AI Governance in Australia: What Organisations Need to Know

26 Aug AI Governance in Australia: What Organisations Need to Know

Artificial Intelligence is becoming increasingly popular in Australian companies. From automating mundane tasks, to analyzing data and aiding in decision-making AI creates new opportunities to increase productivity and business efficiency.

However, the use of AI can also bring new issues.

Who is accountable who is responsible for AI decisions? How can sensitive information be effectively protected? What steps should be taken when an AI system produces an inaccurate result? In what ways can organizations promote the responsible and proper use of AI technology?

These are the questions at the very heart of AI governance.

A well-defined governance strategy can assist organizations in managing AI risks while also creating the proper conditions to encourage responsible technological innovation.

What Is AI Governance?

AI governance is the term used to describe the rules, processes as well as the responsibilities and controls employed to oversee AI throughout its lifespan.

It goes beyond following the rules. Governance is essential to know the way AI is utilized, what risk it poses and whether the appropriate safeguards are put in place.

The governance framework for AI could include: AI governance framework could comprise:

• AI Standards and policies
• Responsibilities and roles
• Risk assessment
• Data Governance
• Security and privacy
• Human supervision
• Transparency
• Monitoring of AI systems
• Vendor management
• Record keeping and documentation

The Australian Government’s Voluntary AI Safety Standard offers 10 safety measures that cover aspects like accountability and data governance, risk management testing and human oversight, as well as transparency, stakeholder engagement and more.

Why AI Governance Matters for Australian Organisations

AI is not an individual technology project.

Its use could impact information management security, privacy procurement business processes, and organisational risk.

In this regard, AI governance should be integrated into existing risk and governance procedures.

The Australian Government’s policy recognizes the fact that AI is a function of the regulatory and legal framework instead of being considered an entirely separate field of compliance.

When a company is who are developing plans for an AI approach, that implies that governance must be considered at the start rather than being added following the time an AI solution is already implemented.

1. Establish Clear Accountability

One of the primary prerequisites for a successful AI management is to know who is accountable.

AI tools may be implemented by different departments, teams, or even individual employees. Without a clear oversight, organizations may not be aware of the systems being utilized and the information they process.

The areas of accountability should be covered like:

• Approval of AI use instances
• Assessing risks
• Monitoring AI systems
• Managing policies
• Reviewing suppliers
• Responding to incidents
• Ensure that the appropriate oversight of humans is in place

The dimensions and structure of the model for governance differ from one organization to the next, but the responsibility for each should be defined clearly.

2. Assess AI Risks Before Implementation

Different AI applications have diverse levels of danger.

A productivity tool within the company may be of limited use however, an AI system that is involved in making decisions that impact employees, customers or others could require a greater degree of oversight.

The risk assessment for AI can include: AI risk assessment could include:

• What is the system intended to do
• What data it utilizes
• Who is affected
• What are the decisions that influence it?
• Potential privacy risks
• Cybersecurity issues
• Accuracy and dependability
• Potential bias
• Human supervision needs
• Effects of inaccurate outputs

The process of risk assessment must be carried out after implementation, as AI technology and its associated environments are subject to change as they evolve.

3. Make Sure Data Is Ready for AI

AI depends heavily on data.

Poor-quality, outdated or fragmented information can affect the reliability of AI outputs and create additional privacy and security concerns.

This makes information management an important part of AI governance.

Before introducing an AI solution, organisations should consider whether information is:

• Accurate
• Accessible to authorised users
• Properly classified
• Secure
• Appropriately governed
• Managed according to retention requirements

An Information Management Assessment can help identify weaknesses in information management practices and provide a clearer understanding of organisational information maturity.

Strong information foundations can make future AI initiatives easier to manage and scale.

4. Protect Privacy and Sensitive Information

AI systems can process large quantities of personal as well as organisational information.

This poses privacy concerns particularly when personal data is used to help train, operate, or assist AI systems.

The organisations should be aware:

• What data is being processed?
• The reason it is processed
• The location where information is stored
• Who has access to it?
• If third-party providers have access to the data
• What information is stored or How information is disposed of or

Privacy concerns must be considered into AI plans, and not discussed until after the deployment.

Australian companies should also keep an eye on developments related to privacy and automated decision-making as the regulatory framework is constantly evolving.

5. Maintain Strong Cybersecurity Controls

AI introduces another dimension to cybersecurity.

Organisations may need to consider risks relating to data exposure, unauthorised access, third-party AI platforms, prompt manipulation, system vulnerabilities and misuse of AI-generated outputs.

Cybersecurity controls should therefore form part of the AI governance framework.

This includes understanding how AI systems connect to existing technology environments and what information they can access.

A Cybersecurity Assessment can help organisations identify cybersecurity risks and assess the maturity of existing security controls.

AI governance and cybersecurity should work together rather than operate as separate programs.

6. Keep Humans Involved in Important Decisions

AI can assist in decision-making however human oversight is vital in situations where AI outputs can have serious implications.

Human involvement could include reviewing outputs, making challenging decisions, or escalating unexpected results and stopping the AI system when it is necessary.

The Australian Government’s AI security guidelines specifically recognize how important it is for humans to have involvement and control throughout the AI life-cycle.

The amount of human supervision will be determined by the risks and the purpose that every AI system.

Applications with higher impact generally require more controls and clearer escalation procedures.

7. Create Transparency Around AI Use

Trust is a crucial element for an ethical AI adoption.

The people who interact with the company might need to be aware of the time AI is used in particular when it affects an interaction, decision, or service.

Transparency may be a means of explaining:

• When AI is used
• What function AI is playing?
• The content created by AI
• How important are AI-assisted decisions taken?
• What are the best ways to raise concerns

A clear communication will help the users to understand the way AI is being utilized and the security measures in place.

8. Manage AI Vendors and Third-Party Technology

A lot of companies will depend on external suppliers rather than develop AI internal systems.

AI Governance therefore must extend to procurement and supplier management.

Before implementing any AI platform, companies must consider:

• Data handling practices
• Security controls
• Storage of data
• Privacy agreements
• Intellectual property
• Model limitations
• Supplier responsibilities
• Incident management
• Data retention
• Auditing requirements

The AI capabilities of a supplier might be impressive, but it must conform to the organization’s policies on risk and governance.

9. Test and Monitor AI Systems

AI governance is not over once a system has been made live.

Monitoring on a regular basis can help to identify issues with security, accuracy and reliability, or even unexpected behavior.

Monitoring can consist of:

• AI output quality
• System performance
• Security-related Events
• User feedback
• Changes in data
• Unexpected results
• Conformity with internal policies

It is the Australian Govt’s voluntary AI safety safeguards suggest the testing of AI systems prior to deployment and then monitoring them after deployment.

This will ensure that governance is relevant even as AI platforms and operating environments evolve.

Building an Effective AI Governance Framework

There isn’t one single model of governance that can work for all companies.

A smaller company using AI to boost internal efficiency will require different specifications than the large company that is using AI for customer-facing or high-impact processes.

However, a practical framework can generally include:

Governance Area	Key Consideration
Accountability	Who owns AI governance and individual AI systems?
Strategy	How does AI support organisational objectives?
Risk	What risks are associated with each use case?
Data	Is information accurate, secure and appropriately governed?
Privacy	Are privacy requirements addressed?
Cybersecurity	Are AI systems and data adequately protected?
Human Oversight	Where is human intervention required?
Transparency	Is AI use appropriately communicated?
Suppliers	Are third-party AI risks understood?
Monitoring	How are AI systems reviewed after implementation?

An organisation’s broader technology strategy should also provide the foundation for AI planning. A Digital & ICT Strategy can help connect technology investment, governance, information management and future capability requirements with wider organisational objectives.

Where Should AI Governance Begin?

AI governance does not need to start with a complex policy document.

A practical starting point is understanding the current state.

An organisation can begin by identifying the AI tools and systems already in use, reviewing potential risks, assessing data and technology capabilities, and identifying gaps in existing governance.

An AI Readiness Assessment can provide a structured starting point by examining areas such as AI strategy, data, technology, people, governance and organisational capability.

From there, governance priorities can be developed according to the organisation’s risk profile and AI ambitions.

AI Governance Should Enable Responsible Innovation

A good governance system should not stop companies from exploring AI.

In addition, they should set clearly defined boundaries that allow for innovation to take place in a responsible manner.

A solid AI governance model gives the leadership greater transparency, empowers employees with better guidance and assists organizations in understanding where AI is able to create value.

It also assists in connecting AI with the existing methods for cybersecurity and information management, as well as technology strategy, and risk management for organisations.

Final Thoughts

AI adoption is accelerating however, a responsible adoption process is more than just choosing the best AI tool.

Australian companies must think about accountability, data privacy security, transparency, human oversight and continuous monitoring as part of the AI life cycle.

A well-designed AI governance framework gives the structure required to manage these areas and also support responsible innovation.

The objective isn’t just to create AI.

The objective is to create the conditions in which AI can be used in a safe manner while being responsible and aligned with organizational goals.

For companies who are just beginning their AI journey, knowing the current state of readiness and capabilities for governance can serve as a basis for making better technology choices and developing an ongoing AI strategy.

 

FAQs 

1. What is AI governance?

AI governance is the framework of policies, processes and controls that organisations use to manage the safe, responsible and effective use of artificial intelligence. It covers areas such as accountability, risk management, data, privacy, cybersecurity, transparency and human oversight.

2. Why is AI governance important for Australian organisations?

AI governance helps Australian organisations manage risks associated with AI while supporting responsible adoption. It can help address concerns around privacy, data security, inaccurate outputs, accountability, transparency and the appropriate use of AI.

3. What should an AI governance framework include?

An effective AI governance framework can include AI policies, clear accountability, risk assessments, data governance, privacy and cybersecurity controls, human oversight, transparency requirements, supplier management, testing and ongoing monitoring.

4. How can an organisation assess its AI governance readiness?

An organisation can begin by reviewing existing AI use, identifying AI-related risks, assessing data and technology capabilities, reviewing existing policies and identifying governance gaps. An AI readiness assessment can provide a structured way to evaluate these areas and establish priorities.

5. What are the key AI governance risks for organisations?

Common AI governance risks include data privacy, cybersecurity, inaccurate or biased outputs, lack of transparency, unclear accountability, inappropriate use of AI and risks associated with third-party AI providers. The level of risk depends on how and where AI is being used.