Privacy Engineering
A builder's course. Translate the Act's duties into architecture - data discovery, minimisation, security safeguards, consent and rights automation, and governance for AI and GenAI systems that touch personal data.
Outcomes
Concrete capabilities you carry away - not just knowledge, but what to do with it.
Design a lawful, minimised data model
Automate consent capture and rights requests
Stand up breach detection and reporting
Govern AI/GenAI use of personal data
The curriculum
About 120 minutes of focused study, each module tied to the sections it draws from.
Translate Section 8's duties into architecture, not afterthoughts.
Security, accuracy, minimisation and retention limits are system requirements - encode them as constraints in the design, not as policies bolted on later.
Tie data fields and pipelines to declared purposes, and block use outside those purposes by construction rather than by convention.
Ship settings off-by-default and collect the minimum. Privacy by default is the cheapest, most reliable control you have.
A new analytics toggle ships off by default - opt-in only.
Every new feature collects data on by default, with opt-out buried in settings.
Takeaway - Turn Section 8 duties into constraints, scope data to purpose, and default everything to private.
Learn by doing
Turn duties into design. Map obligations to controls, judge architecture choices, and decide when data may feed a model.
Each is a real architecture decision. Would it pass a privacy review?
To gate adult content, you store every user's full date of birth - when all you need is an “is 18+” flag.
Build privacy into the product, not on top of it.
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