Law / Frameworks / Crosswalk / NIST AI RMF to NIST AI 200-2
The NIST AI Risk Management Framework, mapped to the NIST TEVV-Athlon Framework for Evaluating AI Systems
20 of NIST AI RMF's controls connect to 4 of NIST AI 200-2's through 88 laws in 40 jurisdictions. Two controls connect when the same duties, in at least 2 laws we track, reach both. The connections are our own reading of the law; no framework publisher has reviewed or endorsed them.
- From
- NIST AI Risk Management Framework, version 1.0, January 2023 (NIST AI 100-1), National Institute of Standards and Technology
- To
- NIST TEVV-Athlon Framework for Evaluating AI Systems, initial public draft, August 2026 (NIST AI 200-2 ipd), National Institute of Standards and Technology
- See also
- NIST AI 200-2 to NIST AI RMF, the other way · every pair in the crosswalk · law library as of
Download this mapping, with every law behind each connection: nist-ai-rmf--nist-ai-200-2.csv · nist-ai-rmf--nist-ai-200-2.json
Not connected to the NIST AI 200-2: 52 of NIST AI RMF's controls
Short of 2 laws
The same duties reach these and a NIST AI 200-2 step, in fewer than 2 of the laws we track.
- GOVERN 1.3 Processes, procedures, and practices are in place to determine the needed level of...: with 2.3 and 4.3 through Model risk management principles for banks, United Kingdom
- GOVERN 1.5 Ongoing monitoring and periodic review of the risk management process and its outcomes...: with 2.3 and 4.3 through Transparency in Frontier Artificial Intelligence Act (SB 53), California
- GOVERN 4.2 Organizational teams document the risks and potential impacts of the AI technology...: with 2.3 and 4.3 through Central Bank of Jordan Artificial Intelligence Framework for the Banking Sector, Version 1.0, Jordan; with 2.4 through Digital Code, Chapter 23: AI system design and risk-management obligations, Kyrgyzstan
- MAP 1.1 Intended purposes, potentially beneficial uses, context-specific laws, norms and...: with 2.4 through AI Act, Article 27 (fundamental rights impact assessment), European Union
- MAP 1.6 System requirements (e.g., “the system shall respect the privacy of its users”) are...: with 2.3 and 4.3 through Use of artificial intelligence in therapy or psychotherapy services, Maine
- MAP 2.2 Information about the AI system’s knowledge limits and how system output may be...: with 2.3 and 4.3 through Federal Law No. 243-FZ, Article 8, Duties of Sovereign and National Foundation Model Developers, Russia
- MAP 3.4 Processes for operator and practitioner proficiency with AI system performance and...: with 2.3 and 4.3 through Decreto Legislativo 160/2026, Police Use of AI Systems: Human Review, Oversight, Collaborations, Sandbox Basis and Training (Artt. 3-6, 21), Italy
- MAP 4.1 Approaches for mapping AI technology and legal risks of its components – including the...: with 2.3 and 4.3 through Commission Guidelines on the scope of the obligations for providers of general-purpose AI models under the AI Act, European Union
- MEASURE 2.1 Test sets, metrics, and details about the tools used during TEVV are documented.: with 2.3 and 4.3 through AI Act, Articles 60 and 61 (testing in real world conditions outside AI regulatory sandboxes, with informed consent), European Union
- MEASURE 2.8 Risks associated with transparency and accountability – as identified in the MAP...: with 2.3 and 4.3 through Practice Direction No. 1 of 2025, Use of Generative Artificial Intelligence in Court Proceedings, Jamaica
- MANAGE 4.1 Post-deployment AI system monitoring plans are implemented, including mechanisms for...: with 2.3 and 4.3 through AI Act, Article 17 (quality management system), European Union
No NIST AI 200-2 step on the same duties
Laws reach these, but no duty that reaches one of them also reaches a NIST AI 200-2 step.
- GOVERN 1.2 The characteristics of trustworthy AI are integrated into organizational policies,..., 6 laws
- GOVERN 1.7 Processes and procedures are in place for decommissioning and phasing out AI systems..., 1 law
- GOVERN 2.2 The organization’s personnel and partners receive AI risk management training to..., 3 laws
- GOVERN 3.2 Policies and procedures are in place to define and differentiate roles and..., 5 laws
- GOVERN 4.1 Organizational policies and practices are in place to foster a critical thinking and..., 3 laws
- GOVERN 4.3 Organizational practices are in place to enable AI testing, identification of..., 3 laws
- GOVERN 5.1 Organizational policies and practices are in place to collect, consider, prioritize,..., 1 law
- GOVERN 6.1 Policies and procedures are in place that address AI risks associated with third-party..., 14 laws
- GOVERN 6.2 Contingency processes are in place to handle failures or incidents in third-party data..., 1 law
- MAP 1.5 Organizational risk tolerances are determined and documented., 1 law
- MEASURE 1.2 Appropriateness of AI metrics and effectiveness of existing controls are regularly..., 1 law
- MEASURE 1.3 Internal experts who did not serve as front-line developers for the system and/or..., 10 laws
- MEASURE 2.2 Evaluations involving human subjects meet applicable requirements (including human..., 1 law
- MEASURE 2.3 AI system performance or assurance criteria are measured qualitatively or..., 6 laws
- MEASURE 3.1 Approaches, personnel, and documentation are in place to regularly identify and track..., 2 laws
- MANAGE 2.1 Resources required to manage AI risks are taken into account – along with viable..., 1 law
- MANAGE 2.3 Procedures are followed to respond to and recover from a previously unknown risk when..., 1 law
- MANAGE 3.1 AI risks and benefits from third-party resources are regularly monitored, and risk..., 3 laws
- MANAGE 4.3 Incidents and errors are communicated to relevant AI actors, including affected..., 17 laws
No law we track reaches these
No duty in the laws we track reaches these controls.
- GOVERN 3.1 Decision-making related to mapping, measuring, and managing AI risks throughout the...
- GOVERN 5.2 Mechanisms are established to enable the team that developed or deployed AI systems to...
- MAP 1.2 Interdisciplinary AI actors, competencies, skills, and capacities for establishing...
- MAP 1.3 The organization’s mission and relevant goals for AI technology are understood and documented.
- MAP 1.4 The business value or context of business use has been clearly defined or – in the...
- MAP 2.1 The specific tasks and methods used to implement the tasks that the AI system will...
- MAP 3.1 Potential benefits of intended AI system functionality and performance are examined...
- MAP 3.2 Potential costs, including non-monetary costs, which result from expected or realized...
- MAP 4.2 Internal risk controls for components of the AI system, including third-party AI...
- MAP 5.2 Practices and personnel for supporting regular engagement with relevant AI actors and...
- MEASURE 1.1 Approaches and metrics for measurement of AI risks enumerated during the MAP function...
- MEASURE 2.12 Environmental impact and sustainability of AI model training and management activities...
- MEASURE 2.13 Effectiveness of the employed TEVV metrics and processes in the MEASURE function are...
- MEASURE 3.2 Risk tracking approaches are considered for settings where AI risks are difficult to...
- MEASURE 4.1 Measurement approaches for identifying AI risks are connected to deployment context(s)...
- MEASURE 4.2 Measurement results regarding AI system trustworthiness in deployment context(s) and...
- MEASURE 4.3 Measurable performance improvements or declines based on consultations with relevant...
- MANAGE 1.2 Treatment of documented AI risks is prioritized based on impact, likelihood, and...
- MANAGE 1.4 Negative residual risks (defined as the sum of all unmitigated risks) to both...
- MANAGE 2.2 Mechanisms are in place and applied to sustain the value of deployed AI systems.
- MANAGE 3.2 Pre-trained models which are used for development are monitored as part of AI system...
- MANAGE 4.2 Measurable activities for continual improvements are integrated into AI system updates...
How these connections are drawn
Each requirement line in the laws we track is read against the framework for its subject (the AI, scraping and news aggregation laws against the NIST AI RMF, the privacy, communications and age assurance laws against the NIST Privacy Framework, the cybersecurity laws against the NIST CSF 2.0); laws on liability and content moderation are not read against any framework yet. A line reaches the NIST AI 200-2 through a kind of duty its law carries, and only where that kind of duty also reaches, on that same line, the NIST framework for the line's own subject. Two controls connect when lines in at least 2 laws reach both. That says the same duties reach both; it does not say the two are alike, or that running either one does what a law asks.
Every duty line in the AI, scraping and news aggregation laws we track has been read against the framework for its subject.