Episode 2 — The AI Security Landscape

This episode defines the AI security landscape by mapping the assets, attack surfaces, and emerging threats that distinguish AI from classical application security. It introduces critical components such as training data, model weights, prompts, and external tools, explaining why each must be protected as an asset. The relevance for certification exams lies in understanding how these components shift trust boundaries and create new risks compared to traditional software systems. The episode emphasizes that adversaries target AI differently, often exploiting natural language, data poisoning, or model extraction techniques. By describing the breadth of risks, the episode establishes the foundation for examining each in detail throughout the Audio course.

In its applied perspective, the episode explores how organizations must expand security programs to account for AI-specific challenges. Examples include leakage of personal information through outputs, manipulation of retrieval-augmented generation pipelines, and exploitation of agents connected to external systems. It discusses how exam candidates should recognize parallels and differences between AI security and established AppSec practices, noting where controls such as authentication, logging, and encryption remain essential but insufficient. Scenarios highlight how adversary motivations—ranging from fraud to disinformation—shape the threat landscape. The description underscores the importance of holistic defenses, aligning technical, organizational, and compliance strategies to manage this new class of risks. Produced by BareMetalCyber.com, where you’ll find more cyber audio courses, books, and information to strengthen your certification path.