Reduce workplace AI data leakage by deciding what employees may share, approving services only after reviewing their data protections, minimizing prompt content, and limiting what connected AI tools can access. Add classification, data-loss prevention (DLP), monitoring, and audit controls where your services support them. These measures reduce risk; none guarantees that disclosure is impossible.
Start with a rule employees can use before sharing data
Set a clear rule for what information may be entered into each approved AI service. Apply it to pasted text, uploaded files, and connected workspaces—not only to prompts typed into a chat box. The exact data categories and exceptions depend on your organization’s information classification and obligations.
Tell employees which tools and accounts are approved, which data must be redacted or must not be shared, and whom to contact when a service’s terms or protections are unclear. Include a simple reporting route for accidental disclosures. Tailor the rule to your organization’s privacy, employment, and sector requirements; those cannot be determined without knowing its jurisdiction, industry, data, and deployment.
Use the minimum information needed
Before submitting a prompt or file, remove names, account numbers, customer details, and other identifiers that are not necessary to complete the task. Where possible, substitute synthetic examples or summaries for source records. A policy is more useful when it tells employees how to get the task done with less sensitive information, rather than relying only on a broad instruction to “be careful.”
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Review the service and the specific plan before approval
Do not assume a service is suitable for every confidential or regulated task just because it is marketed for business. Review the applicable data-processing terms and product commitments, how prompts and responses are handled, retention settings, identity and permission behavior, audit availability, and the permissions requested by integrations or plugins. Confirm the actual account, plan, and configuration employees will use.
Microsoft’s Microsoft 365 Copilot data-protection documentation describes controls such as access controls, sensitivity labels, retention, auditing, and administrative settings, while noting that available controls depend on the underlying subscription. This is a product-specific example, not a universal guarantee or an independent assessment of every AI service. Microsoft’s LLM security planning guidance also recommends checking whether an operator’s policies align with your organization’s data-protection policies.
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Classify information and fix broad access before enabling AI
AI systems that retrieve or act on company information can expose data their users or connected services can already reach. Label sensitive information and review permissions on repositories, shared drives, and other sources before enabling retrieval. Apply least privilege: users and AI-connected services should have only the access needed for their work.
Microsoft recommends discovering AI workloads and SaaS AI applications, applying sensitivity labels and DLP policies, and auditing use in its Purview guidance for AI. Its Purview documentation also describes classification and monitoring for supported AI interactions. These are vendor capabilities; availability and behavior depend on product support, tenant configuration, and subscription.
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Use DLP to warn or block risky prompt sharing
DLP can provide a practical guardrail when an employee attempts to paste sensitive information into an AI site. For example, Microsoft documents Purview endpoint DLP policies that can warn or block users from sharing sensitive information with third-party generative-AI websites accessed through a browser. Its example involves credit-card numbers and ChatGPT; do not assume that the same control covers every sensitive-data type, browser, endpoint, or AI service.
- Check coverage: Confirm that your Purview capabilities, endpoint coverage, browsers, and target AI services support the policy you intend to use.
- Define detection and action: Select the sensitive information types and decide whether the policy should warn, block, or allow an override, as supported in your deployment.
- Test with representative data: Verify the result on the endpoints and services employees actually use. Check for false positives and understand how overrides are handled.
- Review the policy: Use alerts and audit records to determine whether it is catching the activity you intend to control, then adjust it as needed.
Microsoft’s Endpoint DLP documentation describes the capability. Verify current platform support and tenant configuration before relying on a particular warning or block.
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Secure connected AI tools, agents, and integrations
A prompt is not a safe place to store a secret or enforce an access rule. Do not put credentials, connection strings, access rules, or other secrets in system prompts: prompts may be exposed and should not serve as a security control.
Restrict the data and permissions available to repositories, APIs, plugins, and agent functions. Limit external access and outbound operations, and require human approval for high-impact actions such as transferring company data outside the organization. These controls matter especially when an AI application can retrieve information or take actions, rather than only generate a response.
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Compare services and controls against your deployment
Use the same questions to assess candidate services, policies, or configurations. Product coverage can differ, so verify answers for your own deployment rather than treating a feature name as proof of protection.
| What to compare | Questions to ask |
|---|---|
| Data handling and contract | How are prompts and responses handled? What are the retention and training commitments? Which processing terms and service-specific protections apply? |
| Identity and access | Does the service use your organization’s identity model? Does it respect source permissions and sensitivity labels? Can access be limited to least privilege? |
| Prevention coverage | Which data types, browsers, endpoints, and AI services can be detected? Can policies warn, block, or allow an override? |
| Visibility | Can administrators monitor user interactions, review audit records, receive alerts, investigate activity, and retain records for the required period? |
| Administration | What configuration, licensing, and platform dependencies apply? How will policy exceptions be handled? |
Monitor use and improve the controls
Use available discovery, activity, audit, and alerting tools to see which AI services employees use and investigate risky behavior. Review detections and exceptions, check whether controls behave as intended, and revise policy or configuration when the evidence shows a gap. Microsoft’s Purview AI guidance and LLM security planning guidance describe discovery, monitoring, and auditing approaches.
For a broader risk-management reference, NIST’s AI Risk Management Framework Generative AI Profile (NIST AI 600-1), released July 26, 2024, helps organizations identify generative-AI risks and select management actions aligned with their goals and priorities. NIST’s SP 800-218A, published in July 2024, adds generative-AI-specific secure-development practices to the Secure Software Development Framework and is aimed at model producers, AI system producers, and acquirers.
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