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Protect company data in generative AI tools by setting clear rules for what employees may submit, approving specific services and configurations, limiting the information they share, and controlling access to connected data. Review each service’s processing, retention, deletion, feedback, and training terms: a promise not to train on prompts does not mean the service does not process or store them.
What counts as sensitive data in an AI workflow?
Consider every prompt, upload, connected source, and generated answer part of a data flow to manage. Sensitive information is not limited to names or account numbers: a collection of otherwise ordinary facts may reveal something private or confidential when combined. NIST notes that generative AI models may “leak, generate, or correctly infer sensitive information about individuals” in its 2024 Generative AI Profile.
As a starting point for company policy, identify categories such as credentials and access tokens, customer records, personal data, financial information, confidential contracts, and unreleased product or business plans. These are practical examples, not a universal legal classification. Define any approved exceptions and name the person or team employees should ask when a legitimate task appears to require restricted information.
How should employees minimize what they submit?
Before pasting text, uploading a file, or connecting a source, ask whether the task requires the underlying sensitive details. If it does not, remove or replace names, identifiers, secrets, customer specifics, and proprietary information. A brief summary or synthetic example may be enough. Treat pseudonymized data as potentially sensitive if it can be linked back to a person or combined with other information to identify them.
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Apply the same caution to connected data and outputs. An AI feature may draw context from repositories or other sources, and an answer may reveal information present in that context. Avoid providing a broad data set just because a tool can accept it.
How to approve a generative AI service and configuration
Approve a defined service, account type, plan, and configuration—not just a vendor name. Before employees use it for company work, document the terms and controls that apply to the features they will actually use.
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- Training and feedback: Determine whether prompts, outputs, uploaded files, or user feedback may be used to improve models, and which settings or contract terms govern that use.
- Processing and retention: Establish what the service processes to provide the feature, what it stores, for how long, and how deletion works.
- Access and isolation: Review the identity and permission model, including how tenant or project data is separated.
- Connected features: Check uploads, retrieval, agents, plugins, external browsing, and stateful APIs separately; each may introduce another data path or storage behavior.
- Administration and oversight: Confirm which policy settings, activity records, audit features, and retention controls administrators can use.
- Contract and geography: Check the terms and processing locations that apply to your organization, jurisdiction, and data categories. Requirements depend on those specifics.
Do not assume a general vendor statement covers every plan or feature. For example, OpenAI says inputs and outputs from its business services are not used to improve models by default, while use of individual-service content depends on settings; feedback can have separate effects. Check the current OpenAI data-use documentation for the particular service and account in question.
Microsoft says enterprise data protection controls for Copilot vary by subscription. It also documents separate handling for web search queries compared with prompts and Microsoft Graph data. In Microsoft Foundry, optional features can persist conversation history or other content according to configuration. These examples illustrate why processing, retention, and connected features should be checked individually in the applicable Microsoft Copilot enterprise data protection documentation and Foundry data, privacy, and security documentation.
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How to control access to connected company data
Review permissions in the source systems before enabling an AI connection. Remove stale access and broad permissions, and apply least privilege so users and services can reach only the data required for their work. An AI service that follows a user’s permissions may still surface material that the user can already access; the organization needs to ensure those permissions are appropriate first.
In Microsoft’s ecosystem, Copilot can inherit the user’s identity and permissions, sensitivity labels, retention policies, and administrative settings, with specific controls varying by subscription. See Microsoft’s enterprise data protection documentation and Purview protections for generative AI apps for the scope of those controls.
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How to use DLP, labels, auditing, and monitoring
Where your products support them, use data classification and sensitivity labels to guide handling, and configure endpoint or cloud data loss prevention (DLP) to warn about or block risky sharing. Audit and retention features can help authorized teams investigate activity and apply internal policy. Microsoft documents Purview controls for Microsoft 365 Copilot and certain other generative AI apps, but availability depends on supported products, platforms, and configuration; check its Purview AI protections overview and DLP controls for Microsoft 365 Copilot.
Prompt-injection and jailbreak protections can reduce some risks, but their availability and coverage differ by scenario. Microsoft notes that safeguards may not apply uniformly across Copilot experiences. Treat them as one layer—not a substitute for correct permissions, data minimization, and controls over what users can submit. See Microsoft’s Copilot privacy and security documentation for its description of safeguards.
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How to prepare employees and respond to accidental disclosure
Give staff short, practical guidance that identifies approved tools and accounts, prohibited data categories, the approval route for exceptions, and how to report an accidental submission. Explain how to review and handle generated content as well as what may be entered into a tool. Include AI use in vendor review and incident-response procedures, and test the reporting path with realistic scenarios so employees know what to do when a mistake happens.
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