Episode 4 — Explain AI in Plain English: Models, Data, Training, and Inference Basics (Domain 1)
Foundational technical knowledge is the bedrock of Domain 1, as you cannot govern what you do not understand. This episode clarifies complex AI terminology, defining models as mathematical representations and explaining how data serves as the primary fuel for these systems. We distinguish between the training phase, where the model learns patterns from historical data, and the inference phase, where the model applies that learning to new, unseen inputs. Understanding these basics is essential for the AAIR exam because it allows risk professionals to pinpoint where specific vulnerabilities, such as data poisoning or biased training sets, can enter the system. We explore examples like large language models and predictive analytics to illustrate how these components interact in a business environment. Mastering these plain-English definitions ensures you can communicate risk effectively to non-technical stakeholders while maintaining the technical accuracy required for certification success. Produced by BareMetalCyber.com, where you’ll find more cyber audio courses, books, and information to strengthen your educational path. Also, if you want to stay up to date with the latest news, visit DailyCyber.News for a newsletter you can use, and a daily podcast you can commute with.
