AI-Driven Threats and Defenses

This unit examines how generative AI and machine learning reshape cyber threats and defenses, covering AI-enabled malware, deepfakes, automated credential attacks, hardware-level vulnerabilities, and domain-specific risks in healthcare and business. Building on foundational cybersecurity concepts from Unit 1, students assess ethical, legal, and privacy implications and prepare practical mitigation strategies needed for subsequent hands-on defensive work in Units 3–4.

Learning Objectives

• Analyze how generative AI and ML alter attack surfaces and tactics (e.g., polymorphic malware, automated password cracking, deepfakes) by identifying indicators of compromise and evolving threat patterns
• Evaluate AI-enabled defensive techniques and hardware-level security measures to design layered mitigation strategies that improve detection, adversarial resilience, and incident response
• Apply ethical, legal, and privacy frameworks to assess AI deployment risks in critical sectors (healthcare, government, business) and produce compliance-aware risk-reduction recommendations
• Demonstrate the ability to develop an organizational policy and employee training outline that mitigates insider risk and unsafe AI use while enabling secure, responsible adoption of AI tools