This white paper sets out a practical, scalable framework built on a simple premise: governance depth should match risk.
It's grounded in the Australian Government's Guidance for AI Adoption and New Zealand's principles-based approach, and reflects the transparency, human-oversight, and documentation themes of the EU AI Act. It gives organisations a credible operating foundation across all three markets.
What You'll Find in This White Paper
- A risk-based, three-tier model for scaling governance controls to match a system’s autonomy and impact
- A side-by-side comparison of the AU/NZ principles-led approach and the EU AI Act’s legally prescribed obligations — and where this framework fits each
- The six governance principles regulators and boards expect to see evidenced, from accountability to human control
- A clear breakdown of developer vs. deployer responsibilities, so accountability never falls into a gap
- A five-step practical starting point for building an AI Use Register from scratch
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What You'll Learn
Why applying the same governance checklist to every AI initiative slows down low-risk work without actually managing high-risk exposure
How to triage an AI system's risk tier using purpose, data sensitivity, autonomy, and stakeholder impact
What supply-chain transparency means in practice when your AI depends on third-party models and APIs
How to build human-centred design and redress into agentic systems
Why this framework is a foundation to build on, not a substitute for EU AI Act legal obligations like conformity assessment and CE marking
Key Take Away
Governance should be proportionate to risk — not a fixed checklist applied uniformly to every AI initiative, and not an afterthought bolted on after deployment.
