AI Governance Checklist for Australian Organisations
A step-by-step checklist for responsible AI adoption. Covers data management, risk assessment, transparency, and ongoing monitoring.
Why AI governance matters
AI governance sounds bureaucratic, but the core idea is simple: make sure your AI systems are safe, fair, and accountable. Without governance, you risk privacy breaches, biased outcomes, regulatory trouble, and loss of trust.
You don't need a 200-page policy document. You need a practical framework that people actually follow.
The governance checklist
We break AI governance into four areas. Work through each one for every AI system you deploy or adopt.
1. Data management
Checklist
- Document what data the AI system accesses, processes, and stores
- Classify data sensitivity (public, internal, confidential, restricted)
- Confirm data residency. Is all data stored in Australia?
- Verify consent. Do you have the right to use this data for AI?
- Minimise data collection. Only include what the system actually needs.
- Implement retention policies. How long is data kept and when is it deleted?
- Conduct a Privacy Impact Assessment (PIA) if personal data is involved
2. Risk assessment
Checklist
- Identify what decisions the AI influences or makes
- Assess the impact of errors. What happens if the AI is wrong?
- Check for bias risks. Could the system produce unfair outcomes?
- Define failure modes. How does the system behave when it can't answer?
- Implement human oversight for high-stakes decisions
- Document known limitations and communicate them to users
- Create an incident response plan for AI-related issues
3. Transparency & accountability
Checklist
- Assign a responsible owner for each AI system
- Document the system's purpose, capabilities, and limitations
- Disclose AI use to affected people (customers, employees)
- Update your privacy policy to cover AI data processing
- Provide a way for people to query or challenge AI decisions
- Log queries, responses, and decisions for auditability
- Record the rationale for adopting this specific AI approach
4. Monitoring & review
Checklist
- Monitor accuracy, hallucination rate, and user satisfaction
- Review output quality regularly (monthly for new systems)
- Track costs and compute usage
- Re-evaluate data access permissions quarterly
- Update the system when regulations change
- Schedule annual governance reviews for all AI systems
- Keep records of all reviews and changes made
Start small: If you're deploying your first AI system, work through this checklist once. It won't take long, and it'll save you from the most common governance gaps.
Key takeaways
- AI governance doesn't have to be complex. Start with a practical checklist and iterate.
- Cover four areas: data management, risk assessment, transparency, and monitoring.
- Document your AI systems, their purposes, their data flows, and who's accountable.
- Review regularly. AI governance is ongoing, not a one-time exercise.