A Taxonomy of AI Governance Approaches: Distinguishing Visibility, Alignment, and Authorization

The term “AI governance” has become semantically overloaded, applied indiscriminately to logging, guardrails, model alignment, dashboards, and policy workflows. This paper introduces a formal taxonomy that distinguishes three fundamentally different governance problems—visibility (what happened), alignment (is the system generally safe), and authorization (was this specific action permitted under policy at execution time)—and maps common vendor “governance” claims to the problems they actually solve. The taxonomy defines deterministic AI governance as a pre-execution authorization layer in which identical governed state yields identical governance verdicts and the system emits cryptographically verifiable…