Framework Structure
Core Functions of the AI RMF
The framework is operationalized through four interconnected functions, which are designed to work as a continuous, virtuous cycle:
Cultivate Risk Culture
This is the cross-cutting function that sets the foundation. It involves cultivating a culture of risk management, establishing roles and responsibilities, and ensuring that AI risk management is aligned with broader organizational policies and goals.
Identify Context & Risks
This involves identifying the context in which the AI system operates. You must define the AI's intended purpose, identify the stakeholders, and understand the potential positive and negative impacts (risks) in that specific environment.
Assess & Track Risks
Once risks are mapped, you must assess, analyze, and track them. This involves using metrics to evaluate trustworthiness, such as testing for bias, evaluating performance against benchmarks, and assessing security vulnerabilities.
Prioritize & Act
This function involves prioritizing identified risks and taking action to mitigate, transfer, avoid, or accept them based on your organization's risk tolerance.
AI Design Principles
Characteristics of Trustworthy AI
To achieve a "trustworthy" AI system, NIST suggests that organizations focus on the following core characteristics throughout the AI lifecycle:
The system performs as intended and provides consistent results.
The system does not pose unreasonable risk of physical or psychological harm.
The system can withstand and recover from intentional or unintentional adversarial events.
Roles are clear, and there is documentation regarding how the AI works and its limitations.
Stakeholders can understand the rationale behind AI-driven outputs.
Data protection is integrated into the design.
Harmful bias is identified and managed.
Practical Application
Key Implementation Strategies
For organizations looking to implement these guidelines, NIST and related industry resources suggest:
Ensure leadership is committed and that cross-functional teams (legal, ethics, data science, IT) are involved.
NIST provides "Profiles," which are specific implementations of the RMF for particular use cases or sectors (e.g., Generative AI, Critical Infrastructure). Use these to tailor the framework to your specific needs.
NIST is increasingly aligning AI risk management with the NIST Cybersecurity Framework (CSF 2.0) to help organizations secure their AI infrastructure, defend against attacks, and build general resilience.
AI systems evolve as they process new data. Risk management must be an ongoing, cyclical process rather than a one-time "check-the-box" activity.
Official Resources
NIST Resource Center
Trustworthy and Responsible AI Resource Center
NIST maintains the Trustworthy and Responsible AI Resource Center, which offers toolkits, templates, and use-case examples to help organizations transition from the theory of the RMF into practical application.