information.
- Regulated information or data subject to sector-specific rules.
- Information supplied by a customer, partner, or government body with restrictions on disclosure or onward transfer.
Classification labels such as “internal,” “confidential,” or “restricted” are useful starting points, but the underlying content and the agreement governing it control the decision. If a document contains several classifications, treat the submission according to the most restrictive material unless your policy says otherwise.
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Minimize before you submit
Keep only what the model needs to perform the task. For a summary, remove unrelated pages. For a writing task, provide a short description instead of the full contract. Replace real names, account numbers, dates, and unique identifiers with consistent placeholders or synthetic examples. Strip hidden comments, document properties, tracked changes, screenshots, and embedded files that could contain additional information.
Minimization is not merely a privacy preference. It reduces the impact of an incorrect setting, an overly broad access permission, a retention period you misunderstood, or a later security incident. Federal Trade Commission guidance emphasizes taking stock of information, keeping only what is needed, limiting access, and securely disposing of information that is no longer necessary.
Confirm the exact service, product tier, and account
“The AI tool” is not a sufficient description for an approval decision. Record the provider, product name, plan or deployment type, account owner, feature, and date checked. A consumer website, a company workspace, an application-programming interface, and a separately contracted enterprise deployment may have materially different data practices.
Check whether your organization approved this use
Verify that the specific service and account are on your organization’s approved list for the data classification and purpose. Approval for drafting public copy does not automatically authorize processing customer records. Approval for an API may not cover a browser plug-in that sends page content to a different endpoint. A personal account, even when used for company work, may not be governed by the company’s contract or administrative controls.
Confirm who can access the account and whether access is tied to company identity management. Shared logins, unmanaged browser extensions, and personal storage locations make it difficult to remove access when someone changes roles or leaves.
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Check the feature, not just the brand name
File analysis, memory features, voice transcripts, image input, plug-ins, connectors, and agent actions can create different data flows from ordinary text prompts. Identify every enabled feature and connected system. An assistant connected to a document repository may receive more context than the text you deliberately paste.
Read the current data-use and retention terms
Do not infer data handling from a marketing description or from another plan. Read the terms, privacy documentation, security addendum, and administrator settings that apply to the exact product and account. Save the version or date you reviewed because terms and controls can change.
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|---|---|---|
| Are prompts, files, or outputs retained? | Retention increases the period during which the information may be accessible or exposed. | Default period, event that starts deletion, backups, and any exceptions. |
| Are submissions used to train or improve models? | Training or improvement can create a separate use from fulfilling your immediate request. | Whether use is allowed, disabled by default, or controlled by an account setting or contract. |
| Can people review submissions? | Human review may be performed for safety, support, quality, or abuse investigations. | Who may review, under what conditions, and whether confidential data is excluded. |
| Are data or outputs disclosed to other parties? | Vendors, subprocessors, support providers, and connected services may receive information. | Subprocessor list, onward-sharing rules, and notice or objection rights. |
| Can you delete the data? | A delete button may not remove copies from logs, backups, legal holds, or derived systems. | Available deletion controls, timing, scope, and exceptions. |
| Can administrators enforce settings? | Individual choices are unreliable when an organization needs consistent controls. | Workspace defaults, audit logs, disablement options, and permission boundaries. |
The FTC has stressed that AI model-as-a-service companies should honor privacy and confidentiality commitments, including promises about model training and other data use. Treat a promise as something to verify in the applicable terms or contract, not as a reason to skip that review.
Verify security and contractual safeguards
A public privacy statement rarely answers every question a company has about a sensitive disclosure. Ask what written commitments cover the service and retain the answer with your vendor records.
Security controls
Determine how the provider protects data in transit and at rest, separates customer environments, limits internal access, manages privileged accounts, and handles authentication. Check whether administrative access can be restricted to named personnel and whether your organization receives logs or audit evidence suitable for its risk level. Independent assurance may help, but check its scope, date, and exclusions rather than treating a certification logo as proof that every control applies to your use.
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Retention, deletion, and data location
Clarify the retention period for prompts, uploads, outputs, logs, abuse-monitoring records, and backups. Ask whether deletion is immediate, scheduled, or conditional on a support or legal process. Where location or cross-border transfers matter, identify processing regions, transfer mechanisms, and the parties that may receive the data.
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Review the contractual definition of a security incident, the deadline and method for notification, the information supplied during an investigation, and the provider’s cooperation obligations. Identify subprocessors and how new subprocessors are announced. The FTC recommends investigating service providers’ security practices, putting expectations in contracts, and verifying compliance and incident notification rather than relying only on assurances.
Check policy, contracts, and legal restrictions
Your organization’s AI policy is one control, not a universal legal test. Compare the proposed disclosure with:
- Internal data-classification, acceptable-use, records-retention, and information-security policies.
- Customer, partner, employment, nondisclosure, licensing, and government contracts.
- Privacy notices and commitments made to the people whose information you hold.
- Applicable laws, regulations, professional duties, sector requirements, and jurisdictional restrictions.
Ask the owner responsible for the specific question: privacy for personal-data use, security for technical controls and secrets, legal for contracts and regulatory interpretation, or records management for retention and deletion. A general policy or public privacy page may not resolve a particular customer agreement or data transfer.
CMS guidance for its workforce offers a useful caution: If you hesitate to share information with an external expert, do not submit it to generative AI platforms.
That is agency guidance, not a rule that automatically governs every private company, but it is a practical test for recognizing information that needs escalation.
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Use this pre-submission screening process
- Describe the task. State what you want the model to do and what output you need. A precise task usually requires less source data.
- Inventory the input. Include pasted text, attachments, screenshots, links, conversation history, metadata, and data supplied automatically by a connector.
- Classify the content. Mark personal, customer, employee, confidential, regulated, secret, contract-restricted, and public elements.
- Reduce the input. Delete irrelevant sections, redact identifiers and secrets, and substitute synthetic examples where the task still works.
- Identify the exact service. Record provider, product tier, account, feature, connected systems, and review date.
- Confirm authorization. Check the approved-use register and the policy for that classification and purpose. Do not use a personal account as a workaround.
- Read the applicable terms. Verify retention, human access, training or improvement use, sharing, deletion, settings, and data location.
- Verify safeguards. Check the contract or security documentation for access controls, encryption, incident notice, subprocessors, audit evidence, and transfer terms where relevant.
- Document the decision. Save the classification, minimization steps, terms date, settings applied, approver, and any conditions on the use.
- Stop when unresolved. Use an approved alternative, remove more data, or escalate to the responsible privacy, security, legal, or records owner.
Decide whether to proceed, minimize, or escalate
| Situation | Action |
|---|---|
| Public or deliberately non-confidential material; approved service; terms and controls are clear. | Proceed within the approved scope and keep the input limited to the task. |
| Internal information is needed, but identifiers or confidential details are unnecessary. | Redact or replace those details, then confirm the service is approved for the remaining classification. |
| Customer, employee, regulated, secret, or contract-restricted data is involved. | Pause unless the exact service, purpose, authorization, and safeguards expressly cover it. |
| Retention, training use, human review, deletion, subprocessors, or incident terms are unclear. | Do not submit; ask the service owner, privacy, security, or legal owner for a documented answer. |
| The only available route is a personal account or an unapproved plug-in. | Do not use it for company data. Choose an approved channel or escalate the business need. |
Common situations and safer handling
Summarizing a customer document
First remove names, account numbers, signatures, and unrelated pages. Check whether the customer agreement permits the disclosure and whether the approved service’s terms cover the remaining content. If the summary can be produced from a synthetic example or a manually prepared excerpt, use that instead.
Debugging source code
Search for credentials, private keys, proprietary algorithms, customer data, internal hostnames, and comments that reveal architecture. Reproduce the error with a minimal, fabricated example where possible. An approved coding environment may still have separate rules for repositories, extensions, telemetry, and model-improvement use.
Drafting an HR or performance message
Employee details and performance records can be personal and highly sensitive. Remove identifying facts and use a generic scenario unless the organization has expressly approved the service and purpose. Confirm who is authorized to see the output as well as who may see the prompt.
Analyzing support tickets
Tickets often combine personal information, authentication details, order data, and free-form attachments. Filter fields before export, exclude credentials and payment information, and verify that the processing purpose matches customer and internal commitments. A connector that sends an entire ticket system can defeat otherwise careful prompt minimization.
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What to record for repeatable governance
For recurring workflows, create a short submission record or approval template containing:
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- Business purpose and expected output.
- Data categories and maximum permitted classification.
- Approved provider, product tier, account, features, and connected systems.
- Required redaction, retention, access, and deletion settings.
- Terms and security-document review date.
- Named business owner and escalation contacts.
- Conditions that require reapproval, such as a product change, new subprocessor, new jurisdiction, or new data category.
This record does not replace judgment. It makes the decision auditable and prevents a one-time exception from silently becoming an organization-wide practice.
Frequently Asked Questions
Can I paste company data into ChatGPT or another AI tool?
Only when the exact service, account, feature, data type, and business purpose are approved under your organization’s rules and the applicable terms and safeguards are understood. Otherwise, redact the material, use an approved alternative, or ask the responsible owner.
Will the AI provider use my prompts to train its model?
The answer depends on the exact product tier, account settings, and contract. Check current terms for model training or improvement, retention, human review, and related uses; do not assume that one plan has the same treatment as another.
Is it safe to upload a company document to AI?
Safety cannot be determined from the file format or the tool’s name. Classify and minimize the document, confirm authorization, inspect retention and sharing terms, and verify security and incident commitments before uploading.
What should I remove before sending data to an AI model?
Remove anything the task does not require, especially names, account numbers, credentials, API keys, private keys, customer or employee identifiers, confidential attachments, hidden metadata, and unique details that could identify a person or organization.
Who should approve an uncertain AI-data submission?
Route the question to the owner matched to the issue: privacy for personal data, security for controls and secrets, legal for contracts and regulatory questions, or records management for retention and deletion. Your organization may assign these responsibilities differently.
Quick Recap
Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.
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