Use a simple default rule: treat any prompt sent to a public or unapproved AI service as a disclosure outside your company’s controlled environment. Use only the exact AI product, account tier, tenant and configuration your employer has approved. Even then, send the minimum sanitized context needed for the task—never passwords, personal records, confidential contracts, source code or other secrets.
This guide explains what to exclude, how employees can work safely, what IT and security teams should verify, and what to do if information has already been submitted. CMS guidance quoted below applies to CMS staff; it is a concrete government example, not a universal policy for every employer. Follow your organization’s rules and applicable law.
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Why an AI prompt should be treated as a data disclosure
A prompt is not a private note kept on your computer. It is data transmitted to a service, processed by software and potentially stored in prompts, responses, logs, safety systems, connected search, agent memory, plug-ins or retrieval databases. The exposure depends on the exact service and configuration, but an employee often cannot see every system that receives or retains the text.
That is why a company-branded interface is not automatically safe. A business service may have stronger contractual controls than a consumer account, while a different subscription, web-search mode, browser feature or agent may follow separate terms. Microsoft documents this distinction for its AI services: protections and handling practices vary by subscription, and web search queries and agents have their own details. Treat vendor statements as product-specific commitments to verify, not as a blanket permission to paste any company information.
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A practical test for employees is the confidentiality test used in CMS guidance: if you would hesitate to send the material to an outside expert who has no confidentiality agreement or business need to know, do not put it into an unapproved AI tool. This test catches information that may not be labeled “secret” but could still harm a customer, employee, supplier or the company if disclosed.
Information that should stay out of public or unapproved tools
Do not paste the following categories unless your employer has explicitly approved the exact workflow and required protections:
| Category | Recognizable examples | Why it matters |
|---|---|---|
| Credentials and security material | Passwords, API keys, access tokens, private certificates, recovery codes, firewall rules and unpatched vulnerability details | A prompt can become a copy of a secret that must later be rotated, investigated and removed from every system that retained it. |
| Personal and employee information | Names tied to identifiers, addresses, account numbers, identity documents, performance records, disciplinary notes and recruiting files | Personal data can trigger privacy, employment, contractual or breach-notification duties. |
| Health information | Diagnoses, treatment details, disability information, insurance claims and identifiable medical notes | Health data often has stricter legal and contractual handling requirements than ordinary business text. |
| Customer and user records | Support transcripts, transaction histories, usage logs, contact databases and unique case details | Removing a name may not prevent re-identification when dates, locations or unusual events remain. |
| Financial and commercial information | Unreleased results, forecasts, pricing models, banking details, credit assessments and investor material | Disclosure can affect customers, markets, negotiations and regulatory obligations. |
| Contracts, vendors and procurement | Signed agreements, negotiation positions, vendor bids, evaluation scores, purchase plans and confidential supplier data | These documents can reveal legal commitments, competitive strategy or another party’s protected information. |
| Intellectual property and business plans | Source code, product roadmaps, designs, algorithms, security findings, research results and unreleased features | They may be trade secrets or provide competitors with a blueprint of planned work. |
| Internal policy and governance drafts | Draft policies, investigation reports, audit findings, board material and incident communications | Drafts can expose unresolved decisions, allegations or weaknesses before the organization is ready to publish them. |
CMS’s employee guidance states: “Never disclose or input PII, PHI, or any sensitive CMS data (including financial, health, vendor, procurement, evaluations, draft policies, or proprietary/business information) into publicly accessible AI platforms, chatbots, or prompts.” The named agency and its rules are specific to CMS; use your employer’s classification scheme and legal requirements for your own decisions.
Start with the approved-tool and account check
Before entering any work-related text, identify the exact service, account and feature. “We have an AI policy” is not enough if employees can sign in through several subscriptions or connect different extensions.
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- Find the approved-tool list. Read the current acceptable-use policy and data-classification rules. Confirm whether the service, mobile app, browser extension, plug-in, agent and connected storage integration are each approved.
- Confirm the account and tenant. Use the company-managed identity and tenant, not a personal account or an unmanaged trial. Check the subscription or plan name; controls can differ within the same product family.
- Check the permitted data classes. An approval may cover public and internal information but exclude personal, regulated, export-controlled or customer data. Follow the narrowest restriction.
- Verify the feature being used. Chat, file upload, web search, custom agents, retrieval from company repositories and automated actions can have different data paths.
- Use the escalation route when uncertain. Ask security, privacy, legal or the designated AI owner before submitting information. Do not “test” an unapproved service with real data.
Use the least sensitive input that can solve the task
Approval does not remove the need to minimize. Give an AI system only the context required to produce the desired result. If a task can be completed with a short description, do not upload the whole document or database export.
Remove direct identifiers and secrets
Delete names, email addresses, phone numbers, account numbers, exact addresses, credentials, tokens, private URLs and unique identifiers. Never place a secret in a prompt even when asking the model to “find,” “check” or “redact” it. Redaction after transmission cannot undo a disclosure.
Generalize details that identify people or businesses indirectly
Replacing a name alone does not make data anonymous. A rare job title, exact date, location, transaction amount and unusual event may identify the person when combined. Broaden dates, ranges and locations; remove unnecessary narrative; and check whether the remaining combination could point back to a real individual or customer.
Use fictional labels such as [CUSTOMER A], [PROJECT X] and [VENDOR B]. Keep a mapping in an approved internal system rather than in the prompt. Do not include the mapping when asking the AI service to summarize or transform the text.
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Prefer synthetic or generic examples
Ask the tool to work on a made-up example, a public document or a deliberately synthetic dataset whenever possible. AWS guidance identifies anonymization and synthetic data as approaches used in regulated contexts, but the right method depends on the risk and the applicable rules. Involve compliance or privacy specialists early for regulated work; an employee should not decide that an irreversible anonymization standard has been met by intuition.
Keep the task narrow
Instead of uploading a complete contract, describe the clause type and ask for a checklist of issues to review internally. Instead of pasting a support history, provide a fictional scenario with the general error pattern. Instead of sharing source code, explain the language, function and desired behavior without proprietary names or implementation details.
A safer employee workflow
- Define the output. State what you need—such as a neutral outline, test cases or a plain-language explanation—before collecting input.
- Classify the source material. Check the company label and whether it contains personal, regulated, contractual, security or proprietary information.
- Choose an approved service and feature. Confirm the managed account, subscription and connected systems. If any part is not approved, stop.
- Minimize and sanitize. Remove identifiers, secrets and unnecessary passages. Generalize dates, amounts and locations. Use fictional placeholders or synthetic data.
- Review the prompt for hidden disclosure. Inspect attachments, pasted history, automatic file context, browser pages and agent instructions. A prompt can disclose more than the visible question.
- Submit only the minimum. Do not add credentials or “temporary” secrets. Do not paste a full database export when a few synthetic rows demonstrate the problem.
- Check the result. AI output may be wrong, biased or an unintended reproduction of supplied information. Verify important claims against trustworthy sources and independent research before using or sharing the result.
- Store and share appropriately. Keep the output in an approved location, apply the correct classification and remove any residual sensitive text before forwarding it.
What to verify about an approved AI service
Security and procurement teams should document answers for the precise product, subscription and tenant. Employees should be able to find a plain-language summary of those decisions.
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|---|---|---|
| Are prompts, responses and retrieved company content used to train foundation models? | Current contractual terms and product documentation for the exact plan | Do not infer “no training” from a different tier or a marketing page for another service. |
| How long are prompts, files, responses and logs retained? | Retention, deletion and backup schedules, including support and abuse-monitoring access | Short retention is useful but does not eliminate exposure during processing or in backups. |
| Where is data processed and who can access it? | Regions, subprocessors, support access and contractual privacy protections | Residency and transfer requirements may restrict particular data classes or locations. |
| Are web search, agents, plug-ins and browser experiences covered by the same commitments? | Separate privacy statements, terms and configuration documentation | Use a feature only when its data path and controls have been reviewed. |
| Does the service honor identity permissions and information-protection settings? | Tests showing sensitivity labels, retention rules, audit records and administrative policies in the actual tenant | A connected AI feature must not retrieve material a user is not entitled to see. |
| Does the account have the required agreement and configuration? | Subscription record, data-processing terms and tenant settings | A business logo or login page is not proof that the needed controls are active. |
Microsoft describes enterprise examples in which prompts and responses are not used to train foundation models and services can inherit identity permissions, sensitivity labels, retention, audit and administrative settings. It also states that controls depend on subscription and that web search queries and agents require separate consideration. Recheck the current terms and configuration before relying on those claims for your organization.
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Controls IT and security teams should implement
Publish a short, enforceable rule
Name prohibited categories, approved services and account types, the approval path for new tools, and the channel for reporting mistakes. A one-page rule with examples is more useful than a broad statement that employees must “use AI responsibly.” Update it when services add agents, retrieval, browser features or automated actions.
Enforce least privilege
Limit repository permissions and integration scopes so an AI feature can retrieve only what the signed-in user is entitled to access. Restrict external data sources, outbound actions and the ability to send generated content to third parties. Review service accounts and shared credentials separately; an agent with broad permissions can turn a small prompt into a large data exposure.
Apply information protection and auditing
Use sensitivity labels, retention rules, audit trails and administrative policies where the selected service supports them. Confirm that the settings are active in the actual tenant and test both allowed and blocked cases. Redact personal information from logs where possible, minimize prompt and response retention, restrict log access and monitor unusual retrieval or outbound activity.
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Assess prompts, uploaded files, retrieved context, model inputs, outputs, logs, vector stores, backups and administrator access. AWS guidance recommends encryption in transit and at rest, fine-grained access controls for AI datasets and vector databases, and transport protection for prompts, outputs and retrieved context. A secure chat screen is not enough if a connected index or log retains the same material indefinitely.
Review suppliers and integrations before launch
Privacy, security, procurement and compliance teams should review new models, agents, plug-ins and data sources before deployment. Check that the model operator’s policies match organizational data-protection requirements, that contractual protections cover the intended data and that incident contacts are documented.
Train, test and monitor
Teach staff with realistic examples: a support transcript, a draft contract, a secret in a code block and an agent connected to a repository. Run periodic access and data-loss tests, document mitigations and monitor whether users are switching to unapproved services. Content filters and prompt instructions can help, but they cannot replace minimization, access control and policy. Prompt injection and other adversarial inputs also require security review throughout the system lifecycle.
Special caution for search, agents and connected data
An AI feature that can browse the web, search internal repositories, call a plug-in or take an action has a larger data and permission surface than a standalone text box. The visible prompt may be sent together with retrieved pages, conversation history, file context or hidden agent instructions.
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- Review each agent’s privacy statement, tools, memory and outbound destinations.
- Limit repository connectors to the smallest necessary folders and permissions.
- Block external sharing, purchasing, messaging or code deployment unless the action has a documented approval path.
- Test whether sensitivity labels and user permissions continue to apply to retrieved snippets and generated files.
If sensitive information was already submitted
Act quickly; do not wait to determine whether anyone viewed the prompt. Follow the employer’s incident process and avoid copying the sensitive material into another external channel while reporting.
- Stop the interaction. Do not add clarifying details, upload more files or ask the service to “forget” the information through another prompt.
- Notify security or privacy staff promptly. Use the established incident channel, manager route or emergency contact.
- Record the facts safely. Provide the service name, account or tier, approximate time, data categories, whether files or search were involved, and whether an agent or plug-in had access. Follow the incident team’s instructions for evidence; do not paste the secret into the report.
- Contain related access. The organization may need to revoke sessions, rotate passwords, API keys or tokens, restrict connectors and preserve relevant logs.
- Assess retention and notification duties. Security, privacy and legal teams should determine deletion options, affected people or systems, contractual obligations and jurisdiction-specific notification requirements.
- Document and improve. Record what control failed, update the approved-tool list or training and monitor for recurrence.
CMS guidance explicitly recommends immediate reporting of suspected or verified unintended disclosure. The exact containment and notification steps depend on the organization, the data and the applicable jurisdiction.
How to compare deployment choices without assuming one is safe
There is no universal “safe AI” label. Compare a service or deployment against the same questions every time:
- Is the exact product, account tier and tenant approved?
- How are prompts, responses and retrieved data used for training, safety review, support and abuse monitoring?
- What are the retention, deletion, access and audit controls?
- Where are data processed, backed up and transferred, and which subprocessors are involved?
- Do web search, agents, plug-ins and browser features have separate terms or data paths?
- Can identity permissions, sensitivity labels, retention rules and administrative policies be enforced in the actual configuration?
If a provider cannot answer a question clearly, classify the service as unsuitable for sensitive data until the gap is resolved. A “no training” statement addresses only one use; it does not by itself settle retention, logging, integrations, residency, access or legal obligations.
Quick Recap
What employees and managers should remember
- Public or unapproved AI prompts are disclosures outside the controlled company environment.
- Keep credentials, personal and health data, customer records, financial information, contracts, procurement material, source code, security findings and confidential drafts out of those tools.
- Use only approved services with the approved account, tier, feature and configuration.
- Minimize, sanitize and generalize input; use synthetic examples when practical.
- Never rely on replacing names alone, a “no training” claim or a content filter as complete protection.
- Report an accidental submission immediately through the employer’s incident process.
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