Do not paste confidential company information into an AI tool until your organization has approved the exact service, plan, and configuration for that type of work. A prompt, uploaded file, connector, or agent can send information outside your company boundary and create additional copies in histories, logs, retrieval indexes, caches, embeddings, or memory. Safe use therefore requires more than a vendor’s “no training” statement: classify the data, use an approved tool path, enforce source permissions, minimize what leaves the boundary, control what persists, and monitor the workflow.
Why an AI prompt can become a data-disclosure event
When an employee submits text or a file, the material may be transmitted to a service, processed by several components, returned in an answer, and retained for operational or product purposes. Connected search, browser features, plug-ins, agents, and external applications can add further destinations. NIST identifies disclosure and inference of sensitive information as privacy risks in generative AI systems.
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The exposure is not limited to the visible answer. A system may retain conversation history, prompt and response logs, retrieval indexes, cached embeddings, connector results, telemetry, or agent state. A later user who has access to one of those stores could discover information that was never intended for that task. Microsoft’s security guidance also treats memory and retrieval artifacts as practical disclosure paths.
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Use the following rule for employees: if you would not email the content to an unapproved external service, do not paste it into an unapproved AI tool. For managers and security teams, the corresponding rule is to approve a defined workflow rather than allowing a general-purpose tool to make its own data-handling decisions.
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Define what employees may submit
A short, enforceable policy is more useful than a blanket statement that “AI is allowed.” Give each data class a clear action and examples. Employees should have a route to ask for review when a task does not fit a listed example.
| Data class | Typical examples | Default AI handling |
|---|---|---|
| Public | Published documentation, public product descriptions, released marketing copy | May be used in approved tools, subject to normal copyright and accuracy rules. |
| Internal | Non-public procedures, planning notes, internal FAQs | Use only an organization-approved workspace with access and retention controls. |
| Confidential | Customer information, source code, unreleased product details, deal terms, internal financial material | Do not submit to consumer or unapproved services. Use a specifically approved workflow, with minimization, access control, and monitoring. |
| Restricted or regulated | Credentials, private keys, employee records, regulated personal data, security incident evidence | Keep out of general AI tools unless security, privacy, legal, and the business owner have approved a narrowly scoped design. |
Teach people to remove or replace sensitive values when the task can be completed with less data. Replace a customer name with Customer A, omit account numbers, use a synthetic code fragment instead of production source, and summarize a contract clause instead of uploading the full agreement. Redaction is not a substitute for an approved service, because surrounding text can still identify a person or transaction.
NIST’s Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile (AI 600-1, 2024) calls for policies and procedures that support continual improvement in generative-AI risk measurement. Treat the policy as a living control: update examples, record exceptions, and change the rules when products or integrations change.
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“We use an enterprise AI product” is not precise enough. Controls can differ between consumer and business editions, subscriptions, regions, tenants, browser features, agents, and connected applications. Record the approved configuration, not merely the product name.
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| Question to answer | Why it matters |
|---|---|
| Are prompts, files, outputs, or connected data used to train models? | A non-training commitment limits one use, but does not answer retention, access, or downstream processing. |
| How long are each of those data types retained, and can administrators or users delete them? | Short-lived prompts and long-lived histories create different exposure windows. |
| Who can access content? | Consider provider personnel, tenant administrators, support systems, other users, agents, and connected applications. |
| Where is data stored and processed? | Geography may affect contractual, regulatory, or customer requirements. |
| How are identity, roles, source permissions, labels, DLP, audit, and eDiscovery supported? | These controls determine whether the assistant can enforce the same boundaries as the source system. |
| What do web search, browser functions, plug-ins, agents, and third-party integrations receive? | These paths may have separate terms, privacy notices, or retention behavior. |
| Which controls are actually licensed and enabled in the proposed plan? | Documentation can describe capabilities that are unavailable in a particular subscription or platform. |
Microsoft states that organizational Copilot prompts and responses are covered by enterprise data protection commitments, while available controls vary by subscription. Its documentation distinguishes web-search queries from prompt and response handling and directs organizations to review the privacy statements and terms for agents. Do not assume that a web-search request or an agent call inherits every protection applied to the main chat experience.
OpenAI states that inputs and outputs from ChatGPT Business, Enterprise, Edu, Healthcare, Teachers, and its API platform are not used for training by default, and describes encryption and retention controls for qualifying organizations. That is a provider statement, not an automatic approval for every use. Confirm the exact service, workspace settings, contract, connected applications, and retention configuration before allowing confidential material.
Have procurement, security, privacy, legal, and the business owner sign off on high-risk uses. Keep the service review with the system inventory so a plan change or newly enabled agent triggers a fresh assessment.
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- Inventory every repository and connector proposed for the workflow.
- Remove stale accounts, anonymous links, broad groups, and inherited permissions that are no longer justified.
- Check whether contractors, guests, service accounts, and former employees can read the source content.
- Test retrieval as representative users, including a user who should not see the document.
- Restrict the connector to the smallest source set and purpose that the task requires.
Keep the principle that the model should surface only content the current user is authorized to access. Sensitivity labels can add handling context, and encryption can enforce restrictions, but both must be configured and supported in the specific product path. Microsoft Purview documentation describes permission and label behavior in supported Microsoft AI experiences; verify the behavior in your tenant rather than relying on a label name alone.
Control data at the point of entry
Controls should work before information reaches an external service, not only after a response is generated. Combine classification, endpoint policy, redaction, and user education.
Use classification and DLP
Classify documents and sensitive information consistently so policy engines can recognize what a prompt or upload contains. On supported, onboarded Windows computers, Microsoft endpoint DLP can warn or block sharing sensitive information with third-party generative-AI sites. Coverage depends on licensing, platform, service detection, and policy configuration. Test both the warning and block paths with realistic prompts before declaring the control effective.
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Minimize or transform the payload
Where an external service is necessary, remove direct identifiers, credentials, private keys, and unnecessary business context. Tokenization can replace account numbers with reversible tokens held inside the company; one-way pseudonyms can support classification or drafting without revealing the original identity. Store the mapping separately and protect it as confidential data.
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Use a controlled gateway when appropriate
A secure proxy or internal application can enforce an approved model, redact patterns, attach a purpose and user identity, apply rate limits, and record an auditable event before forwarding a request. A gateway does not make every workflow safe: it must also control connectors, retention, administrator access, and the destinations reached by agents.
Teach the final check
Before pressing send, employees should ask: What is the most sensitive item in this prompt? Is the service approved for that class? Can the task be completed with less data? Does the request include a credential, private key, customer identifier, employee record, unreleased deal, or production source? If the answer is uncertain, stop and use the review route.
Treat histories, memory, and retrieval indexes as data stores
Persistent artifacts deserve the same protection as the original document. Inventory where each workflow writes data and assign an owner, access policy, and retention period.
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- Retrieval indexes and embeddings: scope them by tenant, user, project, and authorization; remove entries when the source permission or retention period ends.
- Caches and connector outputs: prevent one user’s result from being reused for another user without a fresh authorization check.
- Memory and agent state: give users and administrators a way to inspect, correct, and delete stored context.
- Logs and telemetry: redact prompt content where full text is unnecessary, restrict access, and protect exports.
Scan content before writing it to a persistent store and again before returning it to a user or another agent. Monitor unusual retrieval volume, repeated attempts to extract a sensitive field, access outside normal working patterns, and requests that combine many small pieces of restricted information. Microsoft’s sensitive-information-disclosure guidance identifies these context artifacts as exposure paths; your design should document each one explicitly.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.A practical approval and rollout sequence
Use this sequence for a new assistant, connector, or business process. Each step should produce evidence that another reviewer can inspect.
- Describe the task and data. Name the business purpose, users, data classes, source repositories, expected outputs, and whether information leaves the company boundary.
- Choose the minimum-data design. Remove fields that are not needed. Prefer summaries, synthetic examples, or tokenized values over raw records.
- Select an approved service and configuration. Record the product edition, subscription, region, enabled features, agents, integrations, retention settings, and contract terms.
- Review permissions at the source. Correct oversharing, test representative identities, and document which roles can retrieve which content.
- Configure entry controls. Apply labels, endpoint DLP, proxy rules, redaction, tokenization, and user warnings. Define what happens when a control detects sensitive content.
- Configure persistence controls. Set history, index, cache, memory, log, and deletion policies. Confirm that revoking source access also removes or blocks derived content.
- Test failure cases. Try an unauthorized document, a restricted field, an agent call to an unapproved application, an expired permission, and a deletion request. Capture the observed result.
- Launch narrowly. Start with a limited user group and low-risk data. Review alerts, false positives, retrieval accuracy, and access logs before expanding.
- Operate and reapprove. Audit activity as allowed by policy and law, review exceptions, retrain staff, and reassess the workflow when terms, models, connectors, or licensing change.
How to compare approved AI options
When several services appear suitable, compare them on the same evidence rather than on general marketing language.
| Comparison axis | Evidence to request |
|---|---|
| Training use | Contractual language and settings for prompts, files, outputs, and opt-in or opt-out behavior. |
| Retention and deletion | Default periods, administrator controls, user deletion, backups, legal holds, and handling of memory and logs. |
| Identity and source authorization | How roles, groups, labels, and revocations are enforced during retrieval and generation. |
| DLP, audit, and eDiscovery | Supported platforms, licenses, alert detail, export controls, and coverage of prompts, responses, and files. |
| Agents and integrations | Data sent to each third party, separate terms, permission model, and ability to disable functions. |
| Residency and processing geography | Regions available for the exact plan and any exceptions for support, abuse monitoring, or subprocessors. |
| Availability of controls | Tenant-specific entitlement, configuration steps, dependencies, and tested behavior rather than a feature-list claim. |
What to do if confidential data was already pasted
Treat an accidental submission as a potential information-security incident, even if the employee believes the provider does not train on it.
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- Record the service, account, workspace, time, prompt or file involved, recipients, connected tools, and any credentials or personal data included.
- Notify the organization’s security or privacy incident channel immediately, following the company’s escalation policy.
- Rotate exposed passwords, tokens, private keys, or access links. Revoking a credential is urgent because deletion from an AI history does not revoke a credential already copied elsewhere.
- Ask the service administrator what deletion, retention, audit, and legal-hold controls apply, and preserve the provider’s response.
- Assess downstream copies: conversation shares, exports, logs, indexes, agent memory, connector caches, and generated documents.
- Document the root cause and update the policy, DLP rule, training, or approval record that would prevent recurrence.
When confidential computing is worth considering
Hardware-backed confidential-computing technologies can help protect data and models while they are being processed in a specialized trusted execution environment. Azure Confidential AI documentation describes this architecture option. It may be appropriate when the threat model requires stronger protection from the host platform or infrastructure operator and the workload can meet the technology’s deployment constraints.
Confidential computing does not replace identity and access control, data minimization, source-permission cleanup, vendor review, DLP, retention limits, monitoring, or incident response. It is an architectural decision for a defined threat model, not a blanket safety setting for ordinary employee chat.
Responsibilities for employees and approvers
Every employee
- Use only the approved account and workspace for company work.
- Check the data class before uploading or pasting.
- Remove unnecessary identifiers and confidential values.
- Do not enable an agent, plug-in, browser feature, or connector without approval.
- Report accidental disclosure, suspicious retrieval, or unexpected sharing promptly.
Managers and process owners
- Define the business purpose, acceptable inputs, users, and expected outputs.
- Own exceptions and ensure staff training matches the actual workflow.
- Review whether the AI output itself contains confidential material before distribution.
Security, privacy, legal, and IT teams
- Approve services against exact terms, plans, regions, and enabled features.
- Correct source permissions and configure labels, DLP, identity, retention, audit, and deletion.
- Test controls with unauthorized users and realistic failure cases.
- Reassess the workflow when providers change models, agents, contracts, or defaults.
The safest answer to “Can I paste this into AI?” is therefore conditional: only after the data class, approved service, configuration, permissions, entry controls, persistence controls, and monitoring path have all been established for that specific task.
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