Overview

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This four-course Specialization from the University of Illinois College of Law is designed for business and technology professionals who must manage AI under changing law. Learners study why AI needs oversight, how governance differs from compliance, how to turn the EU AI Act, the NIST AI Risk Management Framework, and U.S. state laws into a working compliance program, how to manage vendor and third-party risk, and how to prepare for audits. The courses draw on real third-party liability cases and on rules such as NYC Local Law 144 and California legislation.

Syllabus

Course 1: Why AI Needs Legal Governance: Risk, Liability, & Compliance
Course 2: Building AI Legal Compliance Programs: EU AI Act & NIST
Course 3: AI Legal Compliance: Rules, Standards, and Third-Party Risk
Course 4: AI Legal Governance: Audit, Accountability, & Tradeoffs

Courses

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This course is for learners who need to understand, build, or oversee AI governance and compliance programs. It begins by turning governance design into practice: building accountability structures, setting acceptance criteria, error tolerance, and risk tolerance, and addressing shadow AI with the NIST AI RMF. It then covers the audit function, including audit types, model drift, EU conformity assessments, U.S. state audit expectations, and the audit trail. The course closes with the philosophical debate between the precautionary principle and the innovation principle, tracing it through history, U.S. federal and state policy, the EU AI Act, agentic AI, and real-world cases. Throughout, it emphasizes that effective governance should produce adherence rather than avoidance.
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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.
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This course is for learners and professionals who want to understand how artificial intelligence is governed and what compliance requires of organizations. You will begin by examining the current state of AI governance, including the U.S. executive-order approach, the gap between AI capability and public trust, and the sources of authority and standards used to build a governance framework. You will see why social license is central to sustainable AI through case studies on data centers and energy, the creative arts, and the Tay chatbot. You will then study two comprehensive approaches to governance: the EU AI Act, including its risk tiers, rules for general-purpose AI, enforcement, and global influence, and the NIST AI Risk Management Framework with its govern, map, measure, and manage functions.