The safest employee rule is simple: use only company-approved AI services and accounts, and never submit information classified as confidential, restricted, regulated, or secret unless the specific tool and use case have explicit approval. That rule must cover prompts, uploaded files, connected drives, plugins, agents, and integrations—not only pasted text.
No single policy, vendor promise, or technical control closes every route. Effective protection combines clear allowed-use rules, least-privilege access, data classification, supported DLP controls, vendor due diligence, user training, and continuous monitoring.
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Define what employees may and may not submit
Write an acceptable-use policy that employees can apply while doing ordinary work. NIST’s Generative AI Profile identifies acceptable-use policies and practical guidance as ways to reduce misuse and inappropriate repurposing. The profile is voluntary guidance; it does not replace privacy law, sector rules, employment obligations, or customer contracts.
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Tell employees to use approved services, approved company accounts, and approved workflows. Unless a documented exception exists, prohibit sending information labeled confidential, restricted, regulated, or secret to an AI tool. Your organization must define those labels because classification schemes differ by company and jurisdiction.
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Include every common input path:
- Text typed into a prompt or pasted from another application
- Documents, spreadsheets, images, source code, and log files uploaded for analysis
- Connected repositories such as shared drives, intranets, email, ticketing systems, or code platforms
- Plugins, extensions, custom connectors, agents, and automated workflows
- AI features embedded in another business application
- Personal accounts, consumer applications, desktop clients, mobile apps, and browser sessions
Specify when human approval is required
Require a named owner or privacy/security review before an AI workflow handles personal data, payment information, credentials, customer-confidential material, export-controlled information, regulated records, unreleased intellectual property, or data drawn from multiple systems. Approval should identify the service, account or tenant, data categories, purpose, retention setting, and permitted users.
Give employees a safe alternative
A prohibition without a practical route often drives unsanctioned use. Publish an approved-tool list, the allowed use cases for each service, and a clear place to request an exception. For tasks that do not require real records, show employees how to use synthetic examples, redacted text, masked identifiers, or fabricated names and values. Redaction is useful only when the remaining details cannot be combined to identify a person, customer, project, or confidential transaction.
Reduce access before adding an AI assistant
An AI assistant can retrieve information that a user or connected service is already authorized to access. If a shared site contains excessive permissions, adding an assistant does not repair that exposure; it can make discovery faster and easier.
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Review permissions on shared sites, folders, mailboxes, repositories, and business applications before enabling AI retrieval. Give each employee, service account, connector, and agent only the access required for its role. Separate high-sensitivity repositories from broadly shared workspaces, and avoid granting an assistant a whole repository when a limited collection meets the use case.
Make access review part of the lifecycle
Remove or reduce access when someone changes role, leaves a project, or leaves the organization. Use scheduled access reviews, joiner-mover-leaver workflows, and expiration dates for temporary permissions. Microsoft Entra guidance recommends granular access policies, lifecycle workflows, access reviews, Conditional Access, compliant-device requirements, and phishing-resistant multifactor authentication options for governing AI applications as corporate resources.
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Secure the account and device path
Require company identities for approved services and block or restrict personal accounts where your risk model demands it. Apply Conditional Access and device-compliance rules to sensitive workflows. Decide explicitly whether browser, desktop, and mobile clients are in scope; a control that covers a managed browser on a company laptop may not cover an unmanaged phone or a locally installed application.
Understand what permission-respecting assistants do—and do not do
Microsoft says organizational Copilot protections include identity and permission enforcement, sensitivity-label inheritance, retention policies, audit support, and administrative settings. The company also states that prompts and responses are covered by its enterprise terms and that specific controls vary by subscription. Agents can have separate privacy statements and terms.
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These protections are useful only when the underlying permissions and labels are correct. If a user can already open an over-shared file, an assistant that respects that permission may still summarize or surface the file. Treat permission hygiene, labeling, and access reviews as prerequisites rather than assuming the assistant creates a new security boundary.
Classify sensitive content and use DLP where it is supported
Define organization-specific sensitive information types and labels for the data you need to protect. Examples can include payment-card numbers, government identifiers, health information, credentials, source code, customer records, acquisition plans, and unreleased product material. Classifications should reflect your legal, contractual, and operational obligations, not a generic list copied from another company.
Use browser and endpoint controls for the paths they cover
Microsoft Purview documents endpoint DLP policies that can warn or block a user who shares sensitive information with third-party generative AI sites through a browser on an onboarded Windows computer. Its example is a user pasting credit-card numbers into ChatGPT. That description is narrower than “all AI leakage”: verify the supported sites, browsers, operating systems, device enrollment, licensing, and policy configuration in your environment.
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Distinguish a warning from a block
A warning can educate a user and may be overridable; a hard block prevents the configured action. Decide which data classes require blocking and which can use a justified override with a business reason. Test both outcomes with harmless test records before relying on them, and confirm that alerts contain enough context for an investigator without exposing the sensitive value again.
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Document whether your policy covers:
- Managed browsers versus desktop and mobile applications
- Company-managed versus unmanaged devices
- Corporate accounts versus personal accounts
- Copied text, file uploads, screenshots, and generated exports
- Connected repositories, plugins, agents, and API-based automations
- Home networks, virtual desktops, and offline work followed by later synchronization
The available product guidance does not establish universal coverage for every AI application, operating system, network path, or client. Use your own telemetry and controlled tests to establish the actual boundary.
Assess each AI service and plan before approval
Review the exact product, account type, subscription, region, and enabled features. An “enterprise” label is not a universal guarantee: contractual terms, retention controls, administrative features, and agent behavior can vary by plan and feature.
Record the service’s data-handling answers
Procurement, security, privacy, and legal reviewers should document:
- What data is sent, for which purpose, and through which client or API
- Whether prompts, files, outputs, metadata, and abuse-monitoring copies are retained
- Deletion timing, backup handling, legal holds, and customer-controlled retention settings
- Whether data is used to train or improve models, and how an opt-in or opt-out is recorded
- Which employees, support teams, subprocessors, or systems can review the data
- Tenant isolation, encryption, residency and transfer locations, and key-management options
- Connected-service, plugin, connector, and agent permissions
- Audit logs, administrator controls, incident notification terms, and evidence available for investigations
- Service-level commitments and independent attestations relevant to your risk
NIST calls out privacy, intellectual-property, and information-security risks in third-party generative-AI integrations. It recommends risk-based due diligence through existing processes, including review of relevant service-level and attestation information.
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Read vendor promises narrowly
Microsoft describes organizational Copilot as using tenant isolation, encryption, enterprise privacy commitments, existing access controls, retention and audit controls, and no foundation-model training on prompts, responses, or Microsoft Graph data. Those statements are tied to the organizational offering and Microsoft notes that controls vary by subscription. An agent connected to the service may publish separate terms.
An OpenAI update dated September 22, 2026 describes zero data retention (ZDR) for eligible API customers: prompts and responses are not retained after processing and are not available to OpenAI personnel for review. Eligibility and exceptions apply, including an exception concerning potential child sexual abuse material images. The announcement also states that enterprise customer data is not used to train models unless the customer opts in. Confirm eligibility and the current developer documentation for the specific account; do not generalize ZDR to consumer accounts or every OpenAI product.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.“Enterprise AI adoption depends solely on customer control of data, with no direct or derivative use beyond the chosen service. OpenAI’s no-training commitment and ZDR give Glean confidence to build with OpenAI. As models become more capable, OpenAI shows safety can advance without compromising the privacy and control that sustain enterprise trust.”
— Sunil Agrawal, Chief Information Security Officer at Glean, in a customer testimonial attributed by OpenAI
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Match controls to the leakage path
Different controls act at different points. Use the following as a design comparison, then verify the implementation in your own environment.
| Control | Where it acts | Primary coverage | Enforcement strength | Important qualification |
|---|---|---|---|---|
| Acceptable-use policy and training | Employee decisions and approved workflows | Prompts, uploads, connected sources, agents, integrations, and account choice | Education and administrative accountability | Requires clear classifications, examples, exception handling, and periodic refreshers |
| Identity, permissions, and device access | Accounts, repositories, applications, and endpoints | Who can use a service or retrieve enterprise content | Access prevention, conditional access, and review | Does not correct overshared content or inspect every prompt |
| Classification and endpoint DLP | Content and supported browser or endpoint actions | Identified sensitive data shared with supported third-party AI sites | Audit, warning, overrideable warning, or block | Coverage depends on supported sites, devices, clients, enrollment, licensing, and policy settings |
| Vendor retention and training terms | Provider processing and storage | Retention, deletion, model-training use, review access, and tenant isolation | Contractual and configuration controls | Terms differ by product, plan, feature, eligibility, and exceptions |
| Monitoring and investigation | Identity, DLP, application, and service telemetry | Discovery of use, policy violations, exceptions, and suspected incidents | Audit and response rather than automatic prevention | Available signals and retention depend on deployment and licensing |
Train employees to recognize risky material
Training should use examples from the employee’s actual work. Show that sensitive data is not limited to a single field: a combination of project name, dates, customer details, and unusual technical facts can identify a confidential matter.
Use recognizable examples
- Customer records and support transcripts
- Passwords, API keys, access tokens, and private certificates
- Source code, vulnerability reports, and internal logs
- Unreleased financial results, product plans, acquisition discussions, or contract terms
- Employee health, payroll, performance, or government-identification data
- Any document whose combination of details could identify a person, customer, or project
Teach a safe transformation workflow
- Identify the purpose of the task and whether real company data is necessary.
- Use the approved service and company account for that purpose.
- Remove unnecessary fields, names, identifiers, secrets, and unique contextual details.
- Replace values with synthetic equivalents when the task is about format, reasoning, or code structure rather than the underlying record.
- Check the output for copied sensitive content before sharing or storing it.
- Report a mistaken submission immediately through the organization’s incident channel; do not wait to see whether the service produces an alarming response.
Monitor use and keep the program current
Review discovery data for unsanctioned AI applications, personal accounts, unusual connectors, and high-risk data transfers. Investigate DLP, identity, and insider-risk alerts according to documented severity and privacy procedures. Microsoft describes discovery, monitoring, and insider-risk capabilities in its Purview guidance, but the exact features depend on deployment.
Maintain an approved-tool register
For every approved service, record the owner, permitted data classes, allowed clients and regions, subscription, retention and training settings, connected systems, review date, and decommissioning process. Remove tools that no longer have a business owner or whose terms no longer meet requirements.
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Recheck after product changes
Reassess controls when a provider changes its terms, adds an agent or connector, introduces a new client, changes retention behavior, or expands model-training settings. Re-test warnings and blocks after policy, licensing, operating-system, browser, or application updates.
Respond when data may already have been submitted
Employees need a short, non-punitive reporting route so a mistake is disclosed quickly. The response team should preserve the prompt or upload details, account and client used, time, recipients, connected sources, and relevant service terms without copying the sensitive content into additional systems.
Contain and assess
- Ask the user to stop the workflow and avoid further prompts that repeat the material.
- Revoke or rotate exposed credentials, tokens, keys, or links immediately.
- Restrict the affected account, connector, agent, or repository while access is reviewed.
- Determine what data was sent, whether it included personal or regulated information, and which provider, plan, region, and retention settings applied.
- Request deletion or exercise contractual incident procedures where available, while recognizing that deletion assurances may have conditions or exceptions.
- Escalate to privacy, legal, compliance, customer-notification, and security teams according to the applicable obligations.
- Record the control gap and update policy, permissions, DLP coverage, or training rather than treating the event as an isolated user error.
A practical rollout sequence
- Inventory: identify approved and discovered AI services, accounts, clients, connectors, agents, and data sources.
- Classify: define the company’s sensitive data categories and the permitted AI handling for each category.
- Control access: remediate overshared repositories, apply least privilege, require managed identities and devices where appropriate, and schedule access reviews.
- Approve services: complete product- and plan-specific privacy, security, retention, residency, subprocessor, and incident reviews.
- Configure prevention: deploy supported labels, DLP rules, warnings, blocks, Conditional Access policies, and audit collection; test with synthetic records.
- Train and publish: provide the approved-tool list, safe transformation examples, exception route, and rapid reporting channel.
- Measure and revise: review telemetry, exceptions, false positives, incidents, product changes, and license coverage on a defined schedule.
The goal is not to promise that leakage is impossible. It is to make unsafe use difficult, approved use understandable, sensitive access narrow, provider handling explicit, and mistakes quickly discoverable and containable.
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