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Executive guide

How to Build an Enterprise AI Governance Operating Model

A practical sequence for moving from AI principles and committees to accountable inventory, decisions, controls, evidence and continuous oversight.

CAIO · Risk · Compliance · CIO6 operating steps
01

Start with the AI estate

Governance cannot operate on AI systems the institution does not know about. Establish one inventory across models, agents, applications, embedded SaaS AI and suppliers.

02

Define materiality and decision rights

Decide which AI activities require institutional intake, risk review, legal/compliance input, executive approval or independent assurance.

03

Translate policy into workflow

Turn policy statements into conditions, required evidence, accountable authorities, review queues and exceptions with expiry.

04

Preserve the decision record

Capture what was proposed, what evidence existed, who decided, which conditions applied and what changed later.

05

Extend governance into runtime

Monitor operating systems for material changes, incidents, policy breaches, human-review requirements and evidence gaps.

06

Report decisions, not dashboard volume

Give executives a view of material AI exposure, value, exceptions and decisions requiring action rather than a catalogue of metrics.

Implementation note

This guide provides an operating structure. The specific controls, authorities, legal interpretations and assurance requirements should be adapted to the institution, use case and jurisdiction.

From guidance to operating control

Put this operating model into practice.

Cybatar connects AI inventory, policy, accountable decisions, evidence, assurance and economics so governance can operate continuously rather than live in disconnected documents.