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Before sending a prompt to a third-party AI service, classify the data, remove everything the task does not need, replace identifiers and secrets with consistent placeholders, generalize details that could identify someone, and verify the provider’s current data practices. Authorization is necessary for sensitive or confidential information, but it does not by itself prevent logging, retention, human review, or reuse.
The safest default is to use synthetic examples or a local processing path whenever real personal, customer, health, financial, legal, credential, or proprietary data is not essential to the answer.
Why prompt sanitization matters
A prompt can disclose more than the text you intentionally paste. Attachments, comments, filenames, document properties, copied email headers, code comments, and conversation history may carry the same sensitive details. Removing a name is also not enough: an exact date, unusual job, rare medical event, precise location, amount, and relationship can identify a person or confidential project when combined.
The Federal Trade Commission warned in January 2024 that customers may reveal sensitive or confidential information to company models, including internal documents and their own users’ data. The Office of the Privacy Commissioner of Canada recommends using anonymized or de-identified information in generative-AI prompts where possible and reasonable. It also says personal information, especially sensitive or confidential information, should be entered only when authorized. These recommendations address different risks: de-identification reduces exposure, while authorization establishes whether the use is permitted.
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Use this sanitization workflow
1. Classify the content before editing
Read the entire prompt, pasted material, attachments, and conversation context. Mark each item that falls into one or more of these categories:
- Personal data such as names, email addresses, phone numbers, postal addresses, account numbers, customer IDs, precise locations, photographs, or device identifiers.
- Sensitive personal data, including health information, financial records, government identifiers, biometric information, or details about children and vulnerable people.
- Confidential business information such as internal plans, customer lists, pricing, deals, incident reports, source code, product road maps, and information covered by a nondisclosure agreement.
- Credentials and security material, including passwords, private keys, API keys, access tokens, session cookies, recovery codes, database connection strings, and unredacted security logs.
- Legal material, regulated records, or information subject to a contract, professional duty, litigation hold, or other confidentiality obligation.
Classification determines the approval and handling rules. It does not mean that every marked item must be deleted; it means you must justify any item that remains.
2. Minimize to the fields the task actually needs
Write the task in one sentence, then retain only the data needed to complete it. A request to summarize a customer complaint usually needs the issue, product, chronology, and desired output—not the customer’s name, email address, account number, or exact address. A request to identify a trend may need age bands and regions rather than exact ages and towns.
Delete unrelated paragraphs, quoted email chains, signatures, tracking links, hidden spreadsheet columns, document comments, and copied metadata. Remove exact dates when the sequence or approximate interval is sufficient. Replace precise locations with a region, and account numbers with a stable reference token. Minimize attachments as well: send the relevant excerpt instead of an entire mailbox export or contract when the full file is unnecessary.
3. Replace identifiers with stable placeholders
Use tokens that reveal a role without revealing the value. Keep the same token every time that entity appears so the model can reason about relationships.
Original (fictional example): “On 14 March 2026, Jane Doe at jane.doe@example.com told our Berlin office that account 4839201 was charged €7,420 after the contract signed with Northstar GmbH.”Sanitized: “[DATE_1], [CLIENT_A] told [REGION_1] that [ACCOUNT_1] was charged [AMOUNT_1] after the contract was signed with [VENDOR_A].”Choose tokens that do not accidentally encode the answer. [CLIENT_A], [DATE_1], [PRODUCT_X], and [AMOUNT_1] are clearer than a partially masked name or a token containing an account-number fragment. Preserve relationships: if two messages concern the same client, use the same placeholder in both. If the task depends on ordering, use consistent date tokens such as [DATE_1] and [DATE_2] or state the interval directly.
Check every representation of the original value. A name left in a filename, a comment, a spreadsheet tab, an image layer, or document metadata defeats a text-only redaction.
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4. Generalize combinations that could re-identify someone
Quasi-identifiers become identifying when they occur together. Broaden an exact age to an age band, an exact address to a region, a rare job title to a broader category, and an unusual event to a less specific description when the task permits it. For example, “the only 47-year-old pediatric cardiology director at a named clinic who attended a specific conference” may identify one person even after the name is removed.
Generalization must preserve the analytical property the model needs. If the question concerns eligibility at age 65, use a meaningful band around that threshold or a synthetic age that preserves the rule being tested. If a geographic rule matters, retain the relevant region while removing the street, building, or small community. If an uncommon combination is itself the subject of the analysis, obtain specialist advice before sharing it rather than assuming a name-free record is anonymous.
5. Remove secrets completely; do not merely mask them
Never put a password, private key, access token, session cookie, recovery code, or live connection string in a prompt. For debugging, replace it with a dummy value that has the same general format, such as sk-test-placeholder or EXAMPLE_TOKEN. Remove secrets from stack traces, environment dumps, screenshots, shell history, configuration files, and code comments.
If a real credential has already been exposed to an AI service, treat it as compromised: revoke or rotate it, invalidate active sessions where appropriate, inspect access logs, and document the incident under your organization’s security process. Redaction after submission does not undo a copy that may already have been logged or retained.
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6. Test whether the remaining details are still identifiable
Perform a residual-identifiability review from the perspective of someone who has outside knowledge. Ask:
- Could a colleague identify the person, customer, project, deal, or patient from the remaining dates, amounts, roles, locations, and events?
- Does the combination describe a unique incident, even though direct identifiers are gone?
- Would a public search, an internal directory, or the provider’s other data make reconstruction easy?
- Are quoted phrases, file names, ticket numbers, or unusual spelling still searchable?
If the answer may be yes, remove another detail, widen a category, alter an amount while preserving the relevant relationship, or replace the record with a synthetic example. Document why the remaining data is necessary.
7. Validate usefulness without real data
Run the sanitized prompt with synthetic or previously approved sample data first. A local model or an approved test environment can show whether the instructions, structure, and placeholders still produce a useful result. Check that the model can distinguish entities, follow chronology, calculate with the required formats, and return the requested output.
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Validation should test the task, not recreate the confidential record. For a code-debugging request, use dummy credentials and a reduced code sample. For a classification task, preserve the labels and features that drive the decision while replacing names and free-text narratives. If sanitization removes information the model needs, redesign the task or use an approved controlled environment rather than restoring the original values by default.
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8. Review the provider before submission
Provider controls differ by product, account tier, geography, and configuration. Check the current terms and record the date and exact account or workspace you are using. Verify all of the following:
- Whether prompts, files, generated outputs, and conversation history are logged.
- How long each category is retained, what starts the retention period, and whether you can delete it.
- Whether content is used to train or improve models, and whether an opt-out applies to your exact account.
- Whether employees, contractors, or reviewers can access content and under what circumstances.
- Which subprocessors handle the data and what contractual restrictions apply to them.
- Encryption in transit and at rest, tenant isolation, access controls, and audit-log availability.
- Where processing and storage occur, including cross-border transfers and regional-processing options.
- How deletion, access, correction, export, and legal-hold requests work in practice.
- Whether the controls apply to the consumer product, an API, an enterprise workspace, or only a specific edition.
Enterprise plans may provide encryption, tenant isolation, contractual processing terms, and no-training commitments, but those protections are not automatic for every product or geography. Confirm the exact terms rather than relying on a plan name or a sales statement.
9. Record authorization and clean up working copies
For sensitive or confidential data, record the approved purpose, data categories, provider, account tier, retention setting, processing geography, and date of the terms you reviewed. Confirm that the person or team submitting the prompt is authorized and that the use has a lawful and documented purpose.
After the task, delete unsanitized scratch files, temporary exports, local caches, and intermediate redaction copies when they are no longer required. Keep only the records needed for audit, legal, security, or operational purposes. Deletion from your computer does not necessarily delete provider-side copies, so follow the provider’s documented deletion process as well.
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Sanitization is not the same as deleting every detail. The objective is to preserve structure while removing identity and unnecessary exposure.
Keep relationships and order
Use the same placeholder for the same entity, distinguish different entities, and preserve event order. Replace “Alex reported the issue, then Alex approved the refund” with “[EMPLOYEE_A] reported the issue, then [EMPLOYEE_A] approved the refund,” not two unrelated tokens. If timing matters, retain relative intervals or ordered date tokens.
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Keep formats only when they affect the task
For a date-parsing test, preserve a date format with fictional values. For a financial calculation, preserve currency and decimal conventions while substituting amounts. For a database query, preserve table and field relationships but rename schemas, customers, and values. Do not preserve a format merely because it is present; every retained feature should serve the stated task.
Use synthetic records for demonstrations
A synthetic record should have plausible structure without copying a real person’s unusual combination of traits. Change names, identifiers, dates, locations, and amounts together so that a real record cannot be reconstructed by matching one unchanged field. State in the prompt that the values are synthetic when that context helps the model interpret them.
Why a no-training promise is not enough
“Not used for training” addresses one possible use of submitted content. It does not answer whether the prompt is logged, how long it is stored, whether people can review it, which subprocessors receive it, where it is processed, whether backups persist, or whether deletion is available.
Microsoft’s work and school Copilot documentation says prompts, Bing search queries triggered by prompts, and responses are logged even though qualifying accounts receive enterprise data protection. The practical lesson is to evaluate logging and retention separately from training use, and to verify that the documented behavior applies to the account and product you selected.
Use a provider only after you can explain the complete data path: submission, processing, logging, backups, review, subprocessors, deletion, and geographic transfer. If any required control is unclear, do not send the sensitive value; reduce the data further or use an approved local or private processing path.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Authorization, privacy, and regulatory guidance
Authorization is a gate, not a substitute for minimization. The Office of the Privacy Commissioner of Canada says, “Where possible and reasonable, use anonymized or de-identified information within prompts to a generative AI (artificial intelligence) system rather than personal information.” The same authority says, “Where personal information (and, in particular, sensitive or confidential information) must be entered into a prompt, only do so where authorised.” These statements support two checks: can the task be done without the personal information, and if not, is the proposed use approved?
The Information Commissioner’s Office advises that organizations may need to “remove certain features from the data, or apply privacy enhancing technologies (PETs), before sharing it with another organisation.” In practice, that can mean redaction, aggregation, pseudonymization, access controls, or another technique suited to the data and the risk. Pseudonyms are not automatically anonymous if the organization retains a mapping table or if the remaining details identify the person.
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Contractual confidentiality, professional duties, sector rules, and local privacy law may impose stricter requirements than a provider’s general policy. Health, financial, legal, employment, and children’s data deserve a specialized review. When the task involves regulated records or a cross-border transfer, involve your privacy, legal, security, or compliance team before submission.
Common sanitization failures and fixes
Removing only the name
Failure: The prompt keeps an exact date, rare role, small location, amount, and event. Fix: Review combinations, broaden rare attributes, and test whether an informed colleague could identify the subject.
Using inconsistent placeholders
Failure: The same customer becomes [CLIENT_A], [CUSTOMER_3], and a different token in separate passages, so the model cannot follow the case. Fix: Maintain a replacement map locally and use one stable token per entity. Never send the map with the prompt.
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Failure: The visible code is sanitized but a comment, screenshot, filename, or environment dump contains a live key. Fix: Inspect the complete upload and rotate any credential that was exposed.
Assuming an enterprise label answers every question
Failure: A user assumes enterprise data protection means no logs, no human access, and immediate deletion. Fix: Verify each control, its retention period, geographic scope, and applicability to the exact account tier.
Sending more data after the first answer is weak
Failure: A poor result leads to pasting the complete original record. Fix: Identify which missing feature affected the answer, add only that generalized feature, and retest with synthetic data first.
Quick Recap
A pre-submission checklist
- State the task and the minimum fields it requires.
- Mark personal, sensitive, confidential, legal, health, financial, proprietary, and credential data.
- Delete unrelated text, attachments, metadata, comments, filenames, and conversation history.
- Replace identifiers with stable placeholders and keep the replacement map outside the prompt.
- Generalize exact dates, locations, ages, roles, amounts, and rare combinations where the task allows.
- Remove every secret; use dummy values and rotate any real credential already disclosed.
- Ask whether outside knowledge could still identify a person, customer, project, or deal.
- Test the sanitized prompt with synthetic data or a local model.
- Check logging, retention, training use, human access, subprocessors, encryption, tenant isolation, geography, deletion, and auditability for the exact provider account.
- Confirm authorization, record the purpose and terms date, and delete unnecessary unsanitized copies.
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