The four functions, briefly
The AI RMF is meant to be used iteratively. Govern is the throughline that holds the other three together. The Generative AI profile sits on top, calling out risks specific to generative systems such as data leakage, confabulation, and prompt injection.
| Function | What it asks for |
|---|---|
| Govern | A culture of AI risk management: policies, roles, accountability, and oversight that cut across everything else. |
| Map | Establish the context: identify the AI systems in use, their purpose, and the risks they carry. |
| Measure | Assess, analyze, and track the identified risks using repeatable methods and metrics. |
| Manage | Prioritize and act on the risks, treat them, and monitor that the treatment holds. |
Map and Measure are where teams stall, because both need real data about the AI actually running in the environment. That is exactly the gap Senserva fills on the Microsoft side.
How Senserva supports each function
Senserva is not a governance program in a box. It is the instrument that gives each function current, Microsoft-side evidence, so the framework is grounded in what your tenant really looks like rather than in assumptions.
Crosswalk: AI RMF function to Senserva
Use this as a starting map. Senserva covers the Microsoft technical layer of each function. Your program supplies the rest.
| AI RMF function | How Senserva supports it |
|---|---|
| Govern | Policy and responsible-AI evidence: tracked governance controls (approved models, content filtering, human in the loop), plus a tool that runs locally with your own model and approves every change before applying it. |
| Map | An AI and agent inventory with context: blueprints, agent identities and users, inheritable permissions, and the access posture that defines reach. |
| Measure | Findings ranked by real-world risk, deterministic detection of high-risk Microsoft Graph scopes on agents, and audit-log health as a monitoring signal. |
| Manage | Validated remediation, applied only after human approval, with scheduled re-scans that prove a risk was actually resolved. |
For the detail on the deterministic agent checks behind Map and Measure, see Microsoft AI security.
Pair the framework with a standard you can certify against
The AI RMF is deliberately voluntary and outcome-based. If you want a management system you can be assessed against, ISO/IEC 42001 is the natural companion, and Senserva's evidence maps to both. Run a scan, see your AI inventory, and start the crosswalk from real data.