A practical path to standing up the framework in an existing programme — from your first profile to embedded measurement.
The NIST AI Risk Management Framework (AI RMF 1.0, published 26 January 2023) is voluntary and designed to be adapted, not adopted wholesale. You do not need a clean-sheet programme to use it. The more useful starting point is an honest picture of the AI you already run and the risk practices you already have, then a decision about which outcomes matter most for your context.
The framework's companion resources include the idea of profiles — selections of the framework's outcomes tailored to a use case, sector or risk appetite. A profile lets you describe a current state and a target state, so adoption becomes a gap to close rather than an abstract aspiration. Begin with one or two high-priority systems, build a profile around them, and expand once the approach is proven.
Govern is the cross-cutting function and the natural place to begin, because it establishes the accountability, policies and culture that the other functions depend on. Without clear ownership and process, mapping and measurement tend not to stick. Getting Govern right early gives the rest of the framework somewhere to land.
The framework is meant to complement, not replace, what you already do for security, privacy, model risk and quality. Wherever an existing programme already covers an outcome, reuse it and reference it rather than rebuilding. This keeps adoption lighter and avoids creating a parallel governance silo that competes with established controls.
Adoption is durable only when the Measure function becomes routine. Decide early how you will analyse, benchmark and monitor risk, and how those results feed back into the Manage function. Measurement that is captured continuously — rather than as a one-off assessment — is what lets you show progress over time and respond as systems change.
Our Governance Platform is designed to hold profiles, controls and evidence in one place and keep them aligned to the framework, so RMF adoption builds on the programmes you already run rather than standing up a separate one.
From first profile to embedded measurement, with controls and evidence in one place.