NIST AI RMF 1.0: the evidence behind each requirement

72 subcategories. For each, the artefacts auditors commonly ask for, which ones a system already holds, and the first move to stop evidencing it by periodic review.

GOVERN - NIST AI RMF 1.0

GV-1.1Legal and regulatory requirements involving AI are understood, managed, and documented hard GV-1.2The characteristics of trustworthy AI are integrated into organizational policies, processes, and procedures hard GV-1.3Processes and procedures are in place to determine the needed level of risk management activities based on… hard GV-1.4The risk management process and its outcomes are established through transparent policies, procedures, and… hard GV-1.5Ongoing monitoring and periodic review of the risk management process and its outcomes are planned… hard GV-1.6Mechanisms are in place to inventory AI systems and are resourced according to organizational risk priorities hard GV-1.7Processes and procedures are in place for decommissioning and phasing out of AI systems safely and in a… hard GV-2.1Roles and responsibilities and lines of communication related to mapping, measuring, and managing AI risks… hard GV-2.2The organization's personnel and partners receive AI risk management training to enable them to perform their… moderate GV-2.3Executive leadership of the organization takes responsibility for decisions about risks associated with AI… hard GV-3.1Decision-making related to mapping, measuring, and managing AI risks throughout the lifecycle is informed by… hard GV-3.2Policies and procedures are in place to define and differentiate roles and responsibilities for human-AI… hard GV-4.1Organizational policies and practices are in place to foster a critical thinking and safety-first mindset in… hard GV-4.2Organizational teams document the risks and potential impacts of the AI technology they design, develop… hard GV-4.3Organizational practices are in place to enable AI testing, identification of incidents, and information… hard GV-5.1Organizational policies and practices are in place to collect, consider, prioritize, and integrate feedback… hard GV-5.2Mechanisms are established to enable AI actors to regularly incorporate adjudicated feedback from relevant AI… moderate GV-6.1Policies and procedures are in place that address AI risks associated with third-party entities, including… hard GV-6.2Contingency processes are in place to handle failures or incidents in third-party data or AI systems deemed… hard

MANAGE - NIST AI RMF 1.0

MN-1.1A determination is made as to whether the AI system achieves its intended purpose and stated objectives and… hard MN-1.2Treatment of documented AI risks is prioritized based on impact, likelihood, or available resources or methods hard MN-1.3Responses to the AI risks deemed high priority as identified by the MAP function are developed, planned, and… hard MN-1.4Negative residual risks, defined as the sum of all unmitigated risks, to both downstream acquirers of AI… hard MN-2.1Resources required to manage AI risks are taken into account, along with viable non-AI alternative systems… hard MN-2.2Mechanisms are in place and applied to sustain the value of deployed AI systems hard MN-2.3Procedures are followed to respond to and recover from a previously unknown risk when it is identified hard MN-2.4Mechanisms are in place and applied, and responsibilities are assigned and understood, to supersede… hard MN-3.1AI risks and benefits from third-party resources are regularly monitored, and risk controls are applied and… moderate MN-3.2Pre-trained models which are used for development are monitored as part of AI system regular monitoring and… moderate MN-4.1Post-deployment AI system monitoring plans are implemented, including mechanisms for capturing and evaluating… hard MN-4.2Measurable activities for continual improvements are integrated into AI system updates and include regular… hard MN-4.3Incidents and errors are communicated to relevant AI actors including affected communities, and processes for… moderate

MAP - NIST AI RMF 1.0

MP-1.1Intended purpose, potentially beneficial uses, context-specific laws, norms and expectations, and prospective… hard MP-1.2Inter-disciplinary AI actors, competencies, skills and capacities for establishing context reflect… hard MP-1.3The organization's mission and relevant goals for the AI technology are understood and documented hard MP-1.4The business value or context of business use has been clearly defined or, in the case of assessing existing… hard MP-1.5Organizational risk tolerances are determined and documented hard MP-1.6System requirements are elicited from and understood by relevant AI actors, and design decisions take… hard MP-2.1The specific task, and methods used to implement the task, that the AI system will support is defined hard MP-2.2Information about the AI system's knowledge limits and how system output may be utilized and overseen by… hard MP-2.3Scientific integrity and TEVV considerations are identified and documented, including those related to… hard MP-3.1Potential benefits of intended AI system functionality and performance are examined and documented hard MP-3.2Potential costs, including non-monetary costs, which result from expected or realized AI errors or system… hard MP-3.3Targeted application scope is specified and documented based on the system's capability, established context… hard MP-3.4Processes for operator and practitioner proficiency with AI system performance and trustworthiness, and… hard MP-3.5Processes for human oversight are defined, assessed, and documented in accordance with organizational… hard MP-4.1Approaches for mapping AI technology and legal risks of its components, including the use of third-party data… hard MP-4.2Internal risk controls for components of the AI system including third-party AI technologies are identified… hard MP-5.1Likelihood and magnitude of each identified impact are identified and documented, based on expected use, past… hard MP-5.2Practices and personnel for supporting regular engagement with relevant AI actors and integrating feedback… hard

MEASURE - NIST AI RMF 1.0

MS-1.1Approaches and metrics for measurement of AI risks enumerated during the MAP function are selected for… moderate MS-1.2Appropriateness of AI metrics and effectiveness of existing controls is regularly assessed and updated… moderate MS-1.3Internal experts who did not serve as front-line developers for the system and independent assessors are… hard MS-2.1Test sets, metrics, and details about the tools used during test, evaluation, validation, and verification… hard MS-2.2Evaluations involving human subjects meet applicable requirements including human subject protection and are… hard MS-2.3AI system performance or assurance criteria are measured qualitatively or quantitatively and demonstrated for… hard MS-2.4The functionality and behavior of the AI system and its components, as identified in the MAP function, are… moderate MS-2.5The AI system to be deployed is demonstrated to be valid and reliable, and limitations of the… hard MS-2.6AI system is evaluated regularly for safety risks as identified in the MAP function, is demonstrated to be… moderate MS-2.7AI system security and resilience as identified in the MAP function are evaluated and documented hard MS-2.8Risks associated with transparency and accountability as identified in the MAP function are examined and… hard MS-2.9The AI model is explained, validated, and documented, and AI system output is interpreted within its context… hard MS-2.10Privacy risk of the AI system as identified in the MAP function is examined and documented hard MS-2.11Fairness and bias as identified in the MAP function is evaluated and results are documented hard MS-2.12Environmental impact and sustainability of AI model training and management activities as identified in the… hard MS-2.13Effectiveness of the employed TEVV metrics and processes in the MEASURE function are evaluated and documented hard MS-3.1Approaches, personnel, and documentation are in place to regularly identify and track existing… hard MS-3.2Risk tracking approaches are considered for settings where AI risks are difficult to assess using currently… hard MS-3.3Feedback processes for end users and impacted communities to report problems and appeal system outcomes are… hard MS-4.1Measurement approaches for identifying AI risks are connected to deployment contexts and informed through… hard MS-4.2Measurement results regarding AI system trustworthiness in deployment contexts and across the AI lifecycle… hard MS-4.3Measurable performance improvements or declines based on consultations with relevant AI actors including… hard