What is the NIST AI RMF and what is its purpose?
The NIST AI Risk Management Framework (AI RMF 1.0), published in January 2023 by the National Institute of Standards and Technology (NIST), helps organizations identify, assess, and manage the risks associated with artificial intelligence.
Its purpose is to support the responsible, safe, and trustworthy development and use of AI systems.
The Framework is:
- Voluntary (not mandatory)
- Rights-preserving
- Universal (applicable across all sectors and organization sizes)
- Use-case agnostic, adaptable to any context
In short, it serves as a compass for navigating AI risks, helping organizations build reliable, fair, and transparent AI systems.
What are the four main functions of the NIST AI RMF?
The Framework is built around four key functions that structure AI risk management:
- GOVERN: Establishes and maintains a culture of risk management. It connects organizational values with technical design and ensures governance mechanisms are in place.
- MAP: Defines the context for identifying and understanding AI-related risks. This includes recognizing stakeholders, system purposes, and potential impacts.
- MEASURE: Uses quantitative and qualitative tools to assess, analyze, and monitor AI risks and trustworthiness metrics.
- MANAGE: Allocates resources to mitigate identified risks, prioritizes actions, and prepares responses and recovery plans for incidents.
Together, these functions form a continuous cycle of trust, guiding organizations from awareness to action.
What are the seven characteristics of trustworthy AI systems?
To be trustworthy, AI systems should demonstrate the following seven characteristics, balanced according to their context of use:
- Valid and Reliable: Performs as intended, accurately and consistently over time.
- Safe: Does not endanger human life, health, property, or the environment.
- Secure and Resilient: Protects confidentiality, integrity, and availability, and can recover from adverse events.
- Accountable and Transparent: Provides accessible information about how the system operates and who is responsible.
- Explainable and Interpretable: Allows users to understand how decisions are made and what outputs mean.
- Privacy-Enhanced: Respects autonomy and dignity, using privacy-preserving techniques to limit data exposure.
- Fair with Harmful Bias Managed: Identifies and reduces bias across human, systemic, and statistical dimensions.
Who is the intended audience of the AI RMF, and what are “AI actors”?
The Framework is intended for organizations and individuals known as AI actors, anyone playing an active role in the AI system lifecycle, such as developers, data scientists, operators, or managers.
It promotes a sense of shared responsibility among these actors.
A secondary audience includes stakeholders affected by AI systems (such as users, communities, and advocacy groups) whose perspectives help shape responsible AI practices.
How are AI risks different from those of traditional software?
While AI shares some risks with traditional software (like cybersecurity or privacy concerns), it introduces unique and amplified risks, including:
- Data Risks: Incomplete, biased, or non-representative data can lead to harmful outcomes.
- Complexity and Scale: Massive data dependencies and opaque decision-making make oversight difficult.
- Unpredictability: Model training and retraining can change performance in unforeseen ways.
- Opacity: AI “black boxes” limit transparency and complicate testing.
- External Dependencies: Third-party tools and models may introduce risks beyond an organization’s control.
What are AI RMF Profiles and why are they useful?
AI RMF Profiles are practical implementations of the Framework’s functions and categories tailored to specific use cases or organizational contexts.
There are two main types:
- Use-Case Profiles: Focused on a particular application (e.g., AI for recruitment or healthcare).
- Temporal Profiles: Compare the current state of risk management (Current Profile) with the desired future state (Target Profile), revealing gaps and guiding improvement plans.
Profiles help organizations align their AI risk management strategy with their goals, resources, and regulatory environment.
