AI Risk Management
NIST AI 100-1 Framework Overview
The NIST AI 100-1, Artificial Intelligence Risk Management Framework (AI RMF), provides structured guidance for managing AI risks. Transitioning from framework introduction to lifecycle understanding, Part 1 highlights the AI system lifecycle and its key dimensions. Fig. 2 illustrates stages from design and development to deployment and monitoring, emphasizing iterative evaluation and risk assessment. Fig. 3By understanding these risks, organizations can implement controls to prevent adverse impacts.
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AI Risk Management
AI RMF Core Functions
Transitioning from risk identification to core management, Part 2 of NIST AI 100-1 defines the AI RMF Core and its four functions: govern, map, measure, and manage. The govern function establishes policies, roles, and accountability structures for responsible AI use. Map identifies system components, stakeholders, and potential risks throughout the AI lifecycle. Measure evaluates AI performance, reliability, and compliance with regulatory and organizational requirements. Manage implements mitigation strategies, monitors outcomes, and adjusts processes as needed to reduce risk. Fig. 5 illustrates profiles that tailor the RMF Core functions to specific organizational contexts.
Trustworthiness and Practical Application
Transitioning to practical application, these principles guide AI deployment to minimize harm and maximize benefits. Integrating the AI RMF Core and trustworthiness principles supports risk-informed decision-making and ethical deployment. Organizations adhering to this framework improve system resilience, reduce adverse impacts, and enhance societal trust.
AI RMF Core Functions
Transitioning from risk identification to core management, Part 2 of NIST AI 100-1 defines the AI RMF Core and its four functions: govern, map, measure, and manage. The govern function establishes policies, roles, and accountability structures for responsible AI use. Map identifies system components, stakeholders, and potential risks throughout the AI lifecycle. Measure evaluates AI performance, reliability, and compliance with regulatory and organizational requirements. Manage implements mitigation strategies, monitors outcomes, and adjusts processes as needed to reduce risk. Fig. 5 illustrates profiles that tailor the RMF Core functions to specific organizational contexts.
Trustworthiness and Practical Application
Transitioning to practical application, these principles guide AI deployment to minimize harm and maximize benefits. Integrating the AI RMF Core and trustworthiness principles supports risk-informed decision-making and ethical deployment. Organizations adhering to this framework improve system resilience, reduce adverse impacts, and enhance societal trust.
