3 The Six-Level GenAI Governance Framework
This chapter covers
- A practical governance framework for GenAI, from high-level strategy to post-deployment monitoring.
- Adaptable guidance for different GenAI adoption models, from SaaS integration to full-stack model development.
- Real-world artifacts, tooling, and feedback loops that bring your Responsible AI program to life.
- Risk domains every GenAI initiative must address: from hallucinations and bias to prompt injection and IP leakage.
- A maturity model that helps your organization evolve from ad hoc GenAI controls to traceable, measurable, and auditable outcomes.
Governance for Generative AI is an ongoing process. As Fig 3.1 shows, organizations must navigate a dynamic loop between external stakeholder pressures (laws, regulations, and societal expectations like fairness and privacy), internal strategy (products and principles define the business, what is the risk appetite of the organization), and daily operations (where policies, risk practices, and engineering choices live)[1]. These dimensions (shown in Fig 3.1) influence each other constantly: a regulatory shift from the external environment may force a change in corporate direction or the organization, but a bold organizational strategy can also shape new industry norms. And insights from operations (such as gaps discovered in AI audits) should feed back into governance decisions. This recursive loop is the foundation of practical GenAI governance.