AI governance requires more than internal use rules

Hello, this is Legal Agent.

When companies start using generative AI internally, the first instinct is usually to write an AI usage policy. That is the right starting point, but treating it as the whole of AI governance is risky, since AI use touches information management, personal data, copyright and board-level risk oversight all at once.

Rules alone do not cover the real cases

A policy that just lists prohibitions, such as no confidential information, no personal data, don't trust the output blindly, is a reasonable start, but the risk actually depends on the tool: summarizing a client contract on a managed enterprise plan with training off is a different question from pasting the same contract into a free personal account. Risk also varies by function, so use rules need to be paired with the actual workflow.

Separate input, output and ownership

Governance should classify input data by the impact of a leak, whether personal data, trade secrets, unpublished financials or a client's contract, rather than lumping everything together as "confidential." It should also decide who checks AI-generated output before it goes external, and who owns that judgment.

Read the AI vendor's own terms

Governance also means checking the AI service's own contract: whether inputs train the model, where data is stored, what logs exist, who the subprocessors are, and how liability is allocated if something goes wrong. These points matter directly against a company's own confidentiality and data-transfer obligations to its clients.