Relying on manual human review as the primary AI governance mechanism creates a false sense of security. This approach is unscalable and breaks down silently under the high volume of automated decisions, failing to provide genuine, consistent oversight where it's most needed.
An AI governance policy is only effective if it is an active, enforceable part of the development lifecycle. Policies that exist only in documents and don't manifest as automated, blocking gates in the deployment pipeline are merely for liability mitigation, not true governance.
Simply adapting the Infrastructure-as-Code (IAC) model for AI is insufficient. Because AI systems are probabilistic—producing varied outputs from the same input—effective governance requires a multi-level strategy covering pre-deployment validation, runtime enforcement, and continuous monitoring, rather than a single configuration policy.