I’m using AI to build internal tools, but how do I know what I’m missing?

Your concern is well placed. Building a tool that works is not the same as knowing it is safe to deploy or maintain. Before anything touches business data or becomes relied on, ask management to identify who is accountable for reviewing and supporting it. If nobody has that expertise, that is a staffing and governance gap, not something an AI assistant can close by itself.

In the meantime, keep experiments out of production. Use a separate development environment, a version-controlled repository, and a test environment with non-sensitive data. Get a qualified person to review anything that handles credentials, personal information, network access, or important business processes.

Start with the basics you can put into practice right away: use version control so changes can be reviewed and rolled back, keep secrets out of source files, and give each tool only the access it actually needs. Don’t run unreviewed code with broad privileges or point it at production data just because it seems to work.

For a shared company repository, ask someone responsible for it to enable protected branches and require review before changes are merged. Automated tests and security checks can help catch problems, but they do not replace a human who understands the system and its risks.

AI can help you learn, but treat its explanations and generated code as suggestions to verify. Ask it to explain what a change does, what assumptions it makes, and how it could fail. Then check those answers against documentation or a knowledgeable reviewer. A passing test only shows that particular cases passed, not that the tool is secure or ready for production.

A useful first learning path is basic programming, Git, testing, dependency management, and common security concepts like authentication, authorization, input validation, and secret handling. Keep the projects small while you build that foundation.

I’d make this a management decision, not a solo technical gamble. Write down what each tool does, what systems and data it can reach, who will use it, and who will fix it if it breaks. Then ask for a lightweight approval process and a named reviewer before tools are used across teams.

For a company of this size, even a small internal app can create support, privacy, and operational risks. A clear policy about approved tools, data, environments, and deployment responsibilities is a reasonable first step.