Turn AI governance into operational controls rather than a policy document. EF establishes who owns AI systems, how risk is classified, what agents may do, where approval is required, how incidents are handled, and what evidence must exist.
AI systems, models, agents, vendors, data sources, and use cases.
Risk, data sensitivity, customer impact, autonomy, and criticality.
Ownership, permissions, approval boundaries, exceptions, and escalation.
Evaluations, decisions, incidents, monitoring, changes, and lifecycle records.
Organizations moving from scattered AI experimentation into repeatable, accountable use of AI and agents.
Product, engineering, security, privacy, legal, compliance, risk, audit, and business owners with one operating model rather than disconnected control documents.
Establish enforceable boundaries and evidence before scale makes governance harder.
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