Protect company data in generative AI tools by setting clear rules for what employees may submit, requiring approved services and accounts, checking each service’s retention and data-use settings, and limiting what the tool can access. Use permissions, sensitivity labels, data loss prevention (DLP), and monitoring where available. A promise not to train on your data is useful, but it does not mean the service never processes or stores it.
What makes generative AI a company-data risk?
Information can be exposed not only by pasting an obvious secret into a prompt, but also through uploaded files, connected repositories, web searches, agents, and generated responses. AI systems may also infer sensitive details by combining information that appears harmless in isolation. NIST’s 2024 Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile warns that “Models may leak, generate, or correctly infer sensitive information about individuals.” Treat prompts, attachments, connected sources, and outputs as data flows that need controls.
Set rules for what employees may enter
Write rules in terms employees can apply before they paste, upload, or connect information. Identify the company’s restricted categories and give examples such as credentials, customer records, personal data, unreleased product details, financial information, and confidential contracts. These are practical policy examples, not a universal legal classification. Define any approved exceptions and name the person or team authorized to approve them.
Cover information that could identify someone or reveal a secret when combined with other facts—not just direct identifiers. A short excerpt or pseudonymized record may remain sensitive if it can be linked back to a person or company project.
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Minimize and sanitize prompts and uploads
Before using an AI tool, ask whether the task requires the underlying sensitive material. If not, remove names, account numbers, credentials, customer details, and proprietary specifics. Use a summary, abstracted example, or synthetic data when it can produce a useful answer. Do not assume replacing a name with a code makes information safe if the code can be mapped back or combined with other data.
Approve the service and its exact configuration
Review the product, account type, plan, contract, and features employees will actually use. Record how the service handles prompts, outputs, uploaded files, feedback, processing, retention, deletion, and administrator access. Assess web search, agents, plugins, retrieval, and stateful features separately; a general statement about a vendor may not describe every plan or feature.
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For example, OpenAI says business-service inputs and outputs are not used to improve models by default. For individual services, use for improvement depends on settings, and submitting feedback can result in the associated conversation being used. Check the current terms and settings for the specific service and account at OpenAI’s data-use documentation.
Microsoft says Copilot data protections and controls vary by subscription, and web search queries have handling distinct from prompts and Microsoft Graph data. Its enterprise data protection documentation explains those distinctions. For Microsoft Foundry, optional features can persist history or other content depending on configuration; consult the Foundry data, privacy, and security documentation before enabling stateful features.
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Check access before connecting company data
Review permissions in repositories and business systems before making them available to an AI feature. Remove stale access and broad groups, and grant only what users and services need. A tool that respects a user’s existing permissions can still return information that user should not have been able to access in the first place.
In Microsoft’s ecosystem, Copilot can inherit identity permissions and sensitivity labels, but specific controls vary by subscription. Microsoft describes these protections in its enterprise data protection guidance. They help enforce configured access; they do not replace reviewing whether the underlying permissions are appropriate.
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- Check FIDO2 compatibility before purchase - Known limitations: ID Austria is not supported (requires FIDO2 Level 2). Windows Hello login only works with Windows Enterprise editions that support Entra ID. NFC functionality is not supported.
Use DLP, labels, audit, and retention controls where supported
Use your existing data-classification and security tools to prevent risky sharing where the product and platform support it. Depending on your configuration, controls may warn or block attempts to send sensitive content to third-party AI sites, apply sensitivity labels, or record supported AI interactions for review. Set audit and retention to fit company policy and applicable obligations, and verify which apps, devices, and interaction types are covered.
Microsoft documents capabilities and scope limits for Purview protections for Microsoft 365 Copilot and other generative AI apps and Endpoint DLP. Availability depends on supported products, platforms, and configuration; do not assume a control covers every AI service or route for sharing data.
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Train staff and prepare for accidental disclosure
Show employees which tools and accounts are approved, which information is prohibited, how to sanitize a prompt, and how to report an accidental disclosure. Include AI use in vendor reviews and incident-response procedures. Make the reporting route easy to find and test it with realistic scenarios, such as a customer record pasted into an unapproved account.
Explain that model-level safeguards are not a substitute for sound permissions or data minimization. Microsoft notes that prompt-injection protections can vary by scenario in its Copilot privacy and security documentation. Do not rely on a model to recognize every sensitive input or prevent every unsafe response.
Compare services using the same questions
When evaluating multiple products or deployments, use a consistent checklist rather than relying on a broad “enterprise” or “no training” label.
- Training and improvement: Are prompts, outputs, files, or feedback used to improve models? Which settings and account types change that?
- Processing and retention: What is processed to provide the feature, what is stored, for how long, and how can it be deleted?
- Access and isolation: Which identity and permission model applies, and how are customer data and projects separated?
- Connected features: Do browsing, agents, retrieval, uploads, or stateful APIs create additional data paths or storage?
- Administration and oversight: Can the organization configure policies, monitor activity, investigate incidents, and apply retention controls?
- Safeguards: Which prompt-injection, abuse, and DLP controls apply to the exact plan and scenario?
- Contract and geography: Which contractual terms and processing locations apply to your organization and data? Resolve these against your jurisdiction and obligations rather than assuming a general vendor statement answers them.
This is general security and governance guidance, not a jurisdiction-specific legal assessment. Requirements depend on the organization, data, industry, deployment, and applicable law. Verify current service terms and settings when approving a tool and when its features or plan change.
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