AI Risk and Defensive Automation

This capstone unit integrates advanced concepts in artificial intelligence, social engineering, and browser automation to identify, reproduce, and mitigate real-world deception and fraud scenarios. Students consolidate prior learning in ethics, system design, and security to build controlled proofs-of-concept, evaluate risk management strategies, and propose tactical, policy, and partnership responses.

Learning Objectives

• Analyze the socio-technical risks posed by advanced AI (echo chambers, artificial superintelligence, singularity, and brain–computer integration) and identify potential vectors for deception and misuse
• Apply browser emulation and automation techniques in a controlled, ethical environment to construct reproducible proofs-of-concept that demonstrate credential harvesting and wire-fraud exploitation paths
• Demonstrate defensive techniques and mitigation strategies (technical controls, detection heuristics, and incident containment) to stop or limit runaway automated attacks and fraud campaigns
• Evaluate AI risk management frameworks, legal/ethical constraints, and international partnership approaches to recommend tactical solutions and governance measures for preventing large-scale deception