Foundations of AI-Driven Deception

This unit introduces students to the history, mechanics, and societal implications of deception as it transitions into the digital and AI era. Students will explore synthetic media, the AI inflection point, and basic cognitive security principles to prepare for technical detection and defense work in subsequent units.

Learning Objectives

• Analyze the historical evolution of deception by identifying at least three classical scams or deceptive practices and mapping their modern digital equivalents (e.g., social engineering, phishing, synthetic media).
• Evaluate the AI inflection point and current capabilities by summarizing three key AI milestones, explaining debates about machine understanding, and assessing how these developments change the threat landscape for deception.
• Demonstrate detection skills for synthetic media and AI-driven deception by identifying common artifacts, patterns, or indicators in at least two provided audio, visual, or text samples and justifying why they suggest inauthenticity.
• Apply cognitive security principles to design and justify at least two practical mitigations for individuals or organizations (e.g., verification workflows, communication protocols, awareness strategies) to reduce susceptibility to AI-enabled deception.