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Use only organization-approved AI services for confidential work, and share the least sensitive information needed to complete the task. Before employees submit prompts, files, or connected-workspace content, the organization should review the specific service and plan’s data-use terms, settings, security controls, and contractual commitments. Redaction, synthetic examples, and restricted access can reduce exposure, but they do not guarantee trade-secret protection.
Why AI use can put trade secrets at risk
An AI tool used at work is also a third-party information system. Prompts, uploaded files, and content retrieved through connected applications may disclose business information to the provider or other services involved in processing it. A private-looking chat interface is not proof that its contents remain confidential. The FTC has described customers revealing sensitive or confidential information, including internal documents and user data, to model-as-a-service companies.
A trade secret is not defined by a label alone. The USPTO says information must have actual or potential independent economic value because it is not generally known, derive value from others’ inability to ascertain it through proper means, and be subject to reasonable efforts to maintain its secrecy. It states that all three elements are required. The USPTO’s trade secret policy also says protection has no set time limit only while those elements continue to exist.
That makes disclosure controls important: sending valuable information to a service without suitable confidentiality commitments and safeguards may create a problem for the secrecy element. But an AI prompt does not automatically waive trade-secret protection. The legal result depends on the facts and applicable law.
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Can I paste confidential company information into ChatGPT?
Do not paste it into ChatGPT—or any other AI service—unless your organization has approved that specific service, account or plan, and data use for the information involved. “ChatGPT” is not one uniform set of terms: product, account type, settings, and current provider commitments matter. Check the governing terms and controls that apply to your organization’s actual service rather than assuming that a consumer-facing interface or a paid account is suitable for confidential work.
If no approved route exists, use a non-sensitive description of the task, remove identifying details, or create a synthetic example. If the work still requires the secret itself, stop and ask the appropriate legal, security, or procurement contact for guidance.
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What to check before approving an AI service
Have legal, security, and procurement review the exact product, plan, terms, and settings. FTC guidance says providers must honor data-use promises made in terms, promotional materials, and other customer-facing contexts. The questions below are a practical due-diligence checklist; they are not claims about what any particular provider currently does.
- Training and improvement: Are prompts, uploaded files, and outputs used to train or improve shared or customer-specific models?
- Retention and deletion: How long is content retained, and what deletion controls apply to provider-held copies?
- Human access: Who may access customer content, in what circumstances, and what logging or safeguards apply?
- Other recipients: Do subcontractors, connected services, or integrations receive prompts or files?
- Administrative controls: Can the organization centrally restrict users, integrations, permissions, and data sharing?
- Written commitments and oversight: What confidentiality and security obligations apply by contract, and how will the organization verify that the provider follows them?
Review the provider’s actual terms and settings, not marketing language alone. The FTC’s January 2024 guidance on AI companies’ privacy and confidentiality commitments emphasizes that providers must honor their promises, including promises about using customer information to train or update models. The FTC’s Start with Security guide for businesses recommends written security requirements for service providers and follow-up verification.
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Set a practical workplace policy
Before employees use AI
- Inventory sensitive information employees handle, such as source code, formulas, product roadmaps, pricing, customer lists, designs, processes, credentials, unpublished research, and confidential partner information. A “confidential” label can communicate handling expectations, but the label alone does not establish that information is a trade secret.
- Define which information classes may be used with which AI services. Name the approved service and account or plan, permitted data classes, allowed integrations, and a clear route for exceptions.
- Have legal, security, and procurement review applicable terms and settings, including training or improvement use, retention, human access, deletion, security measures, and subprocessors. Recheck the actual service and account because provider terms vary.
- Put confidentiality and security requirements in provider contracts where appropriate, and assign responsibility for checking compliance.
While employees use AI
- Share only what the task needs. Replace names, identifiers, exact figures, code, or formula components with placeholders if the tool can still produce a useful result.
- Use fabricated or synthetic examples for demonstrations and training when they work as well as real data. The FTC security guide identifies fictitious data as a way to avoid unnecessary exposure in training or development.
- Do not submit credentials, secrets, regulated information, or another party’s confidential data unless the organization has explicitly approved that use and relevant obligations permit it.
- Check what files, drives, repositories, and enterprise applications a connected AI feature or agent can access. Limit permissions to what the task requires; connected tools create additional routes to information.
- Review generated outputs before relying on them. Confidentiality safeguards do not establish that an answer is accurate or free of third-party rights concerns.
After use and as the service changes
- Follow organizational rules for chat history, uploaded files, exports, and deletion. Removing a visible chat does not, by itself, establish that every provider-held copy has been deleted.
- Train employees to recognize sensitive information, use approved tools, and follow need-to-know access rules. Periodically review access and provider compliance.
- Reassess controls when the provider changes its terms, features, account tiers, data settings, or integrations.
- If someone may have submitted a trade secret to an unapproved service, preserve relevant facts and promptly notify the organization’s legal and security contacts under its incident procedures. Do not assume a later deletion request restores secrecy.
Do AI tools train on my prompts?
There is no single answer for every service or plan. The applicable provider terms and settings determine what is promised for the particular account, and those commitments can vary. Check whether prompts, files, or outputs may be used for training or model improvement, as well as retention, access, and deletion rules. Do not infer that data is excluded from training—or that it is used for training—from the fact that an AI tool is available at work.
The FTC has said providers must honor their data-use commitments. Its commentary also notes that using competitively significant business-customer data can raise competition concerns; it does not say that all AI training on customer input is unlawful.
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Can using AI make us lose trade-secret protection?
It can create a risk, but it does not automatically eliminate protection. Under the USPTO’s summary, reasonable efforts to preserve secrecy are one of three required elements, alongside economic value from information not generally known and value tied to others’ inability to obtain it through proper means. Whether a particular disclosure undermines protection depends on what was disclosed, to whom, under what terms and safeguards, and the applicable law.
The cited legal material is U.S.-centered. State and other countries’ laws, customer or partner contracts, privacy rules, export controls, and sector-specific duties may add requirements. This article is practical risk guidance, not a legal determination for a specific disclosure.
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Why AI security controls need ongoing review
NIST identifies confidentiality, integrity, and availability risks in AI systems and their training and output data. Its AI security and resilience research page describes AI-specific control overlays as in development. That work is not a finished certification or a guarantee that a service is secure. Organizations should therefore review their own controls and provider arrangements as services and guidance evolve.
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