Frameworks & Standards
NIST AI RMF
The NIST AI Risk Management Framework is a voluntary framework for managing risks in AI systems. The AI RMF core uses four functions: govern, map, measure, and manage.
Buyer question
"Can Legra help us make AI systems more governable, explainable, and auditable?"
Legra fit
| AI RMF function | Legra fit | Status |
|---|---|---|
| Govern | Legra can store policies, ownership, provenance, validation rules, and approval history alongside the data AI systems use. | Partial fit |
| Map | Workspaces, catalogs, branches, data lineage, and semantic metadata help identify the context and intended use of AI inputs. | Native fit |
| Measure | Validation reports, queryable provenance, inference explanations, and data-quality rules can provide measurable evidence. | Partial fit |
| Manage | Branch isolation, review/merge workflows, revocation, and policy-driven validation help control AI data changes over time. | Partial fit |
What Legra can say
Use Legra to control the knowledge an agent can access and review the data changes it proposes:
- Grant agents access to the workspaces and queries needed for their task.
- Keep agent proposals on separate branches until they have been reviewed.
- Use validation rules to check required data constraints before promotion.
- Provenance ties data, rules, agent activity, and human review together.
- Inference explanations can show why a derived fact exists.
- Encrypted workspaces keep sensitive context inside controlled data boundaries.
Assessment boundary
NIST AI RMF assessment also depends on model evaluation, bias testing, human factors, operational monitoring, organizational governance, and risk acceptance.
Legra supplies a governed, auditable, branch-isolated knowledge layer for AI systems and agents. That evidence supports AI risk management, but it does not replace the buyer's full AI governance program.