Enterprise Risk Management, MS AI and Technology Risk Leadership
The Master of Science in Enterprise Risk Management requires the completion of 12 total courses for the degree: 5 core courses, 6 specialization courses, and the capstone. All students complete a shared core that integrates enterprise, regulatory, quantitative, and technology risk foundations, drawing on widely used frameworks such as COSO ERM, ISO 31000, and NIST. The curriculum emphasizes data-driven risk analysis, ethical decision-making, and risk culture, while building the leadership, project management, and executive communication skills required to engage senior leaders and regulators. Students then pursue either a General Enterprise Leadership or AI & Technology Risk Leadership specialization.
Students pursuing the AI & Technology Risk Leadership Specialization take six advanced courses focused on technology and AI governance.
Curriculum
Core Courses (6 units)
| Course | Title |
|---|---|
| MS_ERM 401-DL | Foundations of Enterprise, Regulatory, and Technology Risk |
| MS_ERM 402-DL | Foundations of Quantitative Reasoning and Risk Analytics |
| MS_ERM 403-DL | Risk Culture, Ethics, and Human Behavior |
| MS_ERM 404-DL | Project and Engagement Management for Risk Leaders |
| MS_ERM 405-DL | Strategic Communication for Risk Professionals |
| MS_ERM 498-DL | Capstone Practicum in Enterprise and AI-Technology Risk |
AI & Technology Risk Leadership Specialization (6 units)
| Course | Title |
|---|---|
| MS_ERM 409-DL | Principles of Regulatory Compliance and Consumer Protection |
| MS_ERM 430-DL | Introduction to Generative AI, Responsible AI, and Technology Risk |
| MS_ERM 431-DL | AI Governance and Responsible Innovation |
| MS_ERM 432-DL | Cybersecurity and Cloud Risk Management |
| MS_ERM 433-DL | Blockchain, Crypto, and Digital Assets Risk |
| MS_ERM 434-DL | Risk Analytics and Intelligent Automation |
About the Experiential & Applied Learning
The capstone practicum in Enterprise and AI-Technology Risk is a culminating, team-based consulting engagement with real-world problems for which students deliver a final risk assessment or governance framework addressing AI, cloud, or resilience challenges. The project includes a written report, board-level presentation, and implementation roadmap evaluated by faculty and industry reviewers.
Prerequisites: Completion of all core courses in the student’s graduate program and specialization.