AI in Cybersecurity and Privacy

This unit examines how generative AI and machine learning reshape cyber threats, defenses, and privacy across sectors such as business and health care. Students explore technical, ethical, and regulatory dimensions of AI-enabled attacks (e.g., deepfakes, polymorphic malware, password cracking) and defense strategies at software and hardware levels.

Learning Objectives

• Analyze emerging AI-driven threat vectors (e.g., polymorphic malware, deepfakes, accelerated password cracking) and summarize their potential impact on organizational and sector-specific assets
• Apply generative AI and machine-learning concepts to design basic defensive approaches and incident-mitigation plans, including use of AI for detection, response, and resiliency
• Evaluate ethical, legal, and privacy implications of AI deployment in business and health care, including governmental roles, regulatory requirements, and risks posed by employee use of AI
• Demonstrate risk-reduction strategies that address hardware-level security, computer-vision ethics, and stakeholder education to mitigate AI-enabled cyber threats