AI Governance & Security 7 min read

AI Governance Checklist for Australian Organisations

A step-by-step checklist for responsible AI adoption. Covers data management, risk assessment, transparency, and ongoing monitoring.

Kasun Wijayamanna
Kasun Wijayamanna Founder & Lead Developer Postgraduate Researcher (AI & RAG), Curtin University - Western Australia

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.
Kasun Wijayamanna
Kasun Wijayamanna Founder & Lead Developer

Postgraduate Researcher (AI & RAG), Curtin University - Western Australia

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Meet the person

Written by the person who does the work

This article comes from real projects. If it raises a question about your own system, you can ask the founder directly.

HELLO PEOPLE designs, builds and looks after AI, software, app and data solutions for Australian businesses, with senior expertise on every project and a scope agreed before work starts. For AI, that means working on your own data, with a person able to check every answer.

Since 2007, HELLO PEOPLE has delivered more than 100 projects from Perth for small and medium businesses across Australia: custom software and apps, system integrations, data migrations, reporting and dashboards, and AI that works inside the systems a business already runs.

I lead every engagement myself. I trained in accounting before moving into IT, hold accounting and IT professional qualifications and an MBA, and bring more than 20 years of experience across sales, service delivery, inventory and compliance. I am also a PhD candidate in AI at Curtin University, researching retrieval-augmented generation (RAG), so the technology is always judged by what it does for the business.

  • An old-fashioned service

    Small and boutique. The person who scopes your AI project is the person who builds it, and the same person checks its answers before your team relies on them.

  • Quick responses

    No ticket queue and no account manager in between. You hear back within one business day, usually sooner.

  • A long-term partner

    The first AI project is the start, not the end. When you need the next system, integration or report, you call the same person, who already knows your business.

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