Organisations often adopt “AI” as an umbrella label that hides critical differences: rules vs. machine learning, predictive vs. generative behaviour, model-in-a-box vs. AI as part of a wider socio-technical system. Without a clear mental model, governance discussions drift, requirements get misapplied, and assurance work focuses on surface artefacts rather than how the system actually operates.

This training module establishes practical AI literacy: key terms, AI system types, and the technical building blocks that recur across most implementations (data pipelines, models, interfaces, and supporting IT controls such as access management, encryption, and logging). It intentionally does not cover AI risk and harm assessment, lifecycle scoping and inventory methods, operational control design, or detailed failure-mode analysis—those are handled in the dedicated follow-up modules.