Law / Frameworks / Crosswalk / NIST AI RMF to NIST CSF 2.0
The NIST AI Risk Management Framework, mapped to the NIST Cybersecurity Framework
2 of NIST AI RMF's controls connect to 2 of NIST CSF 2.0's through 10 laws in 4 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 Cybersecurity Framework, version 2.0, February 2024 (NIST CSWP 29), National Institute of Standards and Technology
- See also
- NIST CSF 2.0 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-csf.csv · nist-ai-rmf--nist-csf.json
Not connected to the NIST CSF 2.0: 70 of NIST AI RMF's controls
Short of 2 laws
The same duties reach these and a NIST CSF 2.0 control, in fewer than 2 of the laws we track.
- GOVERN 1.4 The risk management process and its outcomes are established through transparent...: with PR.PS-04 through Digital Code, Chapter 23: AI system design and risk-management obligations, Kyrgyzstan
- GOVERN 4.2 Organizational teams document the risks and potential impacts of the AI technology...: with PR.PS-04 through Central Bank of Jordan Artificial Intelligence Framework for the Banking Sector, Version 1.0, Jordan
- GOVERN 6.1 Policies and procedures are in place that address AI risks associated with third-party...: with GV.SC-06 through Guidance Note on the Consumer Protection and Responsible Adoption and Use of Artificial Intelligence and Machine Learning by Licensed Financial Institutions in the U.A.E., United Arab Emirates
- MAP 3.5 Processes for human oversight are defined, assessed, and documented in accordance with...: with PR.PS-04 through Digital Code, Chapter 23: AI system design and risk-management obligations, Kyrgyzstan
- MEASURE 2.3 AI system performance or assurance criteria are measured qualitatively or...: with ID.RA-07 through OSFI Guideline E-23, Model Risk Management (2027), Canada
- MEASURE 2.5 The AI system to be deployed is demonstrated to be valid and reliable. Limitations of...: with PR.PS-06 through AI Act, Article 15 (accuracy, robustness and cybersecurity), European Union
- MEASURE 2.7 AI system security and resilience – as identified in the MAP function – are evaluated...: with ID.RA-07 through OSFI Guideline E-23, Model Risk Management (2027), Canada; with PR.AA-05 and PR.PS-04 through Decreto Legislativo 160/2026, After-the-Fact Facial Recognition Integrated in Video Surveillance (Art. 10), Italy; with PR.DS-01 through Superintendencia Circular on AI and Personal Data, Colombia; with PR.PS-06 and DE.CM-09 through AI Act, Article 15 (accuracy, robustness and cybersecurity), European Union
- MANAGE 2.4 Mechanisms are in place and applied, and responsibilities are assigned and understood,...: with PR.PS-04 through Digital Code, Chapter 23: AI system design and risk-management obligations, Kyrgyzstan
- MANAGE 4.3 Incidents and errors are communicated to relevant AI actors, including affected...: with RS.CO-02 through Artificial Intelligence Risk Management and Security Act of 2026, United States
No NIST CSF 2.0 control on the same duties
Laws reach these, but no duty that reaches one of them also reaches a NIST CSF 2.0 control.
- GOVERN 1.1 Legal and regulatory requirements involving AI are understood, managed, and documented., 65 laws
- GOVERN 1.2 The characteristics of trustworthy AI are integrated into organizational policies,..., 6 laws
- GOVERN 1.3 Processes, procedures, and practices are in place to determine the needed level of..., 6 laws
- GOVERN 1.5 Ongoing monitoring and periodic review of the risk management process and its outcomes..., 7 laws
- GOVERN 1.6 Mechanisms are in place to inventory AI systems and are resourced according to..., 8 laws
- GOVERN 1.7 Processes and procedures are in place for decommissioning and phasing out AI systems..., 1 law
- GOVERN 2.1 Roles and responsibilities and lines of communication related to mapping, measuring,..., 9 laws
- GOVERN 2.2 The organization’s personnel and partners receive AI risk management training to..., 3 laws
- GOVERN 2.3 Executive leadership of the organization takes responsibility for decisions about..., 9 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 6.2 Contingency processes are in place to handle failures or incidents in third-party data..., 1 law
- MAP 1.1 Intended purposes, potentially beneficial uses, context-specific laws, norms and..., 3 laws
- MAP 1.5 Organizational risk tolerances are determined and documented., 1 law
- MAP 1.6 System requirements (e.g., “the system shall respect the privacy of its users”) are..., 34 laws
- MAP 2.2 Information about the AI system’s knowledge limits and how system output may be..., 12 laws
- MAP 2.3 Scientific integrity and TEVV considerations are identified and documented, including..., 18 laws
- MAP 3.3 Targeted application scope is specified and documented based on the system’s..., 246 laws
- MAP 3.4 Processes for operator and practitioner proficiency with AI system performance and..., 3 laws
- MAP 4.1 Approaches for mapping AI technology and legal risks of its components – including the..., 597 laws
- MAP 5.1 Likelihood and magnitude of each identified impact (both potentially beneficial and..., 22 laws
- 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 2.1 Test sets, metrics, and details about the tools used during TEVV are documented., 1 law
- MEASURE 2.2 Evaluations involving human subjects meet applicable requirements (including human..., 1 law
- MEASURE 2.8 Risks associated with transparency and accountability – as identified in the MAP..., 150 laws
- MEASURE 2.9 The AI model is explained, validated, and documented, and AI system output is..., 43 laws
- MEASURE 2.10 Privacy risk of the AI system – as identified in the MAP function – is examined and documented., 28 laws
- MEASURE 2.11 Fairness and bias – as identified in the MAP function – are evaluated and results are..., 24 laws
- MEASURE 3.1 Approaches, personnel, and documentation are in place to regularly identify and track..., 2 laws
- MEASURE 3.3 Feedback processes for end users and impacted communities to report problems and..., 53 laws
- MANAGE 1.1 A determination is made as to whether the AI system achieves its intended purposes and..., 6 laws
- MANAGE 1.3 Responses to the AI risks deemed high priority, as identified by the MAP function, are..., 14 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 4.1 Post-deployment AI system monitoring plans are implemented, including mechanisms for..., 24 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...
Lines of the scraping and news aggregation laws we track were not read against the NIST CSF 2.0, so none of them connects the two here (how each subject's lines were read).
Lines of the age assurance and cybersecurity laws we track were not read against the NIST AI RMF, so none of them connects the two here (how each subject's lines were read).
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. Both are read line by line, so a line connects them only where it was read against both and placed on each. 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.
A line placed on a control in the framework for its own subject, whose law carries a kind of duty that reaches another of the three frameworks read line by line, is read against that one as well: every such line, or only those whose wording reaches into it. For these two:
| Laws on | Read against the NIST AI RMF | Read against the NIST CSF 2.0 |
|---|---|---|
| AI | every line (their own framework) | such lines on security, robustness, resilience, vulnerabilities and logs |
| scraping | every line (their own framework) | not read |
| news aggregation | every line (their own framework) | not read |
| privacy | such lines on automated decisions, profiling and AI | such lines on breaches and incidents, security measures, encryption and pseudonymization, retention and disposal, and processor contracts |
| age assurance | not read | every such line |
| cybersecurity | not read | every line (their own framework) |
Every duty line in the AI and privacy laws we track has been read against the framework for its subject.