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Employees using tools like ChatGPT or Claude in their daily work boosts productivity, but without controlling what data goes into these tools and how it's processed, there's a real risk of corporate data leakage. This guide offers a practical framework, from enterprise plan differences to a safe usage policy.
Free or individual plans for ChatGPT and Claude are subject to different data-handling rules than enterprise (Team/Enterprise/Business) plans.
On individual plans, chat history may be used for model training by default, while enterprise plans default to opting your data out.
Enterprise agreements include a formal contract defining what data is retained, for how long, and how it's processed.
Enterprise plans allow centrally managing employee accounts and signing in through your corporate identity provider.
Being able to track which user performed which action and when is necessary for security incident review.
Even if the tool itself is secure, the risk of a leak from human error remains if it's not clear what data employees can enter.
An employee might paste customer information or a contract's text directly into a personal account to get a quick answer.
Code snippets shared for debugging can end up stored in an account with no enterprise data protection policy.
Processing text containing personal data requires documenting the legal basis and retention period used.
Without a central policy, each team sets its own rules, which makes auditing difficult.
The goal isn't to ban AI use, but to clearly define what data can be used and how.
Classify in advance which data types (personal data, customer data, trade secrets) may be entered into AI tools.
Use an enterprise/business plan that offers data-training opt-out and a DPA instead of individual accounts.
Integrate employee access with your corporate identity provider for centralized account oversight.
Clearly document which data types must not be entered and which use cases are approved.
Publishing the policy alone isn't enough; give employees a short training session with concrete examples.
Periodically review usage logs on your enterprise plan to catch policy violations early.
Separately flagging personal data, financial data, source code and trade secret categories.
Checking whether data retention period, sub-processor list and data deletion request process are covered in the contract.
Scope, prohibited data types, approved use cases and a violation reporting process.
Verifying sign-in testing with your corporate identity provider (SAML/OIDC).
Monthly review of usage logs by the security team.
The notification and response process to follow if accidental sensitive data sharing is detected.
On individual/free plans this may be on by default and can be changed in account settings; on enterprise plans it's off by default and covered by contract. Always verify current settings against the provider's official documentation.
It can, but the legal basis, a Data Processing Agreement and a clear retention period are needed; when in doubt, not entering personal data is the safest approach.
Yes; features like opting out of data training, a DPA, SSO, centralized management and audit logs are generally only available on enterprise/business plans.
Generally not recommended; providing a clear policy and an approved corporate tool instead of banning reduces shadow-IT risk.
Not recommended without an enterprise plan and a proper DPA; for private/proprietary codebases, your company's own policy should be the deciding factor.
It depends on your company's risk profile; monthly review is reasonable for most organizations, with more frequent review for higher-risk sectors.
OpenAI's official page on enterprise data handling and privacy practices.
Official resource on Claude's enterprise security, compliance and data-handling practices.
An official reference on EU General Data Protection Regulation compliance.
Get in touch about KVKK/GDPR compliance, server security and self-hosted infrastructure options.
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