NIST AI RMF: AI Risk Management & AI Governance

Assess AI risks across data quality, biases, trust, privacy, and data fit. Manage training changes, model uncertainty, and deployment costs while addressing testing, environment, and governance implications.

The AI risk management framework aligns responsible AI practices with human centricity, social responsibility, and sustainability, guiding design, development, and deployment toward equitable, transparent, and accountable outcomes.

Explore the two-part NIST AI RMF framework, its flexible, non-mandatory design, and how governance, map, measure, and manage address AI risks and guide trustworthy AI.

Frame and measure AI risks using likelihood and impact within ISO 31,000, considering harm to individuals, groups, communities, organizations, the environment, and society.

Frame AI risks and define the intended audience for governance. Assess AI trustworthiness through accuracy, reliability, safety, security, accountability, privacy, and fairness.

Explore the intended audience of the NIST AI RMF and how diverse actors across the life cycle collaborate to manage AI risk for trustworthy AI.

Explore how AI risk management and governance builds trustworthiness by balancing accountability, transparency, safety, fairness, and privacy through context-driven trade-offs, stakeholder collaboration, and ongoing evaluation.

Explore the four core functions—governance, map, measure, and manage—to address AI risks across the lifecycle, with governance spanning all stages and the other three functions tailored to specific contexts.

Explore the measure function in the NIST AI RMF to assess AI risk using quantitative, qualitative, and mixed metrics. See how measurement informs management and governance.

Workday demonstrates applying the AI RMF to its enterprise cloud software, governance, privacy compliance, bias mitigation, and responsible AI practices, including an AI advisory board and third-party tool evaluations.

Examine how the City of San Jose strengthens AI governance using the NIST AI RMF, building a citywide policy, staff education, user feedback, and rigorous testing.

Explore the NIST AI risk management framework blueprint for trustworthy AI. Access the knowledge base and playbook, explore use cases like Workday and San Jose, and connect with workshops.

Explore the NIST AI RMF playbook to implement and apply the framework with best practices and incremental updates, using governance, audit logs, and FAQs to guide transparency and documentation.