{"id":723,"date":"2026-08-31T00:09:55","date_gmt":"2026-08-30T16:09:55","guid":{"rendered":"https:\/\/yenhui.co\/?p=723"},"modified":"2026-08-31T00:09:55","modified_gmt":"2026-08-30T16:09:55","slug":"generative-ai-usage-policy","status":"publish","type":"post","link":"https:\/\/yenhui.co\/en\/insights\/generative-ai-usage-policy\/","title":{"rendered":"How to Set Generative AI Usage Guidelines? Enterprise Policy Fields, Templates, and Implementation Guide"},"content":{"rendered":"

Generative AI Usage Policy<\/strong>Rather than a \"prohibited\" list, it should organize actionable common rules for employees detailing what the enterprise allows, what cannot be entered, who reviews the output, and how to report issues when they occur. Good guidelines must both reduce the risks of confidential information, personal data, copyright infringement, and incorrect content, while also preserving reasonable space for approved use.<\/p>\n\n\n

Why do enterprises need generative AI usage policies?<\/h2>\n

Even with good intentions, employees may paste customer data into unapproved tools, directly publish erroneous content externally, or use generated results without traceable sources. If a company simply says \"exercise your own judgment,\" everyone will use different standards to evaluate situations, and managers will have no way of knowing what applications are being used.<\/p>\n\n

The purpose of the standard is to establish traceable boundaries for tools, data, tasks, responsibilities, and records. It should be aligned withCorporate AI Governance<\/a>Integration, rather than being completed by a single department and then saved in a folder.<\/p>\n\n

An executable specification should contain 10 fields<\/h2>\n\n
Field<\/th>Decision to be made<\/th>Example<\/th><\/tr><\/thead>
Purpose and Scope<\/td>Which employees, contractors, and jobs apply<\/td>Internal drafts, customer service, programming collaboration<\/td><\/tr>
Allow tools<\/td>Which accounts, versions, and features are approved<\/td>corporate account or designated service<\/td><\/tr>
Data classification<\/td>What data can be entered, must be masked, and is prohibited from being entered<\/td>Public, Internal, Confidential, Restricted<\/td><\/tr>
Account permissions<\/td>Who can use, enable plugins, or connect data<\/td>SSO, MFA, principle of least privilege<\/td><\/tr>
Task boundary<\/td>Available scenarios, application required, or prohibited<\/td>Ideation available, high-impact decisions require review<\/td><\/tr>
Manual review<\/td>Who checks correctness, bias, and risks<\/td>Content owner, legal, security<\/td><\/tr>
Intellectual property<\/td>How to handle sources, licensing, citations, and delivery<\/td>Do not assume that the generated content is free to use.<\/td><\/tr>
Expose and Record<\/td>When to indicate AI collaboration and save evidence<\/td>Prompt, version, reviewer and publisher<\/td><\/tr>
Exceptions and Reporting<\/td>Who approves the exception, and how should a suspected leak be handled?<\/td>Cease use, preserve records, report to the point of contact<\/td><\/tr>
Update mechanism<\/td>Who tracks tools and regulatory changes<\/td>Review periodically and after major changes<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n

Data classification needs to be written in actions that employees can understand.<\/h2>\n

Just writing \"do not input confidential information\" is usually not enough. Employees need to know which classification client lists, contracts, unreleased financials, medical records, ID numbers, source code, and internal meeting minutes belong to, whether they can be de-identified, and who has the authority to approve them. The policy should include positive and negative examples, and explain that copying, uploading files, plugin connections, and custom GPTs all count as data input.<\/p>\n\n

Manual review isn't just about glancing at it and moving on.<\/h2>\n\n