AI governance requires more than internal use rules

Hello, this is Legal Agent.

Organizations adopting generative tools often begin by publishing internal guidelines. While an internal use policy offers an initial reference, equating a written code with comprehensive governance leaves corporate exposure unaddressed. AI use can affect confidential information, personal data, intellectual property and the risks management needs to oversee.

Rules for the work people actually do

Generic prohibitions against inputting sensitive records, personal data, or relying unchecked on generated results provide incomplete safeguards. Exposure varies across platforms and commercial environments: reviewing client agreements under an enterprise subscription with model training disabled differs substantially from entering identical text into personal accounts. Plan titles or training opt-out toggles alone do not establish lawful handling of confidential or personal data. Operational risks depend on actual departmental workflows, so the policy should address those workflows.

Data classification and output accountability

Classify data by what a disclosure could affect. Personal records, trade secrets, unpublished financial information and client contracts may need different controls. Decide who checks AI output before it goes outside the company and who is responsible for the final decision.

Vendor contracts and client confidentiality obligations

The vendor review should cover model training parameters, server locations, audit trail retention, designated subprocessors, and contractual liability caps. These technical terms govern whether platform interactions align with existing client nondisclosure agreements, statutory cross-border transfer rules, and the company's privacy obligations to customers.