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University of Illinois at Urbana-Champaign

AI Legal Compliance: Rules, Standards, and Third-Party Risk

University of Illinois at Urbana-Champaign via Coursera

Overview

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This course helps professionals turn AI governance principles into practical programs and compliance strategies. You will learn how the NIST AI Risk Management Framework and its playbook translate legal requirements into concrete tasks, including AI inventories and use case profiles. You will also explore how California, Colorado, and Illinois regulate AI, and how holistic and targeted regulatory models affect compliance planning. Finally, you will learn how to manage third-party AI risk, drawing on real-world case studies and contract provisions such as indemnity.

Syllabus

  • Module 1: Building AI Compliance Programs Across Changing Regulations
    • You will learn how the NIST AI Risk Management Framework helps organizations turn legal requirements into governance and compliance processes through use case profiles, the generative AI profile, and audit crosswalks. You will also learn how U.S. states such as California, Colorado, Texas, Utah, and Illinois regulate AI, including the differences between holistic and targeted approaches and the open question of federal preemption. Finally, you will learn how organizations can build a baseline governance architecture that supports both broad and sector-specific AI obligations.
  • Module 2: Operationalizing AI Governance and Managing Third-Party Risk
    • You will learn how to turn high-level AI governance principles into a working program using the NIST AI Risk Management Framework and its playbook, including how to build an AI system inventory. You will also learn how third-party AI tools, models, and data create risk, as illustrated by cases involving Apple, McDonald's, Workday, and Rite Aid, and how contract provisions such as indemnity help allocate risk without replacing your own governance and oversight.

Taught by

Brian Trackman

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