Building an AI governance model your CISO will sign off on

A workable AI governance model has six components: acceptable-use policy, data-handling and residency, model-risk management, vendor evaluation, monitoring and incident response, and human-in-the-loop design standards. Each is owned by a named function; none should sit unowned.

Controls bolted on at the end are expensive and brittle. Controls designed alongside the build are cheaper, more durable, and have the side effect of producing a system risk can actually sign off on. The cost difference between the two patterns is enormous — almost always under-appreciated until the third or fourth pilot tries to scale.

The teams that ship AI at scale bring risk and legal into the design from week one. Their concerns get baked into architecture decisions, not negotiated away later. The partnership is genuinely cheaper for everyone — and dramatically faster than the alternative.

Acceptable-use violations, model agreement rates against human decisions, prompt-injection signal, data-leakage signal. These are the things that actually move once a system is in production. Dashboards for them, owned by named people, with a documented escalation path. Without monitoring, governance is just documentation.