「これAIに入れていい？」社内AIガイドラインで事前に決めておくべきこと This article argues that companies should define clear internal AI usage guidelines before…

As generative AI tools spread through the workplace, IT and legal teams are increasingly fielding the same recurring question from employees: can I paste this document into ChatGPT or Claude? This article argues that answering case by case does not scale, and that every organization should publish an internal AI usage guideline that resolves these questions before they are asked.

The author's starting point is data classification. Companies need to define categories such as public, internal, confidential, personal data, and contractually restricted information, then specify for each whether it may be entered into an external AI service. Because different AI products treat input data differently, the guideline should also enumerate approved tools as a whitelist. Enterprise contracts such as ChatGPT Enterprise or Claude for Work typically guarantee that prompts are not used for training, whereas free-tier consumer products often reserve broader rights under their terms of service. Conflating the two creates real risk.

Beyond data scope, the guideline should cover approved use cases, prohibited applications (such as fully automated hiring or credit decisions where human accountability is required), handling of AI outputs, reminders about copyright and hallucination risk, and an incident response path if sensitive data is leaked into a model. Embedding these rules into onboarding and posting them in shared channels reduces the cognitive load on individual employees, who otherwise hesitate or, worse, guess.

For context, public reference material is becoming easier to find. Japan's METI publishes an AI Business Operator Guideline, IPA offers security checklists, and the Japan Deep Learning Association distributes a template generative-AI usage policy that many firms adapt. Internationally, the well-known 2023 incident in which Samsung engineers reportedly pasted proprietary source code into ChatGPT became a catalyst for stricter corporate policies, and similar episodes have surfaced in finance and healthcare. At the same time, overly restrictive blanket bans tend to push usage underground, producing so-called shadow AI that the company cannot monitor at all.

The practical takeaway is that guidelines should be lived documents rather than legalistic prohibitions. They likely need to be reviewed every few months as new models, agentic features and connector ecosystems emerge, and as regulators including the EU AI Act and Japan's evolving guidance set new baselines. Striking the right balance between enabling productivity and protecting information may well become a meaningful differentiator between organizations that successfully internalize AI and those that merely talk about it.