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Do not paste personal, confidential, regulated or secret information into a public AI assistant unless your organisation has approved the use and the data has been minimised and assessed. That includes medical records, payment details, passwords, direct identifiers and confidential documents. If an approved task can be done with redacted excerpts or synthetic examples, use those instead. The right safeguards also depend on whether you are using a consumer chat account, a work workspace, an API or a cloud-hosted deployment.
What counts as sensitive data in an AI chat?
Sensitive data is not limited to information formally labelled “confidential.” It includes information that could expose a person, organisation, account or system if copied, retained, reviewed or reproduced in an answer. Anthropic’s consumer guidance specifically cautions users about financial information such as Social Security numbers, credit-card and bank-account details; health records or medical information; passwords and private login credentials; and confidential business or personal documents.
Use a deny-by-default rule for those categories in consumer chats. Extend it to regulated records, API keys, access tokens, private keys, customer case details, unannounced business plans and any other material your employer or contract restricts. A prompt can also reveal sensitive facts indirectly: a seemingly ordinary excerpt may identify someone when combined with a rare role, location, event or date.
- Personal and regulated information: names linked to account records, addresses, dates of birth, medical details, government identifiers and payment data.
- Credentials and secrets: passwords, recovery codes, API keys, tokens, private URLs containing credentials and infrastructure secrets.
- Confidential work material: source documents, customer communications, contracts, personnel records, unreleased financials and internal incident reports.
The Office of the Australian Information Commissioner (OAIC) says privacy obligations apply both to personal information entered into an AI system and to personal information in its outputs. Its guidance, updated 17 January 2025, recommends that organisations do not enter personal information—particularly sensitive information—into publicly available generative-AI tools because of significant and complex privacy risks. That is Australian guidance; organisations elsewhere must also check the laws and obligations that apply in their jurisdiction.
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Can I paste a work document into Claude?
Only if your organisation has approved that specific product and use, and you have minimised the material first. For a consumer chat, the safer default is not to paste confidential work documents or personal information. A paid subscription alone does not establish that a particular use is authorised or that the account has the contractual, retention and access controls your organisation requires.
Before using a document, check your employer’s AI policy and the document’s classification, contractual terms and applicable privacy rules. Approval should cover the precise surface—such as consumer Claude, Claude for Work, an API integration, Claude Code or a cloud-hosted deployment—not merely “AI” in general. If there is no clear approval, ask the document owner, privacy officer or security team rather than assuming the task is low risk.
When an approved task can be done without original values, replace identifiers with consistent placeholders such as [CUSTOMER_NAME], [ACCOUNT_ID] or [CONTRACT_VALUE]. Remove unnecessary surrounding text and unique case details as well. If you need to restore the real values later, keep the mapping in an approved system outside the assistant.
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How do Claude’s different product surfaces handle data?
Claude does not have one universal data practice: consumer accounts, work products and API or cloud deployments can have different terms, controls and retention behavior. Confirm the exact surface and feature in use, then check its current documentation and contract rather than applying a statement about one Claude product to another.
| Surface | What the cited documentation says | What to verify before use |
|---|---|---|
| Consumer Claude Free, Pro or Max | These accounts are governed by consumer privacy settings and product retention practices. Anthropic says data is encrypted in transit and at rest, employee access is limited by default, and controlled access can occur for feedback or trust-and-safety enforcement. | Check the current privacy settings and retention terms for the account and feature. These security measures reduce risk but do not make it advisable to paste highly sensitive information. |
| Claude for Work | Anthropic describes the customer organisation as controller and Anthropic as processor. The organisation controls membership and can access or export user-submitted conversation history. Under the commercial terms described by Anthropic, it processes data to provide the service and does not use it to train generative models. | Confirm the current contract and data processing agreement (DPA), region, subprocessors, administrator access, export rights, deletion process and incident obligations. |
| Claude API | Anthropic documents zero-data-retention (ZDR) and HIPAA-ready arrangements for eligible customers and features. Retained data is not used for model training without express permission, but storage and retention vary by feature; some functions require retention. | Check feature eligibility and the applicable retention terms. Do not assume an organisation-wide setting covers every endpoint or feature. |
| Claude through Amazon Bedrock or Google Cloud | Anthropic says cloud-hosted access uses the relevant cloud provider’s controls. | Assess the provider’s role and controls alongside Anthropic’s terms, including where data is stored and which party handles access, logging and deletion. |
“Controller” and “processor” describe different responsibilities under privacy frameworks: broadly, the controller determines why and how personal information is processed, while the processor handles it on the controller’s behalf. The contractual wording and applicable law govern the actual arrangement; do not treat a product label as a substitute for reviewing the agreement.
Does Claude train on my data, and how long are chats retained?
The answer depends on the product surface, settings, feature and applicable terms. Anthropic’s commercial documentation says Claude for Work customer data is not used to train generative models under the described commercial terms, and says retained data is not used for training without express permission. Do not infer that this describes every consumer account or every integration; verify the current terms for the account you are using.
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Anthropic’s commercial retention guidance says API inputs and outputs are normally deleted within 30 days. That is not a universal deletion deadline for every Claude interaction: saved conversations in Claude products, Files and API features, legal requirements, policy enforcement, feedback and other settings can result in different periods. For designated Covered Models, Anthropic also documents a 30-day retention period for prompts and outputs for safety work, with controlled human review in limited cases; flagged or legally required data may be kept longer.
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ZDR or a HIPAA-ready arrangement can reduce routine retention for eligible customers and features, but neither should be read as a promise that data can never be retained or reviewed. Anthropic describes exceptions for safety, legal obligations and particular product features. Deleting a conversation from chat history is not necessarily the same as immediately removing every backend copy or compliance record.
These retention periods and product terms can change. Before handling sensitive information, check the current Anthropic documentation for the exact feature and your organisation’s agreement, including any retention exceptions. For API or cloud use, examine each enabled tool or endpoint rather than assuming all requests follow the same rule.
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How can I redact or pseudonymise data before using an assistant?
Reduce the prompt to the smallest excerpt and the exact fields needed to get the result. Replace identifying values consistently, remove irrelevant context and keep any re-identification key outside the assistant in an authorised location.
- Copy only what the task requires. For a summary, provide the relevant passage rather than an entire file. For a formula or code explanation, remove real customer values and unrelated comments.
- Remove direct identifiers. Delete names, account numbers, addresses, dates of birth, phone numbers, email addresses and other fields that directly identify a person.
- Replace remaining references consistently. Use placeholders such as
[CUSTOMER_A]or[ACCOUNT_ID]so the assistant can follow the example without receiving real values. Avoid using a real name as a supposedly harmless stand-in. - Check indirect identifiers and secrets. Look for unusual job titles, rare events, exact dates, unique case numbers, embedded credentials, API tokens and URLs that contain credentials. A combination of details can identify someone even if a name is removed.
- Keep the mapping separate. If a result must later be reconnected to a real person or record, store the placeholder-to-identity mapping outside the AI service in an approved system.
- Review the prompt before sending. Check pasted text, attachments and tool inputs—not just the visible question—for leftover names, metadata, comments or credentials.
Redaction and pseudonymisation reduce exposure, but they are not the same as proving information is anonymous. If the remaining details still allow a person or account to be recognised, handle the material according to its original classification.
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Approval should be based on the actual workflow and the exact terms and controls—not a marketing claim or a general assurance that a service is secure. OAIC recommends due diligence, a privacy-impact assessment, human oversight, transparent policies and ongoing monitoring. Its guidance describes AI due diligence as an ongoing responsibility rather than a one-time sign-off.
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- Purpose and necessity: document the task, why an AI assistant is needed and whether the same result can be achieved with less data or without personal information.
- Product and feature scope: identify the account type, workspace, model, integration, endpoint and enabled features that will receive or process data.
- Contract and data roles: review the contract and DPA, controller/processor responsibilities, training-use terms, subprocessors and permitted purposes.
- Retention and deletion: identify default and configurable retention, ZDR or HIPAA eligibility where relevant, feature-specific exceptions, deletion procedures and how deletion is verified.
- Location and transfers: establish storage and processing regions, regional transfer terms and the role of any cloud provider.
- Access and security: check administrator visibility and export rights, encryption and key management, authentication, least-privilege controls and available audit logs.
- Incident handling: establish notification commitments, response responsibilities and who to contact if information is exposed.
- Human review and monitoring: define who checks prompts and outputs, how approved use is logged, how staff are trained and how the workflow is reassessed.
When comparing Claude with another assistant, ask for current contractual and technical evidence for each of these areas. A vendor’s statement about training or encryption does not answer questions about administrator access, retention exceptions, deletion verification or regional transfers.
What workflow should staff follow before sending a prompt?
A short pre-send check helps convert policy into a repeatable habit. It should happen before opening a chat or attaching a file, not after a potentially sensitive prompt has already been submitted.
- Classify the material: identify whether it is public, internal, confidential, personal, regulated, a credential or a secret.
- Apply the deny-by-default rule: do not send secrets, passwords, payment details, medical records, direct identifiers or confidential source documents to consumer chat.
- Minimise and redact: retain only the smallest excerpt and fields required, and replace identifying values with placeholders.
- Verify the surface: confirm the product, plan, workspace and feature approved for this information and task.
- Check the controls: ensure the approved retention, access, authentication, logging and contractual settings apply to the exact workflow.
- Review the result: inspect the output for reproduced personal information, inferred sensitive traits or confidential text before sharing it.
- Reassess over time: review training, monitoring, product changes and fitness for purpose throughout the lifecycle, as OAIC recommends.
What are the most common mistakes?
- Assuming a paid plan is automatically approved: an individual subscription does not by itself establish organisational approval, contractual protections or the right to submit work data.
- Assuming encryption means no access or retention: encryption in transit and at rest is one protection, not an answer to all questions about processing, review, exports or deletion.
- Using one product’s terms for another: consumer chat, a work workspace, an API endpoint and a cloud-hosted integration can have different controls and responsibilities.
- Removing only names: unique dates, locations, roles, case details and embedded tokens may still expose the subject or system.
- Treating deletion as a complete erasure guarantee: chat-history deletion may not immediately remove all backend or compliance records.
- Trusting an answer without review: an assistant can reproduce sensitive prompt content or infer information; outputs need the same disclosure discipline as inputs.
What is the safest practical rule?
Use public AI assistants with public or deliberately sanitised information. Treat personal, confidential, regulated and secret data as prohibited unless an authorised workflow has assessed the use, minimised the data and verified the product-specific contract and controls. For organisations, document the decision and revisit it when the product, feature, terms or workflow changes.
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Frequently Asked Questions
Can an AI assistant identify someone after I remove their name?
Yes. A rare job title combined with a location, event date or unusual case detail can point to one person even without a name. Treat a prompt as identifying if a knowledgeable reader could reasonably connect those details to someone.
What should I do if I accidentally paste a password or API key into a chat?
Treat the credential as exposed: revoke or rotate it through the system that issued it, then notify your security team and follow your organisation’s incident process. Deleting the message should not replace credential rotation or incident reporting.
Can I use an AI assistant with information that is already public?
Often that is lower risk, but check whether the prompt combines public facts with private context, credentials or restricted material. Also follow your organisation’s rules for approved tools and review the answer before publishing it.
Quick Recap
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