Checklist21 May 20267 min read

Generative AI Governance Checklist for Australian Businesses

A generative AI governance checklist helps teams move from informal AI use to clear controls.

It should cover approved tools, data rules, review standards, human oversight, vendor terms, staff training, monitoring, and escalation.

1. Accountability

Name the business owner for AI governance.

For higher-risk use cases, record:
- Who owns the use case
- Who approves it
- Who reviews output quality
- Who can pause or reject use
- Who handles incidents or exceptions

Accountability should be visible enough for leadership, not buried inside informal team habits.

2. AI Register

Record the AI tools and use cases already active in the business.

Include:
- Tool name
- Vendor
- Team or user group
- Purpose
- Data types used
- Integrations
- Output type
- Review date
- Business owner

This register does not need to be complex. It needs to exist and stay current.

3. Data Rules

Define what staff can and cannot enter into AI tools.

Rules should cover:
- Client information
- Personal information
- Employee records
- Financial data
- Contract terms
- Credentials and secrets
- Confidential internal documents
- Source code and system details

If a data type is unclear, staff should know where to ask before using it.

4. Output Review

AI outputs need human checking before they are used in important work.

Review should test:
- Accuracy
- Confidentiality
- Bias or unfair treatment
- Unsupported claims
- Copyright or source concerns
- Fit for purpose
- Customer, employee, or legal impact

The more important the use case, the stronger the review should be.

5. Vendor And Access Controls

Standalone AI tools and embedded AI features both need review.

Check:
- Terms of use
- Data retention
- Whether prompts or outputs train models
- Admin settings
- Identity and access controls
- Logging and audit options
- Data location where relevant
- Contract and procurement status

For tools like Copilot, permissions and data access are part of the governance problem.

When an AI system can call tools, trigger workflows, or act across connected business systems, static policy is not enough. Use the AI Agent Runtime Governance Checklist to test whether tool access, human approval points, logging, and rollback paths are clear before autonomy increases.

6. Monitoring And Improvement

The checklist should create a review rhythm.

Track:
- Incidents
- Staff questions
- New tool requests
- Vendor changes
- Permission changes
- High-risk output failures
- Policy exceptions

AI governance improves when the business learns from real use, not only from initial policy work.

Sources

For help turning this checklist into controls, see generative AI governance and AI policy development.

Common questions

What should be on a generative AI checklist?

Ownership, approved uses, data rules, human review, vendor checks, training, monitoring, and escalation.

Should every AI use case be approved?

Low-risk uses can follow standard rules; higher-risk uses should need explicit review.

How does this relate to AI.gov.au?

The checklist supports essential practices such as accountability, risk management, testing, monitoring, and human control.

How often should the checklist be updated?

Update it when tools, vendors, data access, business processes, or obligations change.