ChatGPTやCopilotに「社内コード」を貼り付ける前に知っておきたい！AI利用で絶対にやってはいけない3つのことAs AI coding tools become standard practice, this article outlines three…

The habit of pasting a snippet of code into ChatGPT or GitHub Copilot to debug it has become an everyday reflex for many software engineers, but a Qiita blog post warns that this convenience can expose companies to serious security and compliance problems. The piece outlines three things developers should never do when using AI tools, with the sharing of proprietary code through public services at the top of the list.

The advice lands against a backdrop of near-universal adoption. A JetBrains survey conducted in October 2025 found that close to 85 percent of developers already rely on AI coding tools in their daily work. At that scale, the question is no longer whether teams use these assistants but how they use them, and the details of prompts, service choice, and output handling now carry weight for the majority of organizations rather than a handful of experimenters.

The central risk concerns what happens to data once it leaves a company's systems. When developers paste internal source code, configuration details, credentials, or personal information into a public AI service, that content can be transmitted to and stored on third-party servers. Depending on the provider and the plan, submitted text may be retained, reviewed by human evaluators, or used to help train future models. Free consumer tiers generally offer weaker guarantees than paid enterprise plans, and the practical effect is that confidential material can end up outside the organization's control, potentially violating contracts, non-disclosure agreements, or data-protection rules.

The three practices the article flags appear to fall into familiar categories of AI hygiene. The first is submitting proprietary or confidential code to public-facing chatbots without authorization. The second is likely the exposure of secrets and sensitive data, such as API keys, passwords, customer records, or personally identifiable information, which can create both security and privacy liabilities. The third is likely the uncritical use of AI-generated output, since suggested code can contain vulnerabilities, insecure patterns, or snippets that carry unclear licensing, all of which require human review before they reach a production codebase.

These concerns are not hypothetical. In 2023, Samsung reportedly restricted employee use of generative AI tools after internal source code was entered into ChatGPT, an episode that became a widely cited cautionary example of accidental data leakage. Similar worries have prompted many enterprises to issue formal policies, block certain services on corporate networks, or move to controlled deployments.

There are established ways to reduce the exposure. Vendors now offer business and enterprise tiers, such as ChatGPT Enterprise and GitHub Copilot Business and Enterprise, that contractually commit not to train on submitted content and provide administrative controls, logging, and data-retention settings. Some organizations route requests through private endpoints, self-hosted models, or cloud services like Azure OpenAI to keep data within a defined boundary. Complementary safeguards include data loss prevention tooling, secret scanning in repositories, and policies that require sensitive values to be stripped or replaced with placeholders before any prompt is sent.

Understanding a few prerequisite concepts helps explain why the warnings matter. A prompt is not