AI governance is an architecture decision, not a purchase

AI governance is presented as an architectural capability rather than a software product, requiring organizations to ensure every AI interaction passes through a controlled point where permissions, costs, policies, and records can be consistently applied. The central test is whether an organization can prevent an AI call when necessary and demonstrate afterward what occurred; policies alone are insufficient if they depend on people remembering to follow them. Drawing on the evolution of API governance, the piece argues that organizations should map all AI-call pathways, identify where control and auditability are absent, and address those gaps through deliberate system design. It also notes that differing industry definitions of AI governance, ranging from policy and accountability frameworks to real-time technical controls, reflect complementary aspects of a broader organizational property that is easier and less costly to build into systems early than to retrofit later.

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