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Use only an AI service your employer has explicitly approved, and treat every prompt sent to a public or unapproved service as a disclosure outside the company’s controlled environment. Do not paste personal, customer, employee, health, financial, credential, vendor, contract, procurement, draft-policy, source-code, security, or other proprietary information. Before using an approved tool, verify the exact product, account tier, contract, tenant settings, retention, training use, connected features, and access controls.
When policy allows the task, minimize the input: remove identifiers and secrets, generalize details, use synthetic examples or fictional labels, and provide only the context needed. If sensitive information has already been submitted, stop using the tool for that matter and report it immediately through your organization’s security or privacy process.
Why a prompt is a data disclosure
A prompt is not merely a private note to a piece of software. It may be transmitted to a service provider, stored in conversation history, processed by safety or abuse-monitoring systems, exposed to administrators or support personnel, included in logs, sent to connected search or agent systems, or retained in backups and analytics. The exact handling depends on the product, subscription, configuration, and feature being used.
That is why a company account, a browser extension with a company logo, or a label such as “enterprise” does not automatically make every prompt appropriate. A service may offer strong protections for one plan while a different plan, web-search mode, plugin, agent, or personal account follows different terms. The safe default is to regard an unapproved prompt as an external disclosure and to verify the precise service before sharing business information.
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AI output also creates a second risk. The system can reproduce details from the context you supplied, make an inaccurate claim, or expose information through a generated summary, email, code sample, or report. Protecting the input does not remove the need to review the output before it is circulated or used in a decision.
Information that should stay out of public or unapproved AI tools
The following categories are a practical minimum. Your employer may prohibit additional information or impose stricter rules for regulated work.
| Category | Examples | Why it matters |
|---|---|---|
| Credentials and secrets | Passwords, API keys, access tokens, private certificates, recovery codes and connection strings | A prompt can expose a credential to a provider, a log, a browser history, or anyone who later accesses the conversation. A secret should be rotated, not “cleaned up” after sending. |
| Personal information | Names tied to account details, identification numbers, addresses, contact details, customer IDs and unique transaction records | Combinations of ordinary fields can identify a person even when one field has been removed. |
| Health information | Medical histories, diagnoses, treatment details, claims data and employee accommodation records | Health information can trigger legal, contractual and organizational obligations that a general-purpose service is not approved to handle. |
| Financial information | Bank details, payment data, unreleased results, forecasts, payroll records and internal pricing | Disclosure can create fraud, market, contractual or competitive harm. |
| Customer and employee records | Support transcripts, performance files, investigations, complaints and case notes | These records often combine personal, confidential and access-controlled information. |
| Contracts and legal material | Signed agreements, negotiation history, attorney communications, statements of work and confidentiality terms | Sharing can violate a confidentiality obligation or compromise a negotiation. |
| Vendor and procurement information | Bids, evaluation scores, due-diligence files, supplier rates and selection recommendations | Premature disclosure can disadvantage vendors, undermine a fair process or breach procurement rules. |
| Security information | Vulnerability reports, incident details, network diagrams, detection rules and internal threat assessments | Attackers could use operational details, and disclosure may interfere with an investigation. |
| Source code and product plans | Private repositories, unreleased features, architecture documents, roadmaps and proprietary algorithms | Code and plans can be valuable intellectual property and may contain embedded secrets. |
| Confidential drafts | Draft policies, board material, strategy documents, research results and internal business correspondence | A draft can be sensitive even if it contains no obvious personal identifier. |
The Centers for Medicare & Medicaid Services (CMS) gives its staff a particularly clear agency-specific rule:
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CMS guidance is an example of a government agency’s policy, not a universal rule for every employer. Follow your organization’s policy and the laws and contracts that apply to your work.
The outside-expert test
Ask whether you would send the exact material to an outside expert who has no confidentiality agreement and no business need to know. If you would hesitate, do not paste it into an unapproved AI tool. The test catches information that employees sometimes overlook, such as a draft pricing sheet or a screenshot containing a customer number.
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Three checks before you open an AI chat
- Check the approved-tool list. Confirm that the service, account, mobile app, browser extension, plugin, agent and connected storage feature are approved for work. A personal account for an otherwise familiar service is not automatically covered.
- Check the data classification. Identify whether the material is public, internal, confidential, restricted, personal, regulated or subject to a contract. If the classification is unclear, stop and ask your manager, security team, privacy office or records owner.
- Check the task’s minimum information. Decide what the tool actually needs. Most drafting, formatting, brainstorming and coding tasks can be demonstrated with a generic or synthetic example rather than a live customer record or confidential document.
If any answer is uncertain, do not proceed with sensitive data. Use an approved internal workflow or obtain an explicit decision through the organization’s approval path.
A safer workflow for everyday AI use
1. Start from the company’s rules, not from the tool’s marketing page
Read the acceptable-use policy, data-classification standard and approved-service register. Note the permitted account or tenant, allowed data classes, prohibited features, retention limits and incident-reporting channel. If the register names a product but not a specific plan, assume the plan still needs verification.
2. Reduce the input before writing the prompt
- Remove names, email addresses, phone numbers, account numbers, dates of birth, precise locations and unique transaction identifiers.
- Remove passwords, tokens, keys, certificates, connection strings and other secrets entirely.
- Replace real parties with fictional labels such as
[CUSTOMER A],[EMPLOYEE B]and[PROJECT X]. - Generalize dates, amounts, locations and technical details when the exact value is not required.
- Delete unrelated paragraphs, attachments, metadata, tracked changes and comments before uploading a file.
- Check whether the remaining combination of facts could identify the person, customer, supplier or project indirectly.
Replacing a name is not the same as anonymizing data. A rare diagnosis, a distinctive contract date and a small town may identify someone together. Keep only the fields needed for the stated task.
3. Prefer synthetic or generic examples
For a request such as “write a response to a delayed shipment,” provide a fictional order and a general description of the problem. For code assistance, create a small reproducible example with fake data and environment variables such as API_KEY rather than a live key. For policy drafting, describe the requirement in abstract terms instead of uploading the confidential draft.
Anonymization and synthetic data are established approaches in regulated environments, but the right method depends on the information and the applicable rules. Involve compliance or privacy specialists early when the task concerns regulated data.
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4. Never put a secret in the prompt
Do not ask an AI service to decode, validate, store or troubleshoot a live password, token or private key. Redacting the text after submission does not undo transmission, retention or access. If a secret was sent, treat it as exposed and follow the credential-rotation process.
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5. Control files, retrieval and integrations
Uploading a document, enabling a connector or allowing an agent to search a repository can disclose far more than the text you intended to discuss. Select only the necessary file, verify the destination, and disable connectors that are not required. Confirm that the feature uses the same privacy and retention commitments as the base service; web search, agents and plugins may have separate handling.
6. Review the result before it leaves the approved boundary
Check for copied names, identifiers, confidential passages and unsupported claims. Verify factual statements against trustworthy sources and independent research before using the output in a decision, customer communication, legal document or operational change. An AI system can be wrong or biased even when the prompt was safe.
What to verify about an approved service
Approval should attach to a precise product and configuration, not just a vendor name. Ask the service owner or procurement team for current answers to the following questions.
| Control area | Questions to answer |
|---|---|
| Model training | Are prompts, responses or retrieved company documents used to train foundation models? Is the answer different for consumer, business and enterprise subscriptions? |
| Retention and deletion | How long are prompts, files, responses, safety records and backups retained? Can an administrator set deletion periods, and who can restore or access the data? |
| Human and support access | Can provider staff, contractors or safety reviewers view content? Under what circumstances, with what controls and for what duration? |
| Processing location | Which regions and subprocessors handle prompts, retrieved context, logs and outputs? Do contractual or regulatory requirements limit those locations? |
| Connected features | Are web search, agents, plugins, browser experiences, file uploads and external actions covered by the same commitments, or do they have separate terms? |
| Identity and permissions | Does the tool enforce single sign-on, multifactor authentication, role-based access and the source system’s document permissions? |
| Information protection | Can sensitivity labels, retention rules, data-loss-prevention policies and encryption settings travel with content into prompts and outputs? |
| Audit and investigation | What events are logged, how long are logs kept, who can search them, and can the organization detect unusual downloads or outbound actions? |
| Contract coverage | Is the exact account tier covered by the required data-processing agreement, confidentiality terms and incident-notification obligations? |
| Tenant configuration | Has the organization actually enabled the protections, restricted sharing and disabled unapproved connectors in its own tenant? |
A vendor-specific example: Microsoft Copilot services
Microsoft’s published enterprise information describes Microsoft Copilot and Microsoft Copilot Chat as services in which prompts and responses are not used to train foundation models. It also describes inheritance of identity permissions, sensitivity labels, retention policies, auditing and administrative settings. Those statements are tied to particular services and subscriptions; Microsoft says controls vary by subscription.
Web-search queries have their own handling practices, and agents can have separate privacy statements and terms. Therefore, an organization must check the current documentation for the exact Copilot product, account tier, tenant configuration and enabled features rather than treating the brand name as a blanket approval.
What a “no training” statement does not settle
A no-training commitment does not by itself answer how long content is retained, who can access it for support or safety, where it is processed, what subprocessors are involved, whether logs contain personal information, or what a connected agent can retrieve. Evaluate the complete data lifecycle and contract.
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Controls for IT, security and privacy teams
Publish a short, enforceable AI data rule
Employees need a rule they can apply during a busy workday. Name prohibited categories, list approved services and account types, define the approval path for new tools, identify forbidden connectors, and state exactly where to report a suspected disclosure. Link the rule to the organization’s existing classification and incident procedures.
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Apply least privilege to repositories and integrations
An AI feature should be able to retrieve only what the signed-in user is entitled to see, and preferably only what the task requires. Scope connectors to approved repositories, restrict cross-tenant sharing, limit external data sources and require confirmation before an agent sends messages, changes records or performs other outbound actions. Test permission changes with ordinary and privileged accounts.
Use information-protection controls where supported
Enable sensitivity labels, retention policies, audit settings and administrative restrictions in the selected service. Confirm that labels remain effective when content is summarized, exported or copied into another application. A control that exists in documentation but is disabled in the tenant provides no protection.
Minimize and protect logs
Prompt and response logs can contain the same sensitive material as the original conversation. Configure the shortest useful retention, restrict log access, redact personal information where feasible, and monitor searches, downloads and unusual activity. Define who may inspect logs during support or incident response.
Protect the full data lifecycle
Encrypt prompts, retrieved context, outputs and administrative traffic in transit and at rest. Include vector stores, caches, uploaded files, temporary processing areas, backups and exported transcripts in the assessment. Use fine-grained access controls for AI datasets and retrieval indexes, and document deletion behavior for each component.
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Procurement, legal, privacy and security teams should review terms before approving a new model, agent, plugin, browser feature or connected data source. Confirm that the model operator’s policies match organizational data-protection requirements and that incident-notification, deletion and subprocessor clauses are acceptable.
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Use layered defenses
Content filters and prompt instructions can reduce accidental misuse, but they are not a substitute for minimizing data, controlling access and enforcing policy. Assess prompt injection and other adversarial-input risks throughout the system lifecycle. An attacker may try to make an agent reveal retrieved information or take an unauthorized action, so test the complete workflow rather than only the chat interface.
Train and repeat
Use realistic examples: a pasted support ticket, a spreadsheet with hidden columns, a screenshot containing a token, or an agent connected to a broad repository. Explain the approved alternative for each example. Refresh training and documentation whenever services, subscriptions, connectors or internal policies change.
How to compare AI services or deployment choices
When evaluating alternatives, compare the exact offerings and configurations rather than relying on a general security label.
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| Decision axis | What to compare | Evidence to request |
|---|---|---|
| Approval scope | Whether the employer approves the precise product, account tier and region | Approved-tool register, contract and tenant record |
| Use of content | Training, evaluation, safety review and human-access practices for prompts, responses and retrieved data | Current product terms and data-processing documentation |
| Retention | Conversation, file, log, backup and deletion periods | Retention settings, deletion procedure and audit evidence |
| Residency and subprocessors | Processing regions, transfers and provider dependencies | Subprocessor list, transfer terms and regional controls |
| Feature boundaries | Separate handling for search, agents, plugins, browser tools and external actions | Feature-specific privacy statements and permissions model |
| Enterprise controls | Identity, labels, retention, audit, DLP, encryption and administrator policies | Configuration guide and test results from the organization’s tenant |
| Operational response | Detection, investigation, credential rotation and provider-notification options | Logging coverage, support process and incident clauses |
No single comparison table can make a service safe for every data class. The result depends on the organization’s legal duties, risk tolerance, configuration and users’ permissions.
If sensitive data has already been submitted
Report promptly even if you are unsure whether the service retained the content. Early reporting gives the organization more options for containment and notification.
- Stop the conversation. Do not continue asking the service about the sensitive material, and do not paste the same content into another external channel.
- Use the internal reporting route. Contact the security, privacy or incident-response team specified by your employer. If there is an emergency or active compromise, use the organization’s urgent escalation path.
- Record essential facts without duplicating the data. Provide the service name, exact account or tier if known, approximate time, data categories, whether a file was uploaded, and whether search, an agent, plugin or connected storage was involved. Follow company instructions for preserving evidence.
- Protect credentials. Tell the response team whether passwords, tokens, keys or certificates were present so they can assess rotation or revocation. Do not include the secret in the report.
- Support containment. Investigators may disable the account or connector, request deletion, restrict access, preserve logs and contact the provider. Do not independently delete evidence unless instructed.
- Assess impact and obligations. The organization should determine what was exposed, who could access it, whether people or systems are affected, and whether contracts or laws require notification. The correct steps depend on the employer, provider and jurisdiction.
CMS guidance recommends immediate reporting of suspected or verified unintended disclosure. That recommendation does not prescribe the same notification timeline or legal conclusion for every organization.
Common mistakes to avoid
- “It has our company logo, so it is safe.” Verify the product, subscription and tenant settings.
- “The provider says it does not train on prompts.” Check retention, support access, processing locations, logs, subprocessors and connected features too.
- “I removed the name.” Re-identification can occur through rare facts, combinations of fields or attached metadata.
- “I will delete the chat later.” Deletion may not cover backups, logs, safety records, exports or provider-side copies.
- “The prompt is fine because the answer is internal.” The input still crossed a boundary, and the output may repeat it.
- “A filter will catch secrets.” Filters can miss credentials and sensitive context; prevention and access control are stronger safeguards.
- “The agent only reads what I can see.” Confirm permission inheritance and test retrieval boundaries before connecting repositories.
- “I can report it by pasting the transcript elsewhere.” Use the designated internal channel and minimize the report.
A practical policy pattern for managers
A useful policy can fit on one page if it answers five operational questions:
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- Which data classes are prohibited, restricted or allowed?
- What sanitization and human-review steps are required?
- Who approves exceptions, new connectors and regulated use cases?
- How and where must employees report accidental disclosure?
Pair the policy with examples and a maintained service register. Assign an owner to review provider terms, subscription changes and new features. Document mitigations and monitoring so that staff know the rule is enforced in practice, not merely published.
Bottom line for employees and administrators
Keep sensitive company data out of public and unapproved AI prompts. For approved use, verify the exact service and configuration, minimize and sanitize the input, restrict retrieval and integrations, protect logs and review outputs. If a disclosure occurs, report it immediately through the organization’s incident process so security, privacy and legal teams can decide on containment and notification.
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