Law / Frameworks / Crosswalk / NIST AI RMF to OWASP Agentic Top 10

The NIST AI Risk Management Framework, mapped to the OWASP Top 10 for Agentic Applications

21 of NIST AI RMF's controls connect to 4 of OWASP Agentic Top 10's through 266 laws in 132 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
OWASP Top 10 for Agentic Applications, 2026 edition, December 2025, OWASP GenAI Security Project
See also
OWASP Agentic Top 10 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--owasp-agentic-top-10.csv · nist-ai-rmf--owasp-agentic-top-10.json

NIST AI RMFOWASP Agentic Top 10LawsJurisdictionsWhere the laws stand
GOVERN 1.1Legal and regulatory requirements involving AI are understood, managed, and documented. ASI09Human-Agent Trust Exploitation 38 20 28 in force, 9 not yet in force, 1 proposed
The 38 laws behind GOVERN 1.1 and ASI09

and 28 more, every one in the free file.

GOVERN 1.4The risk management process and its outcomes are established through transparent... ASI09Human-Agent Trust Exploitation 17 12 11 in force, 6 not yet in force
The 17 laws behind GOVERN 1.4 and ASI09

and 7 more, every one in the free file.

GOVERN 1.6Mechanisms are in place to inventory AI systems and are resourced according to... ASI09Human-Agent Trust Exploitation 7 6 6 in force, 1 not yet in force
The 7 laws behind GOVERN 1.6 and ASI09
GOVERN 2.1Roles and responsibilities and lines of communication related to mapping, measuring,... ASI09Human-Agent Trust Exploitation 7 6 7 in force
The 7 laws behind GOVERN 2.1 and ASI09
GOVERN 2.3Executive leadership of the organization takes responsibility for decisions about... ASI09Human-Agent Trust Exploitation 4 4 4 in force
The 4 laws behind GOVERN 2.3 and ASI09
GOVERN 4.2Organizational teams document the risks and potential impacts of the AI technology... ASI09Human-Agent Trust Exploitation 2 2 1 in force, 1 not yet in force
The 2 laws behind GOVERN 4.2 and ASI09
MAP 1.6System requirements (e.g., “the system shall respect the privacy of its users”) are... ASI09Human-Agent Trust Exploitation 5 5 2 in force, 3 not yet in force
The 5 laws behind MAP 1.6 and ASI09
MAP 2.3Scientific integrity and TEVV considerations are identified and documented, including... ASI09Human-Agent Trust Exploitation 12 9 8 in force, 4 not yet in force
The 12 laws behind MAP 2.3 and ASI09

and 2 more, every one in the free file.

MAP 3.3Targeted application scope is specified and documented based on the system’s... ASI09Human-Agent Trust Exploitation 4 3 3 in force, 1 not yet in force
The 4 laws behind MAP 3.3 and ASI09
MAP 3.5Processes for human oversight are defined, assessed, and documented in accordance with... ASI09Human-Agent Trust Exploitation 25 18 17 in force, 8 not yet in force
The 25 laws behind MAP 3.5 and ASI09

and 15 more, every one in the free file.

MAP 4.1Approaches for mapping AI technology and legal risks of its components – including the... ASI03Identity and Privilege Abuse 145 107 142 in force, 3 not yet in force
The 145 laws behind MAP 4.1 and ASI03

and 135 more, every one in the free file.

MAP 5.1Likelihood and magnitude of each identified impact (both potentially beneficial and... ASI09Human-Agent Trust Exploitation 3 3 3 in force
The 3 laws behind MAP 5.1 and ASI09
MEASURE 2.4The functionality and behavior of the AI system and its components – as identified in... ASI09Human-Agent Trust Exploitation 22 11 12 in force, 10 not yet in force
The 22 laws behind MEASURE 2.4 and ASI09

and 12 more, every one in the free file.

MEASURE 2.5The AI system to be deployed is demonstrated to be valid and reliable. Limitations of... ASI01Agent Goal Hijack 2 1 1 in force, 1 not yet in force
The 2 laws behind MEASURE 2.5 and ASI01
ASI03Identity and Privilege Abuse 2 1 1 in force, 1 not yet in force
The 2 laws behind MEASURE 2.5 and ASI03
ASI04Agentic Supply Chain Vulnerabilities 2 1 1 in force, 1 not yet in force
The 2 laws behind MEASURE 2.5 and ASI04
MEASURE 2.6The AI system is evaluated regularly for safety risks – as identified in the MAP... ASI09Human-Agent Trust Exploitation 5 5 2 in force, 3 not yet in force
The 5 laws behind MEASURE 2.6 and ASI09
ASI01Agent Goal Hijack 3 2 2 in force, 1 not yet in force
The 3 laws behind MEASURE 2.6 and ASI01
ASI03Identity and Privilege Abuse 3 2 2 in force, 1 not yet in force
The 3 laws behind MEASURE 2.6 and ASI03
ASI04Agentic Supply Chain Vulnerabilities 3 2 2 in force, 1 not yet in force
The 3 laws behind MEASURE 2.6 and ASI04
MEASURE 2.7AI system security and resilience – as identified in the MAP function – are evaluated... ASI09Human-Agent Trust Exploitation 2 2 1 in force, 1 not yet in force
The 2 laws behind MEASURE 2.7 and ASI09
ASI01Agent Goal Hijack 2 1 1 in force, 1 not yet in force
The 2 laws behind MEASURE 2.7 and ASI01
ASI03Identity and Privilege Abuse 2 1 1 in force, 1 not yet in force
The 2 laws behind MEASURE 2.7 and ASI03
ASI04Agentic Supply Chain Vulnerabilities 2 1 1 in force, 1 not yet in force
The 2 laws behind MEASURE 2.7 and ASI04
MEASURE 2.8Risks associated with transparency and accountability – as identified in the MAP... ASI09Human-Agent Trust Exploitation 44 34 31 in force, 8 not yet in force, 1 blocked by a court, 4 proposed
The 44 laws behind MEASURE 2.8 and ASI09

and 34 more, every one in the free file.

MEASURE 2.9The AI model is explained, validated, and documented, and AI system output is... ASI09Human-Agent Trust Exploitation 4 4 4 in force
The 4 laws behind MEASURE 2.9 and ASI09
MEASURE 2.11Fairness and bias – as identified in the MAP function – are evaluated and results are... ASI09Human-Agent Trust Exploitation 6 5 4 in force, 2 not yet in force
The 6 laws behind MEASURE 2.11 and ASI09
MEASURE 3.3Feedback processes for end users and impacted communities to report problems and... ASI09Human-Agent Trust Exploitation 2 2 2 in force
The 2 laws behind MEASURE 3.3 and ASI09
MANAGE 2.4Mechanisms are in place and applied, and responsibilities are assigned and understood,... ASI09Human-Agent Trust Exploitation 4 3 4 in force
The 4 laws behind MANAGE 2.4 and ASI09
Not connected to the OWASP Agentic Top 10: 51 of NIST AI RMF's controls

Short of 2 laws

The same duties reach these and a OWASP Agentic Top 10 control, in fewer than 2 of the laws we track.

No OWASP Agentic Top 10 control on the same duties

Laws reach these, but no duty that reaches one of them also reaches a OWASP Agentic Top 10 control.

  • 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 1.1 Intended purposes, potentially beneficial uses, context-specific laws, norms and..., 3 laws
  • MAP 1.5 Organizational risk tolerances are determined and documented., 1 law
  • 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 OWASP Agentic Top 10 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.

Sources and licenses

OWASP GenAI Security Project, Agentic Security Initiative, OWASP Top 10 for Agentic Applications 2026, licensed CC BY-SA 4.0. Excerpted: only the first paragraph of each entry's Description is reproduced, verbatim.