1. Recognize What You Are Sharing
An AI assistant can only help with the information available to it, but that information may extend beyond the sentence you type. Depending on the product and enabled features, it may include uploaded files, conversation history, saved memories, retrieved documents, and information from connected services. Before starting a task, identify what the assistant will receive and whether you have permission to share it.
Sensitive data includes more than passwords or payment details. Customer records, employee information, private messages, unpublished research, internal pricing, and confidential source code can all require protection. Several ordinary details can also become identifying when combined. A job title, office location, and description of an unusual incident might reveal a person even when their name is removed.
Use a simple sharing rule: public information is usually the lowest-risk input; internal information needs an approved environment; highly restricted information requires explicit authorization and appropriate controls. Follow your organization's classification policy where one exists. If you are unsure whether a document belongs in an assistant, pause rather than treating the chat box as a private notebook.
2. Give the Assistant Less Data
Data minimization is often the most effective first step. Share only what the task actually needs. To improve the wording of a customer email, the assistant usually does not need the customer's real name, account number, address, or complete purchase history. Replace those details with clear placeholders such as CUSTOMER_A and ORDER_REFERENCE, then restore the correct values outside the AI tool.
For analysis or troubleshooting, consider synthetic examples, short excerpts, or aggregated figures. Instead of uploading an entire employee spreadsheet to get help with a formula, provide invented rows that preserve the relevant column structure. When debugging software, remove credentials, access tokens, private endpoints, and unnecessary customer data from code and logs before submitting them.
Redaction requires more care than hiding visible text. Documents may contain comments, tracked changes, hidden spreadsheet tabs, or metadata. Use a properly sanitized copy and verify that removed content cannot be recovered by copying or extracting text. Replacing names alone does not guarantee anonymity: distinctive events and combinations of attributes may still identify someone.
3. Check the Product's Actual Privacy Controls
Do not assume that every AI assistant, account type, or deployment handles data the same way. Review the service's documentation and applicable terms for how prompts, files, and responses are processed. Key questions include whether content may be used to improve models, how long it is retained, who can access it, and whether external tools receive it.
Treat training, storage, and deletion as separate issues. Disabling model training does not necessarily mean that conversations are never stored. Deleting a visible chat may not immediately remove every retained copy. Temporary-chat features can also have exceptions. Check the scope of each control rather than relying on labels such as private, secure, or business.
For workplace tasks, use the environment your organization has approved rather than moving information into a personal account. Enable strong authentication and review saved memory and conversation-sharing settings. A locally running model can reduce exposure to an external model provider, but local files, logs, backups, telemetry, and enabled integrations still need protection.
4. Limit Connected Tools and Automated Actions
Connecting an assistant to email, cloud storage, calendars, or business systems changes the risk. It may be able to retrieve information you never paste into a prompt. Grant only the access needed for a specific task, prefer read-only permissions where practical, and avoid broad access to an entire workspace when a restricted folder will do.
Connected assistants can also encounter prompt injection: instructions hidden in documents, webpages, or messages that attempt to redirect their behavior. For example, a retrieved document might tell an assistant to send private information elsewhere. Treat retrieved content as untrusted material, not as authority to change permissions or disclose data.
Use technical restrictions and approval steps rather than relying only on a prompt that says not to reveal secrets. Where supported, require confirmation before sending messages, sharing files, or changing records. Review the destination and content yourself. Disconnect integrations when they are no longer needed, and remember that external tools may have their own retention practices.
5. Review Outputs and Prepare for Mistakes
Sensitive information can leave through the answer as well as the input. An assistant may repeat private details in a summary, generated email, or downloadable report. Before sharing an output, check it for personal information, internal notes, confidential figures, and links that could expose source material. Verify the audience and permissions on any shared conversation.
Build a repeatable routine: classify the information, minimize the input, confirm the environment, limit connected access, and inspect the result. Teams can make this easier with approved tools, sanitized examples, and clear escalation routes. Practical guidance is more useful than a vague instruction to be careful.
If sensitive information is submitted accidentally, stop further sharing and follow your organization's incident-reporting process. Record what was exposed, where, and which settings applied. Revoke exposed credentials promptly through the appropriate owner, and use available deletion controls without assuming they guarantee complete erasure. A prepared response helps contain mistakes before they spread.
