Use and purpose
What the AI is being used for, whether it is optional or required, and whether it only assists or materially influences a decision.
Australian workplace AI guidance
Workplace AI Governance Framework in Australia
A practical guide to help organisations create clear rules, accountability, risk controls, transparency, human oversight and monitoring for AI used at work.
An AI governance framework is not a document that sits on a shelf. It is the operating system for how a workplace chooses, approves, uses, monitors and retires AI. The right framework depends on the AI system, the work it supports, the people using it, the people affected by it and the consequences if it goes wrong.
Start with context
A good framework changes with the AI use case.
Low-risk AI used to draft an internal meeting summary does not need the same controls as AI used to screen applicants, monitor workers, recommend safety actions, handle personal information or support customer decisions.
Users and scale
Who uses the tool, how many people use it, how often it is used and whether contractors, managers or frontline teams rely on it.
People affected
Whether the AI affects workers, applicants, customers, clients, vulnerable people, safety, access to services or legal rights.
Framework building blocks
What a workplace AI governance framework should cover.
The details can be simple or formal, but the framework should make AI visible, assign accountability and create controls before AI becomes normal practice.
AI use register
Record each approved AI tool, owner, purpose, users, data type, vendor, risk level, controls and review date.
Risk classification
Classify AI use by potential impact, data sensitivity, autonomy, scale, affected people and consequences of error.
Roles and accountability
Set who approves AI, who owns the risk, who checks outputs, who handles incidents and who reports to leadership.
Acceptable-use rules
Define approved tools, prohibited uses, data-entry rules, public AI limits, escalation steps and review requirements.
Data protection
Control personal information, confidential business data, worker data, customer data, intellectual property and prompt history.
Human oversight
Decide when a person must review, approve, override, pause or reject AI outputs before they affect people or decisions.
Testing and monitoring
Test accuracy, bias, security, reliability and usability before deployment and monitor outputs after use begins.
Training and reporting
Train staff on safe AI use, privacy, hallucinations and escalation. Make it easy to report errors, incidents and concerns.
Build the framework
A practical path from visibility to ongoing control.
- Find where AI is already used.
Ask teams about approved tools, embedded AI features and unapproved public AI use.
- Define risk levels.
Use stronger controls for high-impact, high-volume, sensitive, automated or people-affecting AI use.
- Set approval pathways.
Make clear who can approve AI, what evidence is needed and when legal, privacy, cyber, safety or people review is required.
- Apply controls.
Use data rules, access limits, human review, testing, record keeping, vendor checks and incident reporting.
- Train people.
Give staff plain-English rules on safe prompting, privacy, poor outputs, bias, over-reliance and when not to use AI.
- Monitor and improve.
Review output quality, worker feedback, incidents, vendor changes and whether the AI still fits its original purpose.
Proportionate controls
The stronger the risk, the stronger the framework.
A framework should scale up when AI touches sensitive data, influences decisions, affects many people, operates with limited supervision or creates safety, privacy, discrimination, cyber security, legal or reputational risk.
Low risk
Internal drafting, brainstorming or summarising with no confidential, personal or decision-critical information.
Moderate risk
AI used by teams for analysis, customer support, workflow assistance or business processes where errors need review.
High risk
AI that affects employment, safety, monitoring, access to services, personal information, vulnerable people or important outcomes.
Emerging risk
Agentic AI, automated workflows and connected tools that can act across systems need clear permissions, logs and human control.
This page provides general education and guidance only. It is not legal, workplace relations, privacy, safety, cyber security, financial, governance or professional advice. Organisations should seek appropriate advice for their circumstances.
Australian guidance
Use recognised public resources as reference points.
Australian organisations can draw from public AI safety, ethics, privacy, cyber security, safety and management-system guidance when designing a workplace AI governance framework.
Useful reference links
- National AI Centre guidance for AI adoption
- Voluntary AI Safety Standard
- Voluntary AI Safety Standard 10 guardrails
- Australia's AI Ethics Principles
- AS ISO/IEC 42001:2023 AI management system information
- OAIC privacy guidance
- Australian cyber security guidance
- Safe Work Australia risk management guidance
Framework questions
Common questions about workplace AI governance frameworks.
No. A framework should be proportionate to the AI system, what it is used for, who uses it, how many people use it, the data involved, the people affected and the potential impact of the output or decision.
A practical framework usually includes an AI use register, risk classification, clear accountability, approved-use rules, data safeguards, human review points, testing, monitoring, incident reporting, staff training and regular review.
Stronger controls are needed when AI affects employment, safety, privacy, vulnerable people, legal obligations, financial outcomes, access to services, monitoring, surveillance or other important decisions.
No. This page provides general education and guidance only. Organisations should seek appropriate legal, privacy, cyber security, safety, governance or professional advice for their own circumstances.
Next step
Turn AI awareness into a repeatable governance process.
Start with visibility, then add controls that match the risk. Keep humans accountable and review the framework as tools, laws, vendors and workplace use change.