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Do not paste confidential company information into an AI tool until your organization has approved the specific service, plan, configuration and workflow. A prompt, uploaded file, retrieval connector or agent can send information to a provider, store it in histories or indexes, expose it through permissions, or pass it to another integration. “Not used to train models” addresses only one part of that chain.

Effective protection combines an acceptable-use policy, approved-tool register, source-system permissions, classification and data-loss prevention (DLP), careful retention and memory controls, monitoring, and periodic review. The process below is designed for employees deciding whether they can use AI and for security, privacy and compliance teams approving workplace deployments.

What counts as confidential data in an AI prompt?

Use your organization’s existing classification scheme rather than inventing a new definition for AI. Confidential material commonly includes customer and employee records, credentials and secrets, unreleased financial or deal information, source code, security findings, legal advice, health information, intellectual property and data received under a non-disclosure agreement. A harmless-looking combination can also be sensitive: names, dates, locations and account details may identify a person or reveal a transaction when joined together.

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Risk exists even when an employee does not intentionally disclose a secret. A copied error log may contain an access token; a spreadsheet may include hidden columns; a document upload may contain comments or tracked changes. An AI service may also infer sensitive facts from otherwise ordinary text. NIST’s Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile (AI 600-1, 2024) identifies disclosure and inference as privacy risks and calls for policies that continually improve generative-AI risk measurement.

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Use a simple decision test before sending anything

  • Would unauthorized disclosure cause harm? Consider financial, legal, safety, competitive or reputational impact.
  • Is every value necessary? If the task can be completed with a summary, sample or synthetic data, do not send the raw record.
  • Is the destination approved for this classification? A personal account, consumer chatbot or browser extension is not an approved business service merely because it is popular.
  • Can the resulting answer be trusted and contained? Outputs can repeat, transform or reveal information from the prompt and connected sources.

Why “the AI does not train on my data” is not enough

Training use is one question in a larger data lifecycle. Before approval, document what happens to prompts, uploaded files, generated responses and data returned by connectors.

Control question What to establish
Training and model improvement Whether inputs and outputs are used for training, whether any opt-in exists, and which product and plan the statement covers.
Retention and deletion How long chats, files, logs, embeddings and agent state remain; who can delete them; and whether deletion is supported for backups and indexes.
Access Which provider staff, administrators, users, agents and integrations can view data, and how access is logged and reviewed.
Processing location Where data is stored and processed when contractual, regulatory or customer requirements specify geography.
Connected features How web search, browser functions, plug-ins, third-party apps, connectors and custom agents handle data.
Administrative evidence Whether the service supplies audit, DLP, eDiscovery, encryption, retention and configuration controls on the proposed plan.

Microsoft states that organizational Copilot prompts and responses receive enterprise data-protection commitments, but controls vary by subscription. Its documentation treats web-search queries separately from prompt and response handling and directs administrators to each agent’s privacy statement and terms. 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. Those are provider statements, not a complete suitability decision: confirm the exact service, workspace settings, contract, connected applications and retention configuration before approval.

Publish rules before employees start experimenting

An acceptable-use policy should answer the employee’s practical question: “Can I paste this into AI?” Write it in plain language and include examples for each data class.

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Minimum policy contents

  • Name data classes that must never be submitted to consumer or otherwise unapproved services.
  • List approved AI tools and state which classes and tasks each one may handle.
  • Require redaction, replacement or synthetic examples when raw identifiers are unnecessary.
  • Define rules for uploads, screenshots, browser extensions, plug-ins, agents, web search and external connectors.
  • Explain how to request a new tool, feature or high-risk use case for review.
  • State how incidents, accidental uploads and suspected data exposure must be reported.
  • Require human review of outputs before they are used in decisions, customer communications, code or records.

Train staff with realistic examples: remove customer names and account numbers from a support summary; replace a production credential with a dummy token; and use a small synthetic dataset to debug a script. NIST’s 2024 profile specifically calls for policies and procedures that support continual improvement in generative-AI risk measurement, so update the rules when products, threats or business processes change.

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Approve the exact service, plan and configuration

“We use an enterprise AI product” is not precise enough. Approval should identify the tenant or workspace, subscription, enabled features, identity integration, retention settings, regions and connected applications. Defaults and entitlements can change after an upgrade, so record the approved configuration and an owner responsible for rechecking it.

Questions for a provider review

  1. Are prompts, files, outputs or connector data used for training or model improvement? Is there an opt-in, and can an administrator disable it?
  2. What are the default and maximum retention periods for conversations, uploads, telemetry, backups, indexes, embeddings and memory?
  3. Can administrators enforce deletion, legal holds, export and eDiscovery requirements?
  4. Which provider personnel and subprocessors can access content, under what controls and with what audit trail?
  5. Where is content processed and stored, and can the organization select an allowed geography?
  6. How do web search, browser actions, agents, plug-ins and third-party applications receive and retain data?
  7. Does the plan support single sign-on, multifactor authentication, role-based administration, DLP, sensitivity labels, encryption and detailed audit logs?
  8. What happens to data when a user leaves, a connector is removed, a contract ends or a tenant is deleted?

Have security, privacy, legal, compliance and the business owner sign off on high-risk workflows. Keep the provider’s terms, configuration export and approval date with the service record so a later reviewer can see what was actually authorized.

Fix source permissions before connecting company data

Retrieval-augmented generation (RAG) and workplace assistants do not repair an over-permissioned repository. If a user can already access a broadly shared folder, an assistant may make that content easier to find and summarize. Review SharePoint sites, drives, document stores, ticket systems and databases before enabling retrieval.

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Permission review checklist

  • Remove stale accounts, anonymous links, “everyone” groups and inherited access that no longer reflects a job role.
  • Separate sensitive projects and customer matters into appropriately restricted locations.
  • Map each connector to an identity and verify that it enforces the current user’s permissions at query time.
  • Test both allowed and denied scenarios with ordinary user accounts, not only an administrator account.
  • Review service accounts and indexing identities; an indexer should not grant users more access than the source permits.

Sensitivity labels can add handling context and, where supported, encryption or usage restrictions. Labels are not a substitute for correct permissions: they must be configured and enforced in the particular AI product path. Microsoft Purview documentation describes permission and label behavior for supported Microsoft AI experiences; verify the supported service, platform and licensing before relying on a control.

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Stop unsafe sharing at the point of entry

Classification and DLP should operate before data leaves a managed device and throughout the AI workflow. Microsoft documents endpoint DLP that can warn or block sharing sensitive information with third-party generative-AI sites on supported, onboarded Windows computers. Coverage depends on licensing, platform, service recognition and policy configuration, so test the actual browser, device and tenant rather than assuming every chatbot is covered.

Practical controls

  • Create detectors for credentials, payment data, personal identifiers, regulated records, source-code markers and internal classifications.
  • Start with audit or user-warning mode, then block high-confidence violations after measuring false positives and legitimate exceptions.
  • Apply the same policy to copy and paste, file upload, print-to-PDF, browser extensions and API integrations where the platform supports it.
  • Use a secure proxy, redaction service or tokenization when an external model is needed but raw identifiers are not.
  • Redact secrets from application logs and support tickets before they become searchable by an assistant.
  • Encrypt data in transit and at rest, and restrict export, download and sharing of generated files.

A warning is useful only if employees understand the approved alternative. The block message should identify the data category, explain the business-safe path and provide a review process for legitimate exceptions.

Treat conversation history, memory and indexes as data stores

Confidentiality does not end when the model returns an answer. Conversation histories, retrieval indexes, cached embeddings, connector results, agent memory, telemetry and logs can preserve sensitive material and expose it to a later user or task.

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Lifecycle controls

  1. Inventory: List every place a prompt, file, output or retrieved passage is copied, including provider logs and internal observability systems.
  2. Minimize: Store only what the task requires. Disable persistent memory and long chat history for workflows that do not need them.
  3. Scope: Tie each artifact to the user, role, task and purpose. Do not let a general-purpose agent reuse a project’s context across tenants or teams.
  4. Scan before storage: Detect secrets and sensitive classes before writing to histories, vector databases, caches or logs.
  5. Scan before display: Check retrieved passages and generated responses before returning them to a user or another agent.
  6. Retain and delete: Apply documented schedules, legal holds and verified deletion to primary stores, replicas and indexes.
  7. Monitor: Alert on unusual bulk retrieval, repeated extraction attempts, cross-project access or sudden downloads.

Microsoft’s sensitive-information disclosure guidance identifies memory and retrieval artifacts as practical exposure paths. A successful test should demonstrate that a revoked user cannot retrieve old content, that deletion removes it from relevant indexes, and that logs do not recreate the secret the workflow was meant to protect.

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Control agents, browser features and integrations

An agent can act as a second data processor. It may call a CRM, search the web, execute code, send email or pass a prompt to another model. Approve each tool and data flow rather than approving “agents” as a category.

Agent review questions

  • What identities and permissions does the agent use, and are they narrower than the user’s general access?
  • Can the agent send content outside the approved tenant or region?
  • Are tool calls, retrieved records and final responses logged with enough detail for investigation?
  • Can an agent write to systems, send messages or make transactions without human confirmation?
  • How are prompts and outputs handled by each third-party application or model in the chain?
  • Can administrators disable an agent, connector or browser action quickly during an incident?

Require confirmation before irreversible actions and isolate high-risk tools in separate environments. Recheck these controls whenever an agent gains a new connector or capability.

Use a risk-based approval path

Not every use needs the same controls. A public-text brainstorming task may fit an approved consumer tool, while customer records or unreleased acquisition documents require a restricted enterprise workflow—or may be prohibited entirely.

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Use case Typical decision Required safeguards
Public information or synthetic examples Low risk when no internal context is added. Approved account, normal review of output and no confidential uploads.
Internal, non-sensitive drafts Allow in an enterprise workspace configured for company data. Identity controls, documented retention, audit and DLP appropriate to the classification.
Customer, employee, financial or source-code data Restricted approval; minimize or redact first. Source permissions, labels, DLP, short retention, access logging and human review.
Regulated, privileged or critical-security data Specialist review; often prohibit external processing. Contractual controls, geography decision, isolated architecture and documented threat model.
Autonomous actions affecting records or customers High-risk approval with a human checkpoint. Least privilege, transaction limits, complete audit trail, rollback and incident response.
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When confidential computing is worth considering

Confidential-computing architectures use hardware-based protections to help protect data and models while they are in use. Azure’s Confidential AI documentation describes this as a specialized option for defined threat models and requirements. It can be relevant when an organization must reduce exposure to infrastructure operators or meet stringent isolation objectives.

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It does not make an unsafe workflow safe. You still need correct source permissions, data minimization, provider and subprocessor review, secure keys, DLP, retention rules, monitoring and an incident plan. Choose it only after documenting the adversary, regulatory requirement, performance and operational costs it addresses.

Test, monitor and revise the controls

Documented settings are not proof that a control works. Run a controlled test using synthetic or authorized test data.

  1. Create test users with different roles and source permissions.
  2. Submit labeled test secrets through copy and paste, upload, connector retrieval and agent actions.
  3. Confirm that DLP warns or blocks the intended channels and records an auditable event.
  4. Verify that denied users cannot retrieve restricted content and that revoked access takes effect.
  5. Delete a test conversation and index entry, then check histories, caches, embeddings and logs for residual content.
  6. Review web-search, browser and third-party integration behavior separately from the core model.
  7. Record results, exceptions, owners and remediation dates; repeat after product, plan or policy changes.

Monitor approved use to the extent allowed by law and policy. Review exceptions, access anomalies, bulk extraction, repeated DLP events and changes to provider terms. Refresh employee training and the approved-tool register on a defined schedule and after significant feature or contract changes.

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What to do after an accidental disclosure

  1. Stop further sharing: end the session, disable the connector or revoke the exposed credential if applicable.
  2. Preserve relevant audit records without copying the sensitive content into new locations.
  3. Notify the security or privacy incident channel immediately, following your organization’s escalation policy.
  4. Identify the exact data, recipients, retention state, integrations and jurisdictions involved.
  5. Request deletion or containment from the provider where available, while recognizing that deletion may not remove every backup or downstream copy.
  6. Rotate credentials, restrict permissions and inspect logs for reuse or extraction.
  7. Document the root cause and update policy, DLP rules, training or service approval before restoring the workflow.

Do not conceal an incident because a provider says it does not train on customer data. Training status does not determine whether a prompt was retained, viewed by an authorized administrator, passed to a connector or exposed through an over-broad permission.

A compact approval checklist

  • The data classification and business purpose are documented.
  • The service, plan, tenant and enabled features are named.
  • Training, retention, deletion, access, geography and subprocessors are understood.
  • Source permissions and sensitivity labels have been reviewed and tested.
  • DLP and classification cover the actual devices, browsers, APIs and integrations.
  • Histories, indexes, embeddings, memory, caches and logs have owners and retention rules.
  • Agents and browser functions have least privilege, approval gates and audit logs.
  • Security, privacy, legal, compliance and the business owner have approved high-risk uses.
  • A test demonstrates that the controls work, and a re-review date is recorded.

The safest default for an employee is simple: if the tool and workflow are not explicitly approved for that data class, do not submit the confidential material. Use a redacted or synthetic example and ask for review through the organization’s designated process.

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