Introduction to Cybersecurity and Artificial Intelligence Law

This course focuses on the regulation of cybersecurity and artificial intelligence (AI) in Canada. Given the borderless nature of data and AI, the course will also discuss international regulation. We will review legal disputes, exposure, and best practices related to cybersecurity and AI. Through practical exercises and industry speaker presentations, the course aims to provide students with an overview of the regulation and legal issues as they are today and an understanding as to how they may evolve.

In addition to being evaluated on class attendance and participation plus a final paper, students will work in groups on two assignments, each worth 10%. One group assignment will be a cyber simulation that would see the class divided into two groups with each dealing with a “live” cyber-attack. It would require limited group preparation and the simulation would be in front of the class (a form of oral presentation). No written submission/document would be required.

Another group assignment would have a group of 3 or 4 students prepare a 500 word memorandum outlining the risks and opportunities related to the implementation of AI solutions within an organization.

Topics covered will include but not be limited to:

Foundations

• Overview of international and Canadian cybersecurity and AI regulatory landscapes 
• Technical foundations of cybersecurity and AI systems 

Cybersecurity Governance and Risk Management

• Supply chain and third-party cybersecurity risk management 
• Cybersecurity considerations in business transactions

Incident Response, Regulation, and Liability

• Cyber and AI incident response, including cross-border considerations 
• Reporting and notification obligations under international and Canadian privacy laws 
• Engagement with law enforcement and preservation of legal privilege 
• Litigation exposure arising from cyber and AI incidents 

AI Governance and Deployment

• Governance of AI systems, including AI supply chains, foundation models 
• Data governance and accountability in AI systems (including privacy, copyright, training data)
  • Shadow AI, Acceptable use policies, Organizational use of AI
• Security, legal, and operational risks in AI deployments 
  • Model vulnerabilities, emerging attack vectors
  • Emerging issues in autonomous or “agentic” AI systems             
• Procurement and contractual considerations of AI systems

AI Disputes and Emerging Legal Issues

AI-related litigation and regulatory enforcement trends