AI governance is the way an organization decides how artificial intelligence may be selected, used, reviewed, and controlled. It is becoming important because AI can affect decisions, documents, customer communication, software, analysis, and internal productivity.
A basic AI governance process should answer a few questions. What AI tools are allowed? What data may be entered? Which use cases are low risk? Which use cases need review? Who checks outputs before they affect customers, employees, contracts, safety, finance, legal work, or public statements?
Governance should also keep evidence. Teams may need to know which tool was used, what the purpose was, whether personal or confidential data was involved, who approved the use case, and how the output was checked.
The goal is not to block useful AI. The goal is to make AI use intentional, traceable, and appropriate to the risk. Human review matters most when output is uncertain, high-impact, externally visible, or based on changing rules.
AI governance can be supported with drafts, evidence records, review actions, use-case registers, review dates, and approval paths. The platform role is to make the decision trail easier to find later.
Related: Information Governance, Data Classification Basics, Audit Logs.