— why AI compliance is not a legal task, but a system design problem
— the 5 architectural capabilities required by the EU AI Act
— why “we’ll document later” and “it’s internal, so low risk” are the most dangerous assumptions
— how AI systems fail without logging, ownership, and escalation mechanisms
— real patterns: logging by design, human oversight, data boundaries, post-deployment monitoring
— how to move from AI experiments to an actual operating model
— what accountable AI looks like in practice and why it’s the only scalable approach
