id string | text string | doc_id string | doc_title string | revision string | pub_date string | tier int32 | chunk_type string | control_id string | section_path string | source_url string | sha256_source string |
|---|---|---|---|---|---|---|---|---|---|---|---|
AI-100-1/ai_rmf_subcategory/govern-1.1 | GOVERN 1.1: Legal and regulatory requirements involving AI are understood, managed, and documented. | AI-100-1 | Artificial Intelligence Risk Management Framework (AI RMF 1.0) | 1.0 | 2023-01 | 2 | ai_rmf_subcategory | null | AI RMF Core > Govern > GOVERN 1.1 | https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf | 7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1 |
AI-100-1/ai_rmf_subcategory/govern-1.2 | GOVERN 1.2: The characteristics of trustworthy AI are integrated into organizational policies, processes, procedures, and practices. | AI-100-1 | Artificial Intelligence Risk Management Framework (AI RMF 1.0) | 1.0 | 2023-01 | 2 | ai_rmf_subcategory | null | AI RMF Core > Govern > GOVERN 1.2 | https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf | 7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1 |
AI-100-1/ai_rmf_subcategory/govern-1.3 | GOVERN 1.3: Processes, procedures, and practices are in place to determine the needed level of risk management activities based on the organization's risk tolerance. | AI-100-1 | Artificial Intelligence Risk Management Framework (AI RMF 1.0) | 1.0 | 2023-01 | 2 | ai_rmf_subcategory | null | AI RMF Core > Govern > GOVERN 1.3 | https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf | 7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1 |
AI-100-1/ai_rmf_subcategory/govern-1.4 | GOVERN 1.4: The risk management process and its outcomes are established through transparent policies, procedures, and other controls based on organizational risk priorities. Categories Subcategories Continued on next page Table 1: Categories and subcategories for the GOVERN function. (Continued) | AI-100-1 | Artificial Intelligence Risk Management Framework (AI RMF 1.0) | 1.0 | 2023-01 | 2 | ai_rmf_subcategory | null | AI RMF Core > Govern > GOVERN 1.4 | https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf | 7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1 |
AI-100-1/ai_rmf_subcategory/govern-1.5 | GOVERN 1.5: Ongoing monitoring and periodic review of the risk management process and its outcomes are planned and organizational roles and responsibilities clearly defined, including determining the frequency of periodic review. | AI-100-1 | Artificial Intelligence Risk Management Framework (AI RMF 1.0) | 1.0 | 2023-01 | 2 | ai_rmf_subcategory | null | AI RMF Core > Subcategories > GOVERN 1.5 | https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf | 7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1 |
AI-100-1/ai_rmf_subcategory/govern-1.6 | GOVERN 1.6: Mechanisms are in place to inventory AI systems and are resourced according to organizational risk priorities. | AI-100-1 | Artificial Intelligence Risk Management Framework (AI RMF 1.0) | 1.0 | 2023-01 | 2 | ai_rmf_subcategory | null | AI RMF Core > Subcategories > GOVERN 1.6 | https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf | 7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1 |
AI-100-1/ai_rmf_subcategory/govern-1.7 | GOVERN 1.7: Processes and procedures are in place for decommissioning and phasing out AI systems safely and in a manner that does not increase risks or decrease the organization's trustworthiness. GOVERN 2: Accountability structures are in place so that the appropriate teams and individuals are empowered, responsible, ... | AI-100-1 | Artificial Intelligence Risk Management Framework (AI RMF 1.0) | 1.0 | 2023-01 | 2 | ai_rmf_subcategory | null | AI RMF Core > Subcategories > GOVERN 1.7 | https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf | 7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1 |
AI-100-1/ai_rmf_subcategory/govern-2.1 | GOVERN 2.1: Roles and responsibilities and lines of communication related to mapping, measuring, and managing AI risks are documented and are clear to individuals and teams throughout the organization. | AI-100-1 | Artificial Intelligence Risk Management Framework (AI RMF 1.0) | 1.0 | 2023-01 | 2 | ai_rmf_subcategory | null | AI RMF Core > Subcategories > GOVERN 2.1 | https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf | 7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1 |
AI-100-1/ai_rmf_subcategory/govern-2.2 | GOVERN 2.2: The organization's personnel and partners receive AI risk management training to enable them to perform their duties and responsibilities consistent with related policies, procedures, and agreements. | AI-100-1 | Artificial Intelligence Risk Management Framework (AI RMF 1.0) | 1.0 | 2023-01 | 2 | ai_rmf_subcategory | null | AI RMF Core > Subcategories > GOVERN 2.2 | https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf | 7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1 |
AI-100-1/ai_rmf_subcategory/govern-2.3 | GOVERN 2.3: Executive leadership of the organization takes responsibility for decisions about risks associated with AI system development and deployment. GOVERN 3: Workforce diversity, equity, inclusion, and accessibility processes are prioritized in the mapping, measuring, and managing of AI risks throughout the lifec... | AI-100-1 | Artificial Intelligence Risk Management Framework (AI RMF 1.0) | 1.0 | 2023-01 | 2 | ai_rmf_subcategory | null | AI RMF Core > Subcategories > GOVERN 2.3 | https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf | 7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1 |
AI-100-1/ai_rmf_subcategory/govern-3.1 | GOVERN 3.1: Decision-making related to mapping, measuring, and managing AI risks throughout the lifecycle is informed by a diverse team (e.g., diversity of demographics, disciplines, experience, expertise, and backgrounds). | AI-100-1 | Artificial Intelligence Risk Management Framework (AI RMF 1.0) | 1.0 | 2023-01 | 2 | ai_rmf_subcategory | null | AI RMF Core > Subcategories > GOVERN 3.1 | https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf | 7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1 |
AI-100-1/ai_rmf_subcategory/govern-3.2 | GOVERN 3.2: Policies and procedures are in place to define and differentiate roles and responsibilities for human-AI configurations and oversight of AI systems. GOVERN 4: Organizational teams are committed to a culture | AI-100-1 | Artificial Intelligence Risk Management Framework (AI RMF 1.0) | 1.0 | 2023-01 | 2 | ai_rmf_subcategory | null | AI RMF Core > Subcategories > GOVERN 3.2 | https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf | 7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1 |
AI-100-1/ai_rmf_subcategory/govern-4.1 | GOVERN 4.1: Organizational policies and practices are in place to foster a critical thinking and safety-first mindset in the design, development, deployment, and uses of AI systems to minimize potential negative impacts. Categories Subcategories Continued on next page Table 1: Categories and subcategories for the GOVER... | AI-100-1 | Artificial Intelligence Risk Management Framework (AI RMF 1.0) | 1.0 | 2023-01 | 2 | ai_rmf_subcategory | null | AI RMF Core > Subcategories > GOVERN 4.1 | https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf | 7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1 |
AI-100-1/ai_rmf_subcategory/govern-4.2 | GOVERN 4.2: Organizational teams document the risks and potential impacts of the AI technology they design, develop, deploy, evaluate, and use, and they communicate about the impacts more broadly. | AI-100-1 | Artificial Intelligence Risk Management Framework (AI RMF 1.0) | 1.0 | 2023-01 | 2 | ai_rmf_subcategory | null | AI RMF Core > Subcategories > GOVERN 4.2 | https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf | 7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1 |
AI-100-1/ai_rmf_subcategory/govern-4.3 | GOVERN 4.3: Organizational practices are in place to enable AI testing, identification of incidents, and information sharing. GOVERN 5: Processes are in place for robust engagement with relevant AI actors. | AI-100-1 | Artificial Intelligence Risk Management Framework (AI RMF 1.0) | 1.0 | 2023-01 | 2 | ai_rmf_subcategory | null | AI RMF Core > Subcategories > GOVERN 4.3 | https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf | 7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1 |
AI-100-1/ai_rmf_subcategory/govern-5.1 | GOVERN 5.1: Organizational policies and practices are in place to collect, consider, prioritize, and integrate feedback from those external to the team that developed or deployed the AI system regarding the potential individual and societal impacts related to AI risks. | AI-100-1 | Artificial Intelligence Risk Management Framework (AI RMF 1.0) | 1.0 | 2023-01 | 2 | ai_rmf_subcategory | null | AI RMF Core > Subcategories > GOVERN 5.1 | https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf | 7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1 |
AI-100-1/ai_rmf_subcategory/govern-5.2 | GOVERN 5.2: Mechanisms are established to enable the team that developed or deployed AI systems to regularly incorporate adjudicated feedback from relevant AI actors into system design and implementation. GOVERN 6: Policies and procedures are in place to address AI risks and benefits arising from third-party software a... | AI-100-1 | Artificial Intelligence Risk Management Framework (AI RMF 1.0) | 1.0 | 2023-01 | 2 | ai_rmf_subcategory | null | AI RMF Core > Subcategories > GOVERN 5.2 | https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf | 7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1 |
AI-100-1/ai_rmf_subcategory/govern-6.1 | GOVERN 6.1: Policies and procedures are in place that address AI risks associated with third-party entities, including risks of infringement of a third-party's intellectual property or other rights. | AI-100-1 | Artificial Intelligence Risk Management Framework (AI RMF 1.0) | 1.0 | 2023-01 | 2 | ai_rmf_subcategory | null | AI RMF Core > Subcategories > GOVERN 6.1 | https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf | 7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1 |
AI-100-1/ai_rmf_subcategory/govern-6.2 | GOVERN 6.2: Contingency processes are in place to handle failures or incidents in third-party data or AI systems deemed to be high-risk. Categories Subcategories 5.2 Map The MAP function establishes the context to frame risks related to an AI system. The AI lifecycle consists of many interdependent activities involving... | AI-100-1 | Artificial Intelligence Risk Management Framework (AI RMF 1.0) | 1.0 | 2023-01 | 2 | ai_rmf_subcategory | null | AI RMF Core > Subcategories > GOVERN 6.2 | https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf | 7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1 |
AI-100-1/ai_rmf_subcategory/manage-1.1 | MANAGE 1.1: A determination is made as to whether the AI system achieves its intended purposes and stated objectives and whether its development or deployment should proceed. | AI-100-1 | Artificial Intelligence Risk Management Framework (AI RMF 1.0) | 1.0 | 2023-01 | 2 | ai_rmf_subcategory | null | AI RMF Core > Manage > MANAGE 1.1 | https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf | 7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1 |
AI-100-1/ai_rmf_subcategory/manage-1.2 | MANAGE 1.2: Treatment of documented AI risks is prioritized based on impact, likelihood, and available resources or methods. | AI-100-1 | Artificial Intelligence Risk Management Framework (AI RMF 1.0) | 1.0 | 2023-01 | 2 | ai_rmf_subcategory | null | AI RMF Core > Manage > MANAGE 1.2 | https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf | 7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1 |
AI-100-1/ai_rmf_subcategory/manage-1.3 | MANAGE 1.3: Responses to the AI risks deemed high priority, as identified by the MAP function, are developed, planned, and documented. Risk response options can include mitigating, transferring, avoiding, or accepting. | AI-100-1 | Artificial Intelligence Risk Management Framework (AI RMF 1.0) | 1.0 | 2023-01 | 2 | ai_rmf_subcategory | null | AI RMF Core > Manage > MANAGE 1.3 | https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf | 7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1 |
AI-100-1/ai_rmf_subcategory/manage-1.4 | MANAGE 1.4: Negative residual risks (defined as the sum of all unmitigated risks) to both downstream acquirers of AI systems and end users are documented. MANAGE 2: Strategies to maximize AI benefits and minimize negative impacts are planned, prepared, implemented, documented, and informed by input from relevant AI act... | AI-100-1 | Artificial Intelligence Risk Management Framework (AI RMF 1.0) | 1.0 | 2023-01 | 2 | ai_rmf_subcategory | null | AI RMF Core > Manage > MANAGE 1.4 | https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf | 7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1 |
AI-100-1/ai_rmf_subcategory/manage-2.1 | MANAGE 2.1: Resources required to manage AI risks are taken into account - along with viable non-AI alternative systems, approaches, or methods - to reduce the magnitude or likelihood of potential impacts. | AI-100-1 | Artificial Intelligence Risk Management Framework (AI RMF 1.0) | 1.0 | 2023-01 | 2 | ai_rmf_subcategory | null | AI RMF Core > Manage > MANAGE 2.1 | https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf | 7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1 |
AI-100-1/ai_rmf_subcategory/manage-2.2 | MANAGE 2.2: Mechanisms are in place and applied to sustain the value of deployed AI systems. | AI-100-1 | Artificial Intelligence Risk Management Framework (AI RMF 1.0) | 1.0 | 2023-01 | 2 | ai_rmf_subcategory | null | AI RMF Core > Manage > MANAGE 2.2 | https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf | 7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1 |
AI-100-1/ai_rmf_subcategory/manage-2.3 | MANAGE 2.3: Procedures are followed to respond to and recover from a previously unknown risk when it is identified. | AI-100-1 | Artificial Intelligence Risk Management Framework (AI RMF 1.0) | 1.0 | 2023-01 | 2 | ai_rmf_subcategory | null | AI RMF Core > Manage > MANAGE 2.3 | https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf | 7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1 |
AI-100-1/ai_rmf_subcategory/manage-2.4 | MANAGE 2.4: Mechanisms are in place and applied, and responsibilities are assigned and understood, to supersede, disengage, or deactivate AI systems that demonstrate performance or outcomes inconsistent with intended use. MANAGE 3: AI risks and benefits from third-party entities are managed. | AI-100-1 | Artificial Intelligence Risk Management Framework (AI RMF 1.0) | 1.0 | 2023-01 | 2 | ai_rmf_subcategory | null | AI RMF Core > Manage > MANAGE 2.4 | https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf | 7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1 |
AI-100-1/ai_rmf_subcategory/manage-3.1 | MANAGE 3.1: AI risks and benefits from third-party resources are regularly monitored, and risk controls are applied and documented. | AI-100-1 | Artificial Intelligence Risk Management Framework (AI RMF 1.0) | 1.0 | 2023-01 | 2 | ai_rmf_subcategory | null | AI RMF Core > Manage > MANAGE 3.1 | https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf | 7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1 |
AI-100-1/ai_rmf_subcategory/manage-3.2 | MANAGE 3.2: Pre-trained models which are used for development are monitored as part of AI system regular monitoring and maintenance. Categories Subcategories Continued on next page Table 4: Categories and subcategories for the MANAGE function. (Continued) MANAGE 4: Risk treatments, including response and recovery, and ... | AI-100-1 | Artificial Intelligence Risk Management Framework (AI RMF 1.0) | 1.0 | 2023-01 | 2 | ai_rmf_subcategory | null | AI RMF Core > Manage > MANAGE 3.2 | https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf | 7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1 |
AI-100-1/ai_rmf_subcategory/manage-4.1 | MANAGE 4.1: Post-deployment AI system monitoring plans are implemented, including mechanisms for capturing and evaluating input from users and other relevant AI actors, appeal and override, decommissioning, incident response, recovery, and change management. | AI-100-1 | Artificial Intelligence Risk Management Framework (AI RMF 1.0) | 1.0 | 2023-01 | 2 | ai_rmf_subcategory | null | AI RMF Core > Subcategories > MANAGE 4.1 | https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf | 7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1 |
AI-100-1/ai_rmf_subcategory/manage-4.2 | MANAGE 4.2: Measurable activities for continual improvements are integrated into AI system updates and include regular engagement with interested parties, including relevant AI actors. | AI-100-1 | Artificial Intelligence Risk Management Framework (AI RMF 1.0) | 1.0 | 2023-01 | 2 | ai_rmf_subcategory | null | AI RMF Core > Subcategories > MANAGE 4.2 | https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf | 7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1 |
AI-100-1/ai_rmf_subcategory/manage-4.3 | MANAGE 4.3: Incidents and errors are communicated to relevant AI actors, including affected communities. Processes for tracking, responding to, and recovering from incidents and errors are followed and documented. Categories Subcategories 6. | AI-100-1 | Artificial Intelligence Risk Management Framework (AI RMF 1.0) | 1.0 | 2023-01 | 2 | ai_rmf_subcategory | null | AI RMF Core > Subcategories > MANAGE 4.3 | https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf | 7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1 |
AI-100-1/ai_rmf_subcategory/map-1.1 | MAP 1.1: Intended purposes, potentially beneficial uses, context-specific laws, norms and expectations, and prospective settings in which the AI system will be deployed are understood and documented. Considerations include: the specific set or types of users along with their expectations; potential positive and negativ... | AI-100-1 | Artificial Intelligence Risk Management Framework (AI RMF 1.0) | 1.0 | 2023-01 | 2 | ai_rmf_subcategory | null | AI RMF Core > Map > MAP 1.1 | https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf | 7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1 |
AI-100-1/ai_rmf_subcategory/map-1.2 | MAP 1.2: Interdisciplinary AI actors, competencies, skills, and capacities for establishing context reflect demographic diversity and broad domain and user experience expertise, and their participation is documented. Opportunities for interdisciplinary collaboration are prioritized. | AI-100-1 | Artificial Intelligence Risk Management Framework (AI RMF 1.0) | 1.0 | 2023-01 | 2 | ai_rmf_subcategory | null | AI RMF Core > Map > MAP 1.2 | https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf | 7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1 |
AI-100-1/ai_rmf_subcategory/map-1.3 | MAP 1.3: The organization's mission and relevant goals for AI technology are understood and documented. | AI-100-1 | Artificial Intelligence Risk Management Framework (AI RMF 1.0) | 1.0 | 2023-01 | 2 | ai_rmf_subcategory | null | AI RMF Core > Map > MAP 1.3 | https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf | 7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1 |
AI-100-1/ai_rmf_subcategory/map-1.4 | MAP 1.4: The business value or context of business use has been clearly defined or - in the case of assessing existing AI systems - re-evaluated. | AI-100-1 | Artificial Intelligence Risk Management Framework (AI RMF 1.0) | 1.0 | 2023-01 | 2 | ai_rmf_subcategory | null | AI RMF Core > Map > MAP 1.4 | https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf | 7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1 |
AI-100-1/ai_rmf_subcategory/map-1.6 | MAP 1.6: System requirements (e.g., "the system shall respect the privacy of its users") are elicited from and understood by relevant AI actors. Design decisions take socio-technical implications into account to address AI risks. MAP 2: Categorization of the AI system is performed. | AI-100-1 | Artificial Intelligence Risk Management Framework (AI RMF 1.0) | 1.0 | 2023-01 | 2 | ai_rmf_subcategory | null | AI RMF Core > Map > MAP 1.6 | https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf | 7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1 |
AI-100-1/ai_rmf_subcategory/map-2.1 | MAP 2.1: The specific tasks and methods used to implement the tasks that the AI system will support are defined (e.g., classifiers, generative models, recommenders). | AI-100-1 | Artificial Intelligence Risk Management Framework (AI RMF 1.0) | 1.0 | 2023-01 | 2 | ai_rmf_subcategory | null | AI RMF Core > Map > MAP 2.1 | https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf | 7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1 |
AI-100-1/ai_rmf_subcategory/map-2.2 | MAP 2.2: Information about the AI system's knowledge limits and how system output may be utilized and overseen by humans is documented. Documentation provides sufficient information to assist relevant AI actors when making decisions and taking subsequent actions. Categories Subcategories Continued on next page Table 2:... | AI-100-1 | Artificial Intelligence Risk Management Framework (AI RMF 1.0) | 1.0 | 2023-01 | 2 | ai_rmf_subcategory | null | AI RMF Core > Map > MAP 2.2 | https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf | 7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1 |
AI-100-1/ai_rmf_subcategory/map-2.3 | MAP 2.3: Scientific integrity and TEVV considerations are identified and documented, including those related to experimental design, data collection and selection (e.g., availability, representativeness, suitability), system trustworthiness, and construct validation. MAP 3: AI capabilities, targeted usage, goals, and e... | AI-100-1 | Artificial Intelligence Risk Management Framework (AI RMF 1.0) | 1.0 | 2023-01 | 2 | ai_rmf_subcategory | null | AI RMF Core > Subcategories > MAP 2.3 | https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf | 7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1 |
AI-100-1/ai_rmf_subcategory/map-3.1 | MAP 3.1: Potential benefits of intended AI system functionality and performance are examined and documented. | AI-100-1 | Artificial Intelligence Risk Management Framework (AI RMF 1.0) | 1.0 | 2023-01 | 2 | ai_rmf_subcategory | null | AI RMF Core > Subcategories > MAP 3.1 | https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf | 7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1 |
AI-100-1/ai_rmf_subcategory/map-3.2 | MAP 3.2: Potential costs, including non-monetary costs, which result from expected or realized AI errors or system functionality and trustworthiness - as connected to organizational risk tolerance - are examined and documented. | AI-100-1 | Artificial Intelligence Risk Management Framework (AI RMF 1.0) | 1.0 | 2023-01 | 2 | ai_rmf_subcategory | null | AI RMF Core > Subcategories > MAP 3.2 | https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf | 7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1 |
AI-100-1/ai_rmf_subcategory/map-3.3 | MAP 3.3: Targeted application scope is specified and documented based on the system's capability, established context, and AI system categorization. | AI-100-1 | Artificial Intelligence Risk Management Framework (AI RMF 1.0) | 1.0 | 2023-01 | 2 | ai_rmf_subcategory | null | AI RMF Core > Subcategories > MAP 3.3 | https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf | 7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1 |
AI-100-1/ai_rmf_subcategory/map-3.4 | MAP 3.4: Processes for operator and practitioner proficiency with AI system performance and trustworthiness - and relevant technical standards and certifications - are defined, assessed, and documented. | AI-100-1 | Artificial Intelligence Risk Management Framework (AI RMF 1.0) | 1.0 | 2023-01 | 2 | ai_rmf_subcategory | null | AI RMF Core > Subcategories > MAP 3.4 | https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf | 7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1 |
AI-100-1/ai_rmf_subcategory/map-3.5 | MAP 3.5: Processes for human oversight are defined, assessed, and documented in accordance with organizational policies from the GOVERN function. MAP 4: Risks and benefits are mapped for all components of the AI system including third-party software and data. | AI-100-1 | Artificial Intelligence Risk Management Framework (AI RMF 1.0) | 1.0 | 2023-01 | 2 | ai_rmf_subcategory | null | AI RMF Core > Subcategories > MAP 3.5 | https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf | 7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1 |
AI-100-1/ai_rmf_subcategory/map-4.1 | MAP 4.1: Approaches for mapping AI technology and legal risks of its components - including the use of third-party data or software - are in place, followed, and documented, as are risks of infringement of a third party's intellectual property or other rights. | AI-100-1 | Artificial Intelligence Risk Management Framework (AI RMF 1.0) | 1.0 | 2023-01 | 2 | ai_rmf_subcategory | null | AI RMF Core > Subcategories > MAP 4.1 | https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf | 7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1 |
AI-100-1/ai_rmf_subcategory/map-4.2 | MAP 4.2: Internal risk controls for components of the AI system, including third-party AI technologies, are identified and documented. MAP 5: Impacts to individuals, groups, communities, organizations, and society are characterized. | AI-100-1 | Artificial Intelligence Risk Management Framework (AI RMF 1.0) | 1.0 | 2023-01 | 2 | ai_rmf_subcategory | null | AI RMF Core > Subcategories > MAP 4.2 | https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf | 7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1 |
AI-100-1/ai_rmf_subcategory/map-5.1 | MAP 5.1: Likelihood and magnitude of each identified impact (both potentially beneficial and harmful) based on expected use, past uses of AI systems in similar contexts, public incident reports, feedback from those external to the team that developed or deployed the AI system, or other data are identified and documente... | AI-100-1 | Artificial Intelligence Risk Management Framework (AI RMF 1.0) | 1.0 | 2023-01 | 2 | ai_rmf_subcategory | null | AI RMF Core > Subcategories > MAP 5.1 | https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf | 7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1 |
AI-100-1/ai_rmf_subcategory/map-5.2 | MAP 5.2: Practices and personnel for supporting regular engagement with relevant AI actors and integrating feedback about positive, negative, and unanticipated impacts are in place and documented. Categories Subcategories 5.3 Measure The MEASURE function employs quantitative, qualitative, or mixed-method tools, techniq... | AI-100-1 | Artificial Intelligence Risk Management Framework (AI RMF 1.0) | 1.0 | 2023-01 | 2 | ai_rmf_subcategory | null | AI RMF Core > Subcategories > MAP 5.2 | https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf | 7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1 |
AI-100-1/ai_rmf_subcategory/measure-1.1 | MEASURE 1.1: Approaches and metrics for measurement of AI risks enumerated during the MAP function are selected for implementation starting with the most significant AI risks. The risks or trustworthiness characteristics that will not - or cannot - be measured are properly documented. | AI-100-1 | Artificial Intelligence Risk Management Framework (AI RMF 1.0) | 1.0 | 2023-01 | 2 | ai_rmf_subcategory | null | AI RMF Core > Measure > MEASURE 1.1 | https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf | 7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1 |
AI-100-1/ai_rmf_subcategory/measure-1.2 | MEASURE 1.2: Appropriateness of AI metrics and effectiveness of existing controls are regularly assessed and updated, including reports of errors and potential impacts on affected communities. | AI-100-1 | Artificial Intelligence Risk Management Framework (AI RMF 1.0) | 1.0 | 2023-01 | 2 | ai_rmf_subcategory | null | AI RMF Core > Measure > MEASURE 1.2 | https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf | 7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1 |
AI-100-1/ai_rmf_subcategory/measure-1.3 | MEASURE 1.3: Internal experts who did not serve as front-line developers for the system and/or independent assessors are involved in regular assessments and updates. Domain experts, users, AI actors external to the team that developed or deployed the AI system, and affected communities are consulted in support of asses... | AI-100-1 | Artificial Intelligence Risk Management Framework (AI RMF 1.0) | 1.0 | 2023-01 | 2 | ai_rmf_subcategory | null | AI RMF Core > Measure > MEASURE 1.3 | https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf | 7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1 |
AI-100-1/ai_rmf_subcategory/measure-2.1 | MEASURE 2.1: Test sets, metrics, and details about the tools used during TEVV are documented. | AI-100-1 | Artificial Intelligence Risk Management Framework (AI RMF 1.0) | 1.0 | 2023-01 | 2 | ai_rmf_subcategory | null | AI RMF Core > Measure > MEASURE 2.1 | https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf | 7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1 |
AI-100-1/ai_rmf_subcategory/measure-2.10 | MEASURE 2.10: Privacy risk of the AI system - as identified in the MAP function - is examined and documented. | AI-100-1 | Artificial Intelligence Risk Management Framework (AI RMF 1.0) | 1.0 | 2023-01 | 2 | ai_rmf_subcategory | null | AI RMF Core > Subcategories > MEASURE 2.10 | https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf | 7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1 |
AI-100-1/ai_rmf_subcategory/measure-2.11 | MEASURE 2.11: Fairness and bias - as identified in the MAP function - are evaluated and results are documented. | AI-100-1 | Artificial Intelligence Risk Management Framework (AI RMF 1.0) | 1.0 | 2023-01 | 2 | ai_rmf_subcategory | null | AI RMF Core > Subcategories > MEASURE 2.11 | https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf | 7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1 |
AI-100-1/ai_rmf_subcategory/measure-2.12 | MEASURE 2.12: Environmental impact and sustainability of AI model training and management activities - as identified in the MAP function - are assessed and documented. | AI-100-1 | Artificial Intelligence Risk Management Framework (AI RMF 1.0) | 1.0 | 2023-01 | 2 | ai_rmf_subcategory | null | AI RMF Core > Subcategories > MEASURE 2.12 | https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf | 7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1 |
AI-100-1/ai_rmf_subcategory/measure-2.13 | MEASURE 2.13: Effectiveness of the employed TEVV metrics and processes in the MEASURE function are evaluated and documented. MEASURE 3: Mechanisms for tracking identified AI risks over time are in place. | AI-100-1 | Artificial Intelligence Risk Management Framework (AI RMF 1.0) | 1.0 | 2023-01 | 2 | ai_rmf_subcategory | null | AI RMF Core > Subcategories > MEASURE 2.13 | https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf | 7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1 |
AI-100-1/ai_rmf_subcategory/measure-2.2 | MEASURE 2.2: Evaluations involving human subjects meet applicable requirements (including human subject protection) and are representative of the relevant population. | AI-100-1 | Artificial Intelligence Risk Management Framework (AI RMF 1.0) | 1.0 | 2023-01 | 2 | ai_rmf_subcategory | null | AI RMF Core > Measure > MEASURE 2.2 | https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf | 7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1 |
AI-100-1/ai_rmf_subcategory/measure-2.3 | MEASURE 2.3: AI system performance or assurance criteria are measured qualitatively or quantitatively and demonstrated for conditions similar to deployment setting(s). Measures are documented. | AI-100-1 | Artificial Intelligence Risk Management Framework (AI RMF 1.0) | 1.0 | 2023-01 | 2 | ai_rmf_subcategory | null | AI RMF Core > Measure > MEASURE 2.3 | https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf | 7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1 |
AI-100-1/ai_rmf_subcategory/measure-2.4 | MEASURE 2.4: The functionality and behavior of the AI system and its components - as identified in the MAP function - are monitored when in production. | AI-100-1 | Artificial Intelligence Risk Management Framework (AI RMF 1.0) | 1.0 | 2023-01 | 2 | ai_rmf_subcategory | null | AI RMF Core > Measure > MEASURE 2.4 | https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf | 7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1 |
AI-100-1/ai_rmf_subcategory/measure-2.5 | MEASURE 2.5: The AI system to be deployed is demonstrated to be valid and reliable. Limitations of the generalizability beyond the conditions under which the technology was developed are documented. Categories Subcategories Continued on next page Table 3: Categories and subcategories for the MEASURE function. (Continue... | AI-100-1 | Artificial Intelligence Risk Management Framework (AI RMF 1.0) | 1.0 | 2023-01 | 2 | ai_rmf_subcategory | null | AI RMF Core > Measure > MEASURE 2.5 | https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf | 7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1 |
AI-100-1/ai_rmf_subcategory/measure-2.6 | MEASURE 2.6: The AI system is evaluated regularly for safety risks - as identified in the MAP function. The AI system to be deployed is demonstrated to be safe, its residual negative risk does not exceed the risk tolerance, and it can fail safely, particularly if made to operate beyond its knowledge limits. Safety metr... | AI-100-1 | Artificial Intelligence Risk Management Framework (AI RMF 1.0) | 1.0 | 2023-01 | 2 | ai_rmf_subcategory | null | AI RMF Core > Subcategories > MEASURE 2.6 | https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf | 7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1 |
AI-100-1/ai_rmf_subcategory/measure-2.7 | MEASURE 2.7: AI system security and resilience - as identified in the MAP function - are evaluated and documented. | AI-100-1 | Artificial Intelligence Risk Management Framework (AI RMF 1.0) | 1.0 | 2023-01 | 2 | ai_rmf_subcategory | null | AI RMF Core > Subcategories > MEASURE 2.7 | https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf | 7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1 |
AI-100-1/ai_rmf_subcategory/measure-2.8 | MEASURE 2.8: Risks associated with transparency and accountability - as identified in the MAP function - are examined and documented. | AI-100-1 | Artificial Intelligence Risk Management Framework (AI RMF 1.0) | 1.0 | 2023-01 | 2 | ai_rmf_subcategory | null | AI RMF Core > Subcategories > MEASURE 2.8 | https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf | 7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1 |
AI-100-1/ai_rmf_subcategory/measure-2.9 | MEASURE 2.9: The AI model is explained, validated, and documented, and AI system output is interpreted within its context - as identified in the MAP function - to inform responsible use and governance. | AI-100-1 | Artificial Intelligence Risk Management Framework (AI RMF 1.0) | 1.0 | 2023-01 | 2 | ai_rmf_subcategory | null | AI RMF Core > Subcategories > MEASURE 2.9 | https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf | 7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1 |
AI-100-1/ai_rmf_subcategory/measure-3.1 | MEASURE 3.1: Approaches, personnel, and documentation are in place to regularly identify and track existing, unanticipated, and emergent AI risks based on factors such as intended and actual performance in deployed contexts. | AI-100-1 | Artificial Intelligence Risk Management Framework (AI RMF 1.0) | 1.0 | 2023-01 | 2 | ai_rmf_subcategory | null | AI RMF Core > Subcategories > MEASURE 3.1 | https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf | 7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1 |
AI-100-1/ai_rmf_subcategory/measure-3.2 | MEASURE 3.2: Risk tracking approaches are considered for settings where AI risks are difficult to assess using currently available measurement techniques or where metrics are not yet available. Categories Subcategories Continued on next page Table 3: Categories and subcategories for the MEASURE function. (Continued) | AI-100-1 | Artificial Intelligence Risk Management Framework (AI RMF 1.0) | 1.0 | 2023-01 | 2 | ai_rmf_subcategory | null | AI RMF Core > Subcategories > MEASURE 3.2 | https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf | 7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1 |
AI-100-1/ai_rmf_subcategory/measure-3.3 | MEASURE 3.3: Feedback processes for end users and impacted communities to report problems and appeal system outcomes are established and integrated into AI system evaluation metrics. MEASURE 4: Feedback about efficacy of measurement is gathered and assessed. | AI-100-1 | Artificial Intelligence Risk Management Framework (AI RMF 1.0) | 1.0 | 2023-01 | 2 | ai_rmf_subcategory | null | AI RMF Core > Subcategories > MEASURE 3.3 | https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf | 7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1 |
AI-100-1/ai_rmf_subcategory/measure-4.1 | MEASURE 4.1: Measurement approaches for identifying AI risks are connected to deployment context(s) and informed through consultation with domain experts and other end users. Approaches are documented. | AI-100-1 | Artificial Intelligence Risk Management Framework (AI RMF 1.0) | 1.0 | 2023-01 | 2 | ai_rmf_subcategory | null | AI RMF Core > Subcategories > MEASURE 4.1 | https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf | 7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1 |
AI-100-1/ai_rmf_subcategory/measure-4.2 | MEASURE 4.2: Measurement results regarding AI system trustworthiness in deployment context(s) and across the AI lifecycle are informed by input from domain experts and relevant AI actors to validate whether the system is performing consistently as intended. Results are documented. | AI-100-1 | Artificial Intelligence Risk Management Framework (AI RMF 1.0) | 1.0 | 2023-01 | 2 | ai_rmf_subcategory | null | AI RMF Core > Subcategories > MEASURE 4.2 | https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf | 7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1 |
AI-100-1/ai_rmf_subcategory/measure-4.3 | MEASURE 4.3: Measurable performance improvements or declines based on consultations with relevant AI actors, including affected communities, and field data about contextrelevant risks and trustworthiness characteristics are identified and documented. Categories Subcategories 5.4 Manage The MANAGE function entails alloc... | AI-100-1 | Artificial Intelligence Risk Management Framework (AI RMF 1.0) | 1.0 | 2023-01 | 2 | ai_rmf_subcategory | null | AI RMF Core > Subcategories > MEASURE 4.3 | https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf | 7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1 |
AI-100-1/section/accountable-and-transparent | Accountable and Transparent
Trustworthy AI depends upon accountability. Accountability presupposes transparency.
Transparency reflects the extent to which information about an AI system and its outputs is
available to individuals interacting with such a system - regardless of whether they are even
aware that they are ... | AI-100-1 | Artificial Intelligence Risk Management Framework (AI RMF 1.0) | 1.0 | 2023-01 | 2 | section | null | AI Risks and Trustworthiness > Accountable and Transparent | https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf | 7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1 |
AI-100-1/section/additional-ai-actors | Additional AI Actors
Third-party entities include providers, developers, vendors, and evaluators of data, al-
gorithms, models, and/or systems and related services for another organization or the or-
ganization's customers or clients. Third-party entities are responsible for AI design and
development tasks, in whole o... | AI-100-1 | Artificial Intelligence Risk Management Framework (AI RMF 1.0) | 1.0 | 2023-01 | 2 | section | null | Appendix A: > Descriptions of AI Actor Tasks from Figures 2 and 3 > Additional AI Actors | https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf | 7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1 |
AI-100-1/section/ai-risk-management-and-human-ai-interaction | AI Risk Management and Human-AI Interaction
Organizations that design, develop, or deploy AI systems for use in operational settings
may enhance their AI risk management by understanding current limitations of human-
AI interaction. The AI RMF provides opportunities to clearly define and differentiate the
various huma... | AI-100-1 | Artificial Intelligence Risk Management Framework (AI RMF 1.0) | 1.0 | 2023-01 | 2 | section | null | Appendix C: > AI Risk Management and Human-AI Interaction | https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf | 7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1 |
AI-100-1/section/ai-risks-and-trustworthiness | AI Risks and Trustworthiness
For AI systems to be trustworthy, they often need to be responsive to a multiplicity of cri-
teria that are of value to interested parties. Approaches which enhance AI trustworthiness
can reduce negative AI risks. This Framework articulates the following characteristics of
trustworthy AI a... | AI-100-1 | Artificial Intelligence Risk Management Framework (AI RMF 1.0) | 1.0 | 2023-01 | 2 | section | null | AI Risks and Trustworthiness | https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf | 7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1 |
AI-100-1/section/ai-rmf-core | AI RMF Core
The AI RMF Core provides outcomes and actions that enable dialogue, understanding, and
activities to manage AI risks and responsibly develop trustworthy AI systems. As illus-
trated in Figure 5, the Core is composed of four functions: GOVERN, MAP, MEASURE,
and MANAGE. Each of these high-level functions is ... | AI-100-1 | Artificial Intelligence Risk Management Framework (AI RMF 1.0) | 1.0 | 2023-01 | 2 | section | null | AI RMF Core | https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf | 7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1 |
AI-100-1/section/attributes-of-the-ai-rmf | Attributes of the AI RMF
NIST described several key attributes of the AI RMF when work on the Framework first
began. These attributes have remained intact and were used to guide the AI RMF's devel-
opment. They are provided here as a reference.
The AI RMF strives to:
1. Be risk-based, resource-efficient, pro-innovatio... | AI-100-1 | Artificial Intelligence Risk Management Framework (AI RMF 1.0) | 1.0 | 2023-01 | 2 | section | null | Appendix D: > Attributes of the AI RMF | https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf | 7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1 |
AI-100-1/section/audience | Audience
Identifying and managing AI risks and potential impacts - both positive and negative - re-
quires a broad set of perspectives and actors across the AI lifecycle. Ideally, AI actors will
represent a diversity of experience, expertise, and backgrounds and comprise demograph-
ically and disciplinarily diverse te... | AI-100-1 | Artificial Intelligence Risk Management Framework (AI RMF 1.0) | 1.0 | 2023-01 | 2 | section | null | Audience | https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf | 7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1 |
AI-100-1/section/descriptions-of-ai-actor-tasks-from-figures-2-and-3 | Descriptions of AI Actor Tasks from Figures 2 and 3
AI Design tasks are performed during the Application Context and Data and Input phases
of the AI lifecycle in Figure 2. AI Design actors create the concept and objectives of AI
systems and are responsible for the planning, design, and data collection and processing
t... | AI-100-1 | Artificial Intelligence Risk Management Framework (AI RMF 1.0) | 1.0 | 2023-01 | 2 | section | null | Appendix A: > Descriptions of AI Actor Tasks from Figures 2 and 3 | https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf | 7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1 |
AI-100-1/section/effectiveness-of-the-ai-rmf | Effectiveness of the AI RMF
Evaluations of AI RMF effectiveness - including ways to measure bottom-line improve-
ments in the trustworthiness of AI systems - will be part of future NIST activities, in
conjunction with the AI community.
Organizations and other users of the Framework are encouraged to periodically evalu... | AI-100-1 | Artificial Intelligence Risk Management Framework (AI RMF 1.0) | 1.0 | 2023-01 | 2 | section | null | Effectiveness of the AI RMF | https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf | 7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1 |
AI-100-1/section/executive-summary | Executive Summary
Artificial intelligence (AI) technologies have significant potential to transform society and
people's lives - from commerce and health to transportation and cybersecurity to the envi-
ronment and our planet. AI technologies can drive inclusive economic growth and support
scientific advancements that... | AI-100-1 | Artificial Intelligence Risk Management Framework (AI RMF 1.0) | 1.0 | 2023-01 | 2 | section | null | Executive Summary | https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf | 7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1 |
AI-100-1/section/explainable-and-interpretable | Explainable and Interpretable
Explainability refers to a representation of the mechanisms underlying AI systems' oper-
ation, whereas interpretability refers to the meaning of AI systems' output in the context
of their designed functional purposes. Together, explainability and interpretability assist
those operating o... | AI-100-1 | Artificial Intelligence Risk Management Framework (AI RMF 1.0) | 1.0 | 2023-01 | 2 | section | null | AI Risks and Trustworthiness > Explainable and Interpretable | https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf | 7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1 |
AI-100-1/section/fair-with-harmful-bias-managed | Fair - with Harmful Bias Managed
Fairness in AI includes concerns for equality and equity by addressing issues such as harm-
ful bias and discrimination. Standards of fairness can be complex and difficult to define be-
cause perceptions of fairness differ among cultures and may shift depending on application.
Organiza... | AI-100-1 | Artificial Intelligence Risk Management Framework (AI RMF 1.0) | 1.0 | 2023-01 | 2 | section | null | AI Risks and Trustworthiness > Fair – with Harmful Bias Managed | https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf | 7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1 |
AI-100-1/section/framework-ai-rmf-1.0 | Framework (AI RMF 1.0)
January 2023
Gina M. Raimondo, Secretary
Laurie E. Locascio, NIST Director and Under Secretary of Commerce for Standards and Technology
Certain commercial entities, equipment, or materials may be identified in this document in order to describe
an experimental procedure or concept adequately. Su... | AI-100-1 | Artificial Intelligence Risk Management Framework (AI RMF 1.0) | 1.0 | 2023-01 | 2 | section | null | Framework (AI RMF 1.0) | https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf | 7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1 |
AI-100-1/section/framing-risk | Framing Risk
AI risk management offers a path to minimize potential negative impacts of AI systems,
such as threats to civil liberties and rights, while also providing opportunities to maximize
positive impacts. Addressing, documenting, and managing AI risks and potential negative
impacts effectively can lead to more ... | AI-100-1 | Artificial Intelligence Risk Management Framework (AI RMF 1.0) | 1.0 | 2023-01 | 2 | section | null | Framing Risk | https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf | 7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1 |
AI-100-1/section/govern | Govern
The GOVERN function:
• cultivates and implements a culture of risk management within organizations design-
ing, developing, deploying, evaluating, or acquiring AI systems;
• outlines processes, documents, and organizational schemes that anticipate, identify,
and manage the risks a system can pose, including to ... | AI-100-1 | Artificial Intelligence Risk Management Framework (AI RMF 1.0) | 1.0 | 2023-01 | 2 | section | null | AI RMF Core > Govern | https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf | 7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1 |
AI-100-1/section/how-ai-risks-differ-from-traditional-software-risks | How AI Risks Differ from Traditional Software Risks
As with traditional software, risks from AI-based technology can be bigger than an en-
terprise, span organizations, and lead to societal impacts. AI systems also bring a set of
risks that are not comprehensively addressed by current risk frameworks and approaches.
S... | AI-100-1 | Artificial Intelligence Risk Management Framework (AI RMF 1.0) | 1.0 | 2023-01 | 2 | section | null | Appendix B: > How AI Risks Differ from Traditional Software Risks | https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf | 7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1 |
AI-100-1/section/need-to-be-clearly-defined-and-differentiated.-human-ai-configurations-can-span | 1. Human roles and responsibilities in decision making and overseeing AI systems
need to be clearly defined and differentiated. Human-AI configurations can span
from fully autonomous to fully manual. AI systems can autonomously make deci-
sions, defer decision making to a human expert, or be used by a human decision
... | AI-100-1 | Artificial Intelligence Risk Management Framework (AI RMF 1.0) | 1.0 | 2023-01 | 2 | section | null | Appendix C: > AI Risk Management and Human-AI Interaction > need to be clearly defined and differentiated. Human-AI configurations can span | https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf | 7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1 |
AI-100-1/section/organizational-integration-and-management-of-risk | Organizational Integration and Management of Risk
AI risks should not be considered in isolation. Different AI actors have different responsi-
bilities and awareness depending on their roles in the lifecycle. For example, organizations
developing an AI system often will not have information about how the system may be... | AI-100-1 | Artificial Intelligence Risk Management Framework (AI RMF 1.0) | 1.0 | 2023-01 | 2 | section | null | Framing Risk > Organizational Integration and Management of Risk | https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf | 7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1 |
AI-100-1/section/privacy-enhanced | Privacy-Enhanced
Privacy refers generally to the norms and practices that help to safeguard human autonomy,
identity, and dignity. These norms and practices typically address freedom from intrusion,
limiting observation, or individuals' agency to consent to disclosure or control of facets of
their identities (e.g., bo... | AI-100-1 | Artificial Intelligence Risk Management Framework (AI RMF 1.0) | 1.0 | 2023-01 | 2 | section | null | AI Risks and Trustworthiness > Privacy-Enhanced | https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf | 7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1 |
AI-100-1/section/risk-measurement | Challenges for AI Risk Management
Several challenges are described below. They should be taken into account when managing
risks in pursuit of AI trustworthiness.
Risk Measurement
AI risks or failures that are not well-defined or adequately understood are difficult to mea-
sure quantitatively or qualitatively. The in... | AI-100-1 | Artificial Intelligence Risk Management Framework (AI RMF 1.0) | 1.0 | 2023-01 | 2 | section | null | Framing Risk > Risk Measurement | https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf | 7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1 |
AI-100-1/section/risk-prioritization | Risk Prioritization
Attempting to eliminate negative risk entirely can be counterproductive in practice because
not all incidents and failures can be eliminated. Unrealistic expectations about risk may
lead organizations to allocate resources in a manner that makes risk triage inefficient or
impractical or wastes scar... | AI-100-1 | Artificial Intelligence Risk Management Framework (AI RMF 1.0) | 1.0 | 2023-01 | 2 | section | null | Framing Risk > Risk Prioritization | https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf | 7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1 |
AI-100-1/section/risk-tolerance | Risk Tolerance
While the AI RMF can be used to prioritize risk, it does not prescribe risk tolerance. Risk
tolerance refers to the organization's or AI actor's (see Appendix A) readiness to bear the
risk in order to achieve its objectives. Risk tolerance can be influenced by legal or regula-
tory requirements (Adapted... | AI-100-1 | Artificial Intelligence Risk Management Framework (AI RMF 1.0) | 1.0 | 2023-01 | 2 | section | null | Framing Risk > Risk Tolerance | https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf | 7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1 |
AI-100-1/section/safe | Safe
AI systems should "not under defined conditions, lead to a state in which human life,
health, property, or the environment is endangered" (Source: ISO/IEC TS 5723:2022). Safe
operation of AI systems is improved through:
• responsible design, development, and deployment practices;
• clear information to deployers ... | AI-100-1 | Artificial Intelligence Risk Management Framework (AI RMF 1.0) | 1.0 | 2023-01 | 2 | section | null | AI Risks and Trustworthiness > Safe | https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf | 7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1 |
AI-100-1/section/secure-and-resilient | Secure and Resilient
AI systems, as well as the ecosystems in which they are deployed, may be said to be re-
silient if they can withstand unexpected adverse events or unexpected changes in their envi-
ronment or use - or if they can maintain their functions and structure in the face of internal
and external change an... | AI-100-1 | Artificial Intelligence Risk Management Framework (AI RMF 1.0) | 1.0 | 2023-01 | 2 | section | null | AI Risks and Trustworthiness > Secure and Resilient | https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf | 7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1 |
AI-100-1/section/understanding-and-addressing-risks-impacts-and-harms | Understanding and Addressing Risks, Impacts, and Harms
In the context of the AI RMF, risk refers to the composite measure of an event's probability
of occurring and the magnitude or degree of the consequences of the corresponding event.
The impacts, or consequences, of AI systems can be positive, negative, or both and... | AI-100-1 | Artificial Intelligence Risk Management Framework (AI RMF 1.0) | 1.0 | 2023-01 | 2 | section | null | Framing Risk > Understanding and Addressing Risks, Impacts, and Harms | https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf | 7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1 |
AI-100-1/section/valid-and-reliable | Valid and Reliable
Validation is the "confirmation, through the provision of objective evidence, that the re-
quirements for a specific intended use or application have been fulfilled" (Source: ISO
9000:2015). Deployment of AI systems which are inaccurate, unreliable, or poorly gener-
alized to data and settings beyon... | AI-100-1 | Artificial Intelligence Risk Management Framework (AI RMF 1.0) | 1.0 | 2023-01 | 2 | section | null | AI Risks and Trustworthiness > Valid and Reliable | https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf | 7576edb531d9848825814ee88e28b1795d3a84b435b4b797d3670eafdc4a89f1 |
AI-RMF-PLAYBOOK/ai_rmf_subcategory/govern-1.1 | GOVERN 1.1: Legal and regulatory requirements involving AI are understood, managed, and documented.
About:
AI systems may be subject to specific applicable legal and regulatory requirements. Some legal requirements can mandate (e.g., nondiscrimination, data privacy and security controls) documentation, disclosure, and... | AI-RMF-PLAYBOOK | NIST AI RMF Playbook | current web version at build time | rolling | 2 | ai_rmf_subcategory | null | AI RMF Playbook > Govern > GOVERN 1.1 | https://airc.nist.gov/docs/playbook.json | aecbee3d3c8820816d295b11d10fb61324b17c25c2ff39ee95e2aa5654555bba |
AI-RMF-PLAYBOOK/ai_rmf_subcategory/govern-1.2 | GOVERN 1.2: The characteristics of trustworthy AI are integrated into organizational policies, processes, and procedures.
About:
Policies, processes, and procedures are central components of effective AI risk management and fundamental to individual and organizational accountability. All stakeholders benefit from poli... | AI-RMF-PLAYBOOK | NIST AI RMF Playbook | current web version at build time | rolling | 2 | ai_rmf_subcategory | null | AI RMF Playbook > Govern > GOVERN 1.2 | https://airc.nist.gov/docs/playbook.json | aecbee3d3c8820816d295b11d10fb61324b17c25c2ff39ee95e2aa5654555bba |
AI-RMF-PLAYBOOK/ai_rmf_subcategory/govern-1.3 | GOVERN 1.3: Processes and procedures are in place to determine the needed level of risk management activities based on the organization's risk tolerance.
About:
Risk management resources are finite in any organization. Adequate AI governance policies delineate the mapping, measurement, and prioritization of risks to a... | AI-RMF-PLAYBOOK | NIST AI RMF Playbook | current web version at build time | rolling | 2 | ai_rmf_subcategory | null | AI RMF Playbook > Govern > GOVERN 1.3 | https://airc.nist.gov/docs/playbook.json | aecbee3d3c8820816d295b11d10fb61324b17c25c2ff39ee95e2aa5654555bba |
RMF/ATO Core Corpus
What this is
A curated corpus of current-revision NIST Risk Management Framework and authorization publications, plus a second tier of AI governance documents. Every row is source text — one control, one assessment objective, one RMF task, one AI RMF subcategory, one SSDF practice, one document section — carrying the identifiers a practitioner actually cites. It is built for retrieval and fine-tuning around authorization workflows: control selection, SSP and assessment work, categorization, continuous monitoring, and the emerging AI governance overlay.
5,510 rows, 1.9 MB, single train split. No embeddings, no synthetic Q&A, no system prompts.
Why another NIST dataset
Existing NIST corpora are typically indiscriminate scrapes, and they share four failure modes. Each is prevented here by construction rather than by cleanup:
| Failure mode | How it is prevented |
|---|---|
| Superseded-document contamination — 1989 guidance sitting beside current guidance | A manifest is the sole authority on scope. Every row traces to one manifest entry. Landing pages are checked for supersession at build time; SP 800-18 Rev 1 was found withdrawn during this build and replaced with Rev 2. |
| Fabricated control IDs — "control HA-25", "control WE-12", produced when a chunker turns any two capital letters near a number into an identifier | A row may carry a control_id only if it came from OSCAL structured data, and the ID must match one of the 20 real SP 800-53 Rev 5 families. PDF-derived rows never carry a control ID, even where the prose names one. Both rules are executable checks, not conventions. |
| Baked-in prompts — "You are a cybersecurity expert…" prefixed to every row | Row text is source text. Validation rejects any row containing prompt scaffolding or unrendered template markers. |
| Embedding lock-in — precomputed vectors tying users to one model | Text only. Bring your own embedding model. |
The dataset is deliberately small. It is meant to be right, not exhaustive.
What's included
| doc_id | document | revision | date | tier | format |
|---|---|---|---|---|---|
SP-800-37r2 |
Risk Management Framework for Information Systems and Organizations | Rev 2 | 2018-12 | 1 | |
SP-800-53r5 |
Security and Privacy Controls for Information Systems and Organizations | Rev 5 (OSCAL 5.2.0) | 2020-09 | 1 | OSCAL |
SP-800-53Ar5 |
Assessing Security and Privacy Controls | Rev 5 | 2022-01 | 1 | embedded in OSCAL |
SP-800-53B-LOW/MODERATE/HIGH/PRIVACY |
Control Baselines | Rev 5 (OSCAL 5.2.0) | 2020-10 | 1 | OSCAL |
FIPS-199 |
Standards for Security Categorization | Initial (in force) | 2004-02 | 1 | |
FIPS-200 |
Minimum Security Requirements | Initial (in force) | 2006-03 | 1 | |
SP-800-60v1r1 / v2r1 |
Mapping Information Types to Security Categories, Vols 1–2 | Rev 1 | 2008-08 | 1 | |
SP-800-18r2 |
Developing Security, Privacy, and C-SCRM Plans for Systems | Rev 2 | 2026-06 | 1 | |
SP-800-30r1 |
Guide for Conducting Risk Assessments | Rev 1 | 2012-09 | 1 | |
SP-800-39 |
Managing Information Security Risk | Initial (in force) | 2011-03 | 1 | |
SP-800-137 |
Information Security Continuous Monitoring | Initial (in force) | 2011-09 | 1 | |
AI-100-1 |
AI Risk Management Framework (AI RMF 1.0) | 1.0 | 2023-01 | 2 | |
AI-RMF-PLAYBOOK |
NIST AI RMF Playbook | rolling | 2026-08 | 2 | JSON |
SP-800-218 |
Secure Software Development Framework (SSDF) | 1.1 | 2022-02 | 2 | |
SP-800-218A |
SSDF Community Profile for Generative AI | Initial | 2024-07 | 2 |
SP 800-53A content is extracted from the SP 800-53 Rev 5 OSCAL catalog's embedded assessment
parts, not from a separate 53A file — NIST publishes no standalone 53A OSCAL artifact. Those
rows are attributed to SP-800-53Ar5 and cite the catalog's hash.
Excluded
Superseded or withdrawn revisions (including 182 withdrawn SP 800-53 controls, each logged by name); NIST annual reports and workshop proceedings; pre-2010 legacy publications except FIPS 199/200, which remain in force; and draft publications. Also excluded from v1: CNSSI 1253 and DoD Instruction 8510.01, whose publishers block automated retrieval; and SP 800-60 Volume 2 Appendix E, which reproduces OMB memoranda and legislative provisions as wide reference tables — source material for the impact determinations rather than guidance, and it extracts as citation soup.
Schema
One row = one chunk.
| field | type | notes |
|---|---|---|
id |
string | Deterministic and human-readable: {doc_id}/{chunk_type}/{slug}, e.g. SP-800-53r5/control/ac-2. Oversized rows split into (part n). Stable across versions, so diffs are meaningful. |
text |
string | Source text. Normalized whitespace, paragraph breaks preserved. 80–8,000 chars. |
doc_id |
string | Matches a manifest entry. |
doc_title |
string | From the manifest. |
revision |
string | From the manifest; names the exact OSCAL content release where applicable. |
pub_date |
string | YYYY-MM or YYYY. |
tier |
int32 | 1 = RMF/ATO core, 2 = AI governance. |
chunk_type |
string | control, control_enhancement, control_discussion, assessment_objective, assessment_method, baseline, section, task, ai_rmf_subcategory, ssdf_practice, definition, table. |
control_id |
string or null | Lowercase OSCAL form (ac-2, ac-2.3). Null for every PDF-derived row. |
section_path |
string or null | Where it sits: AC > AC-2 > Discussion, CHAPTER THREE > TASK P-1, SSDF Practices > PO > PO.1 > PO.1.1. |
source_url |
string | The retrieved artifact's URL. |
sha256_source |
string | Hash of the exact artifact the row came from. |
Rows by chunk type
| chunk_type | rows | chunk_type | rows | |
|---|---|---|---|---|
assessment_method |
1,014 | ai_rmf_subcategory |
154 | |
assessment_objective |
1,014 | ssdf_practice |
94 | |
control_discussion |
999 | task |
47 | |
control_enhancement |
714 | baseline |
4 | |
control |
300 | table |
1 | |
section |
872 | |||
definition |
297 |
The 297 definition rows come from five glossaries: SP 800-37r2 (178), SP 800-137
(101), FIPS 199 (13), SP 800-18r2 (3) and FIPS 200 (2). The two large ones mark their
entries typographically rather than with punctuation — SP 800-37 sets terms in bold,
SP 800-137 marks each entry with a smaller bracketed source line — so both are read
from the font, not from a TERM: pattern.
Example rows
{
"id": "SP-800-53r5/control_enhancement/ac-2.3",
"text": "AC-2(3) Account Management | Disable Accounts\nFamily: Access Control (AC) > AC-2 Account Management\n\nDisable accounts within [Assignment: organization-defined time period] when the accounts:\n(a) Have expired;\n(b) Are no longer associated with a user or individual;\n(c) Are in violation of organizational policy; or\n(d) Have been inactive for [Assignment: organization-defined time period].",
"doc_id": "SP-800-53r5",
"revision": "Rev 5 (OSCAL content version 5.2.0)",
"tier": 1,
"chunk_type": "control_enhancement",
"control_id": "ac-2.3",
"section_path": "AC > AC-2 > AC-2(3)"
}
{
"id": "AI-100-1/ai_rmf_subcategory/govern-1.1",
"text": "GOVERN 1.1: Legal and regulatory requirements involving AI are understood, managed, and documented.",
"doc_id": "AI-100-1",
"revision": "1.0",
"tier": 2,
"chunk_type": "ai_rmf_subcategory",
"control_id": null,
"section_path": "AI RMF Core > Govern > GOVERN 1.1"
}
Note the ODP rendering: {{ insert: param, ac-02_odp.01 }} in the OSCAL source becomes
[Assignment: organization-defined …] / [Selection; one or more: …], the convention SP 800-53
itself prints. No template marker survives into any row.
How it was built
01 verify → 02 fetch → 03 parse OSCAL → 04 parse PDF → 05 chunk → 06 validate → 07 export
Each stage is an independently runnable, idempotent script. Source, tests, and the full rejection log live in the GitHub repository: https://github.com/ezesecops/rmf-ato-core
1,131 rows were rejected across the pipeline, every one recorded with a rule and a reason in
rejections.jsonl. Rejected content is logged, never silently dropped.
| rule | rows | what it is |
|---|---|---|
section_too_short |
449 | layout fragments; their text survives, merged into neighbouring sections |
withdrawn_control |
182 | SP 800-53 controls marked withdrawn in OSCAL |
excluded_appendix |
161 | SP 800-60 Vol 2 Appendix E — OMB memoranda and legal-provision tables |
midsentence_fragment |
97 | sections beginning mid-sentence (see Limitations) |
duplicate_task_stub |
96 | RMF task identifiers repeated in summary tables and contents |
trailing_furniture |
36 | stub sections with no sibling to merge into |
bibliography_entry |
29 | reference-list entries: citation apparatus, no guidance |
duplicate_information_type_stub |
26 | SP 800-60 information-type identifiers repeated in contents |
length_bounds |
19 | text outside the length bounds for its chunk type |
empty_discussion |
15 | SP 800-53 discussions whose whole content is "None." |
front_matter |
8 | title pages, forewords, signature blocks, contents |
block_too_short / definition_too_short |
10 | identifier blocks and glossary entries that extracted as fragments |
near_dupe |
3 | text identical to an earlier row; the later row loses |
Three review passes shaped this log after the pipeline first ran end to end. The first added the
supply-chain rules that reject reference entries, running-header remnants and mid-sentence
fragments. The second showed those rules were discarding rows that had real guidance underneath a
damaged first line, so the pipeline now repairs what it can — a running-header remnant or a
leading citation tag over substantive text is stripped and the row is published, and rows are only
rejected when nothing substantive remains. The third recovered content the rules had been hiding:
SP 800-137's glossary became 101 definition rows, and decorative drop caps stopped being read as
headings. running_header_fragment rejected 101 rows before that repair step existed and rejects
none now.
Provenance & integrity
Every artifact was retrieved once, hashed, and recorded. provenance.json ships with the dataset.
| doc_id | revision | retrieved | bytes | sha256 (first 16) |
|---|---|---|---|---|
AI-100-1 |
1.0 | 2026-08-08 | 1,946,127 | 7576edb531d98488… |
AI-RMF-PLAYBOOK |
rolling | 2026-08-08 | 413,720 | aecbee3d3c882081… |
FIPS-199 |
Initial (in force) | 2026-08-08 | 80,356 | 73d19f05f71e30f3… |
FIPS-200 |
Initial (in force) | 2026-08-08 | 218,892 | 107a9b9cdc8eccf3… |
SP-800-137 |
Initial (in force) | 2026-08-08 | 986,916 | 2d1c0bf459f5e1bf… |
SP-800-18r2 |
Rev 2 | 2026-08-08 | 1,313,448 | 640f9124469f285f… |
SP-800-218 |
1.1 | 2026-08-08 | 739,891 | 617746e553a9e2da… |
SP-800-218A |
Initial | 2026-08-08 | 650,661 | e088c8bc75716824… |
SP-800-30r1 |
Rev 1 | 2026-08-08 | 826,897 | f214087f0bdb3593… |
SP-800-37r2 |
Rev 2 | 2026-08-08 | 2,270,327 | 4f75e1136bb905a6… |
SP-800-39 |
Initial (in force) | 2026-08-08 | 1,228,127 | cf680760d171fc59… |
SP-800-53B-HIGH |
Rev 5 (OSCAL 5.2.0) | 2026-08-08 | 12,492 | 60576970caef91b2… |
SP-800-53B-LOW |
Rev 5 (OSCAL 5.2.0) | 2026-08-08 | 7,234 | 8fd206017c8d718b… |
SP-800-53B-MODERATE |
Rev 5 (OSCAL 5.2.0) | 2026-08-08 | 10,498 | 9030dbf1f1316994… |
SP-800-53B-PRIVACY |
Rev 5 (OSCAL 5.2.0) | 2026-08-08 | 6,064 | 7e650c4397ad633e… |
SP-800-53r5 |
Rev 5 (OSCAL 5.2.0) | 2026-08-08 | 10,442,037 | 01f37cf90ea99d92… |
SP-800-60v1r1 |
Vol 1 Rev 1 | 2026-08-08 | 338,329 | 6f13f57f11697efc… |
SP-800-60v2r1 |
Vol 2 Rev 1 | 2026-08-08 | 1,193,436 | 0b4c5128b39a90f1… |
SP-800-53Ar5 rows cite the SP-800-53r5 catalog hash, because that is the artifact they were
extracted from.
Limitations
- PDF section coverage is partial and best-effort. Layout is not structure. Per-unit rows
(controls, tasks, subcategories, practices, definitions) are high-confidence;
sectionrows are the residue of heading detection. Content loss to furniture stripping is under 1% for most documents and about 8% for SP 800-218, whose bold bullet lists and two-line headings fragment worst. - Assessment objectives and methods are one row per control, not per leaf clause. A single determination statement ("account managers are assigned;") is not retrievable on its own. This keeps the corpus at ~5.5k coherent rows rather than ~13k fragments.
- About 97 mid-sentence fragments were rejected rather than published. PDF page breaks,
footnote interleaving and multi-column layout sometimes hand the extractor a passage that starts
partway through a sentence (
transparent the risk perceptions that organizations routinely use…). Those rows are dropped undermidsentence_fragmentand logged with the text that was discarded. Two consequences worth knowing: a small amount of real guidance — mostly in SP 800-30r1 and SP 800-37r2 — is missing from the corpus, and section coverage of those documents is therefore not continuous. Repairing the fragments would mean stitching text across page boundaries, which risks joining passages that were never adjacent; dropping them was the more conservative choice. - CNSSI 1253 and DoDI 8510.01 are absent from v1 — cnss.gov and esd.whs.mil block scripted retrieval, and their control tables were out of scope for v1 regardless.
- SP 800-60 Volume 2 Appendix E is absent (see exclusions). The information-type entries it
supports — 113
D.xrows — are present. - The AI RMF Playbook is a rolling web resource. Its rows reflect the version retrieved on the date above and will drift as NIST updates it.
- Nineteen rows fell outside the length bounds and were rejected: one AI RMF subcategory
(
MAP 1.5, which the Playbook carries in full), two FIPS 199 glossary terms, and sixteen one-line glossary cross-references of the form "See authorization boundary." - This corpus reflects publications as of the build date. NIST revises documents, sometimes without notice — SP 800-18 Rev 1 was withdrawn six weeks before this build. Re-run the pipeline rather than assuming currency.
- Not legal or compliance advice. These are reference texts; authorization decisions belong to the authorizing official.
License
Source documents are works of the United States Government and are in the public domain under 17 U.S.C. § 105. No copyright is claimed in them. The compilation, curation, manifest, and derived structure are released under CC0 1.0 Universal.
Maintainer & citation
Maintained by @ezesecops — https://ezesecops.com
@misc{rmf_ato_core_2026,
author = {Anene, Ebubeze},
title = {RMF/ATO Core Corpus: a curated, provenance-tracked NIST RMF and AI governance dataset},
year = {2026},
publisher = {Hugging Face},
howpublished = {\url{https://huggingface.co/datasets/ezesecops/rmf-ato-core}},
note = {Built 2026-08-08 from current-revision NIST publications}
}
Found a bad row? That is the most useful thing you can report. Open an issue at
https://github.com/ezesecops/rmf-ato-core/issues with the row id — every row traces back
through sha256_source to the exact artifact it came from, so problems are reproducible.
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