NIST AI RMF 1.0,
subcategory by subcategory.

Legal and regulatory requirements involving AI are understood, managed, and documented.

The characteristics of trustworthy AI are integrated into organizational policies, processes, procedures, and practices.

Processes, procedures, and practices are in place to determine the appropriate level of risk management activities based on the organization's risk tolerance.

The risk management process and its outcomes are established through transparent policies, procedures, and other controls.

Ongoing monitoring and periodic review of the risk management process and its outcomes are planned and organizational roles and responsibilities are clearly defined.

Mechanisms are in place to inventory AI systems and are resourced per organizational risk priorities.

Processes and procedures are in place for decommissioning and phasing out AI systems safely and in a manner that does not increase risks or harms.

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.

The organization's personnel and partners receive AI risk management training.

Executive leadership of the organization takes responsibility for decisions about risks associated with AI system development and deployment.

Policies and procedures define and differentiate the roles and responsibilities for human-AI configurations and oversight of AI systems.

Organizational policies and practices are in place to foster a critical thinking and safety-first mindset in the design, development, deployment, and use of AI systems.

Organizational teams document risks and potential impacts of AI technology.

Organizational practices are in place to enable AI testing, identification of incidents, and information sharing.

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.

Mechanisms are established to enable AI actors to regularly incorporate adjudicated feedback into system design and implementation.

Policies and procedures are in place that address risks associated with third-party entities, including risks of infringement of a third party's intellectual property or other rights.

Contingency processes are in place for handling failures or incidents in third-party data or AI systems.

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.

Organizational risk tolerances are determined and documented.

System requirements (including those related to relevant AI actors) are elicited from and understood by relevant AI actors. Design decisions take socio-technical implications into account.

The specific tasks and methods used to implement the tasks that the AI system will support are defined.

Information about the AI system's knowledge limits and how system output may be utilized is documented.

Potential costs, including non-monetary costs, which result from expected or potential AI errors or system functionality and trustworthiness — as connected to organizational risk tolerance — are examined and documented.

Processes for human oversight are defined, assessed, and documented in accordance with organizational policies from the GOVERN function.

Approaches for mapping AI technology and legal risks of the component AI systems — 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.

Internal risk controls for components of the AI system are identified and documented.

Likelihood and magnitude of each identified impact (both potentially beneficial and harmful) based on expected use, past uses of similar systems, public incident reports, feedback, or other data are identified and documented.

Approaches and metrics for measurement of AI risks enumerated during the MAP function are selected for implementation starting with the most significant AI risks.

Appropriateness of AI metrics and effectiveness of existing measures are regularly assessed and updated, including reports of errors and impacts on affected communities.

AI system performance or assurance criteria are measured qualitatively or quantitatively and demonstrated for conditions similar to deployment conditions.

The functionality and behavior of the AI system and its components — as identified in the MAP function — are monitored when the system is deployed.

The AI system is evaluated regularly for safety risks — as identified in the MAP function. The AI system is demonstrated to be safe, its residual negative risk does not exceed the risk tolerance, and it can fail safely.

AI system security and resilience — as identified in the MAP function — are evaluated and documented.

Risks associated with transparency and accountability — as identified in the MAP function — are examined and documented.

Privacy risk of the AI system — as identified in the MAP function — is examined and documented.

Approaches, personnel, and the frequency of periodic risk tracking are in place to regularly identify and track existing, unanticipated, and emergent AI risks based on factors such as intended and actual performance.

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.

Feedback processes for end users and impacted communities to report problems and appeal outcomes are established.

Measurement approaches for identifying AI risks are connected to deployment context(s) and informed through consultation with domain experts and relevant AI actors.