To reduce data leakage from workplace generative AI, set clear rules for what employees may share, approve services only after reviewing their protections, minimize prompt data, and restrict what connected AI tools can access. Add data classification, DLP, monitoring, and audits where supported. These controls reduce risk; none can guarantee that disclosure is impossible.
Start with a rule employees can follow
Tell employees which AI tools and accounts are approved, which data categories may be entered into each, and what to do when a tool’s terms or protections are unclear. The exact categories and exceptions depend on your organization’s data and obligations.
Make the rule practical for everyday tasks: use the minimum information needed, redact identifying or confidential details when possible, and do not paste or upload material that the policy prohibits. Explain how to report an accidental disclosure so the organization can respond.
Have privacy or legal staff map the organization’s actual uses to applicable requirements. The relevant rules depend on jurisdiction, industry, data types, and deployment; there is no universal legal answer.
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Review each AI service and its configuration
Do not assume that a service is suitable for confidential work simply because it is labeled “business” or “enterprise.” Review the applicable data-processing terms and commitments, how prompts and responses are handled, retention settings, identity and permission behavior, audit availability, and any plugin or integration access.
Microsoft’s Microsoft 365 Copilot data-protection documentation describes protections such as access controls, sensitivity labels, retention, auditing, and administrative settings. The available controls depend on the underlying subscription, so verify the organization’s specific plan and configuration rather than treating the list as universal.
For any provider, confirm that its policies and service configuration align with your organization’s data-protection rules. Review connected integrations as part of the same approval: an AI service’s risk depends not only on prompt handling but also on what it can retrieve or do.
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Classify sensitive data and fix access before enabling AI
AI features that search company files can expose information a user or service account can already reach. Before enabling retrieval or workspace connections, label sensitive data and correct overly broad permissions. Least-privilege access limits the potential exposure if an AI tool retrieves information unexpectedly.
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Use DLP for prompts where it is supported
Data loss prevention can provide a practical guardrail against sensitive content being pasted into AI sites. Microsoft documents Purview endpoint DLP policies that can warn or block users from sharing sensitive information with third-party generative-AI sites through a browser.
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Coverage depends on the supported data types, browser, endpoint, AI site, tenant configuration, and subscription. Microsoft’s documented example is a warning or block when a user pastes credit-card numbers into ChatGPT; do not assume that this specific example means every sensitive-data type or platform can be blocked. Check the current Purview guidance and test policies with representative data before relying on them.
Review false positives and override behavior as well as successful blocks. A warning can prompt better decisions, but it is not the same as preventing disclosure. Combine DLP with clear rules, employee training, and an escalation path.
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Limit what connected AI applications can access and do
Apply least privilege to repositories, APIs, plugins, and agent functions. Grant access only to the sources and actions needed for the task, restrict external access and outbound operations, and require human approval before high-impact actions such as transferring data outside the organization.
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Never put passwords, credentials, connection strings, access rules, or other secrets in a system prompt. Prompts may be exposed, and instructions in a prompt are not a security control. Microsoft’s LLM security planning guidance recommends applying protections to the system around the model, including its access and operations.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Monitor use and improve controls
Use available discovery, activity, audit, and alerting tools to understand which AI services employees use and how they interact with them. Investigate risky activity, review exceptions, and adjust policies when controls miss a meaningful exposure or block legitimate work.
The Purview AI security guidance describes monitoring and auditing AI interactions, while Microsoft’s LLM security planning guidance addresses AI application security. Product capabilities depend on the organization’s configuration and subscription.
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For a broader risk-management framework, NIST’s AI Risk Management Framework: Generative Artificial Intelligence 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. For organizations involved in AI software development or acquisition, NIST SP 800-218A, published in July 2024, adds generative-AI-specific secure-development practices to the Secure Software Development Framework.
Compare controls before choosing a service
When evaluating services or safeguards, compare the areas that determine actual exposure and enforceability:
| Area | What to verify |
|---|---|
| Data handling and contract | Prompt and response use, retention, training commitments, processing terms, and service-specific protections. |
| Identity and access | Whether the service respects organizational identity, source permissions, least-privilege design, and sensitivity labels. |
| Prevention coverage | Which sensitive data types, browsers, endpoints, and AI services can be detected, warned on, or blocked. |
| Visibility | Whether user-level interaction monitoring, audit records, alerts, investigation, and retention meet organizational needs. |
| Administration | Configuration complexity, licensing dependencies, supported platforms, and policy exceptions. |
Verify these details for the organization’s deployment. Vendor documentation describes a product’s capabilities, not an independent guarantee that a particular configuration prevents leakage.
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