Building an AI governance program starts with an AI system inventory and executive sponsorship. Identify all AI tools in use or planned, assign ownership, conduct risk assessments using the NIST AI RMF, then develop policies covering acceptable use, data governance, bias testing, and incident response. A pre-mapped control set like the SCF AAT domain provides a tested baseline that accelerates this process significantly.
Most organizations find that AI governance programs fail at the inventory step - teams are surprised by how many AI tools are already deployed without formal review. A practical sequence:
First, conduct an AI system discovery, including shadow AI and SaaS tools with embedded AI features.
Second, classify each system by risk tier based on the type of decisions it influences and the sensitivity of data it processes.
Third, assign an accountable owner for each system.
Fourth, conduct risk assessments using the NIST AI RMF - work through Govern, Map, Measure, and Manage for each system.
Fifth, develop policies covering AI procurement and approval, acceptable use, data minimization for training data, bias and fairness testing, human oversight requirements, and incident response.
Sixth, establish continuous monitoring - AI systems drift over time as models are retrained or inputs change, so monitoring is ongoing, not a one-time activity.
The SCF AAT domain provides a pre-built, framework-mapped control set that can serve as the governance baseline, significantly reducing the time required to build policies and control evidence from scratch.
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