NIST AI Risk Management Framework

The four functions

The NIST AI RMF is organized around four core functions that are designed to be iterative and integrated into existing enterprise risk management practices. Amanvi operationalizes each one directly within your compliance workflow.

• ✓Govern. Establish the policies, processes, procedures, and practices for managing AI risk across the organization. Amanvi captures governance structures, role assignments, and policy coverage per AI system so leadership can demonstrate accountability.
• ✓Map. Identify the context in which an AI system operates and characterize its risks. Amanvi's inventory and risk classification engine map each system to its purpose, stakeholders, data inputs, and potential impacts.
• ✓Measure. Analyze, assess, and track AI risks and related impacts using quantitative and qualitative methods. Amanvi generates readiness scores, gap analyses, and evidence coverage metrics that give you a measurable view of risk posture.
• ✓Manage. Allocate risk resources to mapped and measured risks on an ongoing basis. Amanvi tracks corrective actions, links evidence to controls, and surfaces findings so risks are managed to closure rather than logged and forgotten.

Trustworthiness characteristics

The RMF emphasizes that trustworthy AI systems must exhibit specific characteristics. Amanvi's risk classification and evidence workflow is designed to surface and document each of them:

• ✓Valid and reliable. Systems produce accurate, consistent results under expected conditions. Amanvi tracks validation records and performance testing evidence.
• ✓Safe. Systems do not pose unreasonable safety risks to people or property. Risk classifications flag systems with physical or safety-related impact potential.
• ✓Secure and resilient. Systems can withstand adversarial attacks and unexpected conditions. Amanvi links security assessments and penetration testing evidence to each system.
• ✓Accountable and transparent. Stakeholders can understand how and why a system behaves as it does. Every Amanvi classification includes a plain-language rationale and citations.
• ✓Explainable and interpretable. Users can understand the basis for AI outputs. Amanvi documents explainability assessments and model cards as evidence.
• ✓Privacy-enhanced. Systems respect privacy rights and preferences. Amanvi tracks privacy impact assessments and data governance controls.
• ✓Fair — with harmful bias managed. Systems treat individuals and groups equitably. Bias testing results and fairness assessments are stored as linked evidence.

How Amanvi aligns with the RMF

The NIST AI RMF is not a checklist — it is a process. Amanvi turns that process into a system of record. Every AI system you register is mapped, measured, and managed through the platform. When you need to demonstrate alignment to a customer, a board, or a regulator, the evidence trail is already complete.