Model-agnostic governance is an approach where policy enforcement operates independently of any specific AI model or provider, sitting outside the model itself rather than being built into it. This means the same governance rules apply whether an organization uses one AI provider or several, and continue working if that provider changes.
Why It Matters
Organizations rarely commit to a single AI model long-term, teams experiment with different providers for different use cases, switch vendors as pricing or capabilities shift, and increasingly run multiple models side by side for different tasks. Governance built into or tightly coupled with one specific model or vendor's tooling breaks down the moment that mix changes, forcing a rebuild of controls every time the underlying AI stack evolves. This is a common blind spot: teams invest in governance that works well for their current model, only to find it doesn't transfer when a new model is adopted for a different workflow.
How It Relates to AI Governance
Model-agnostic governance is what allows a governance layer to remain a stable, long-term control even as the AI landscape underneath it changes quickly. Because it evaluates interactions rather than models, the same Policy Packs and decisioning logic apply consistently across every AI system an organization uses, rather than requiring separate governance implementations per provider.