If your organisation is like most others across the world, then artificial intelligence (AI) tools have now become deeply embedded within your daily operations. These powerful assistants are used to draft documents, summarise meeting notes, protect data, and even automate entire processes. But this creates a new challenge, which many organisations are unprepared for: When one technology has access to so much of your IT infrastructure, there’s more risk than ever before.
Community and health organisations are in even more danger, due to the unique circumstances they operate under. Sensitive information is handled every day, regulatory standards are stricter than ever, and even a small issue causes everyone to ask uncomfortable questions. So what can you do? Is there a way to implement AI without endangering your entire organisation?
What is AI Governance?
AI governance refers to the policies and processes put in place to keep AI in check. This is necessary to combat a number of challenges:
- Inaccurate Output: As valuable as AI is, it’s not perfect. In fact, one interesting study performed by Stanford HAI found that models used for legal work provided incorrect answers between 58% and 82% of the time. Without the proper checks and balances in place, these “hallucinations” could wreak havoc on your organisation.
- Security Risks: In order to do its job well, AI requires access to data. But if access isn’t properly secured, any information it touches could be under threat. Just like any other new technology, AI is being investigated by bad actors as a potential attack vector.
- Accountability: When a machine is left in charge of crucial tasks, it’s difficult to determine who should be held responsible if something goes wrong. Lack of accountability can create serious issues.
- Compliance Challenges: When plugging data into an AI model, it can be difficult to track where that information is sent and what happens to it. The danger here is that your organisation could become noncompliant with Australian privacy standards (such as the Privacy Act).
Governance provides the structure needed to ensure that AI can improve productivity without introducing unnecessary risk.
Why AI Governance is Especially Important for Community and Health Organisations
Australian community and health organisations face unique challenges that make careful implementation of AI even more crucial:
Sensitive Data
Data handled within this industry is among the most sensitive in existence. Health records, mental health histories, financial circumstances, family situations, and other highly personal details are discussed on a daily basis. This means that during a breach, the stakes are extremely high. You could be subject to reputational harm or even legal issues.
Operational Concerns
The services you provide are essential to the lives of everyday Australians. That means they need to be available at all times. Even brief periods of downtime can have a serious impact on your clients, staff, and reputation.
Accountability and Trust
Community and health organisations live and die on their reputation. When the decisions you make have a real impact on your client’s lives, they carry additional weight. The information you give them needs to be accurate and up-to-date. Mistakes aren’t an option.
Most community and health organisations are also funded by the government or other local authorities, compounding the risks involved. Not only is your reputation on the line if you experience a breach, but your funding could be as well.
What an AI Governance Framework Should Cover
When building your AI governance framework, here are the most important elements to consider:
Policy Development
Your starting point should always be a clear organisational policy on AI use. Without this, governance is effectively left up to each individual user. A well-constructed AI policy should cover:
- Which AI tools are approved for use and which are not
- Which types of data may and may not be inputted into AI systems
- How AI-generated outputs should be reviewed before use (particularly where they inform decisions affecting clients)
- What level of risk is acceptable while using AI-powered solutions
- Which compliance requirements need to be considered
- Which underlying governance structures will be in place to reduce risk
- Disclosure obligations where AI has been used in client-facing communications or decisions
- Reporting mechanisms for staff who encounter concerning AI behaviour or outputs
- What will happen in the event of a data breach
Data Handling and Privacy Compliance
Next, you need to ensure your existing tools are compliant with the new policies. To accomplish this, you’ll need a clear understanding of how AI models handle the data they receive. Questions to ask include:
- Is data inputted into the tool used to train the model? If so, what does that mean for sensitive information?
- Where is data processed and stored, and does that satisfy Australian data residency requirements?
- What are the vendor's data retention policies, and how do they align with the organisation's obligations?
- Does the use of this tool require updates to privacy notices or consent processes?
Staff Training
Your policies and controls become meaningless if employees don’t uphold them consistently. Everyone needs to be on the same page regarding AI use, and that means you need to teach your staff what you expect.
Training should cover:
- Your organisation's AI policy in plain language
- Guidance on which information should never be entered into an AI tool
- How to critically evaluate AI-generated outputs
- Where to go with questions or concerns
Vendor Assessment
Some AI tools are safer than others, and it’s your responsibility to ensure you’re not reliant on any that might be endangering your organisation or clients. Vendor assessments should be standard when evaluating new solutions.
Include:
- A review of the vendor's privacy policy and data processing agreement
- Confirmation of where data is processed and stored
- Understanding of whether the tool uses customer data for model training
- Assessment of the vendor's security certifications and practices
- Clarity on what happens to data if your organisation stops using the tool
Practical Steps for Implementing AI Governance
Conduct an Audit
Before implementing governance policies, you first need to understand AI adoption across your organisation. Review the tools currently in use, including AI capabilities that have been embedded within existing workflow platforms. You need to know where this technology is and what it has access to.
Establish Clear Responsibilities
If an AI decision causes harm, someone needs to be held accountable for that. Identify individuals who have authority (for instance, who is fact-checking and editing AI-generated documents) and assign clear ownership. Accountability motivates everyone to actively engage with your governance policies.
Understand Your Compliance Obligations
As you develop your new governance policies, it’s important to keep relevant laws and regulations in mind. Research your obligations, analyse them, and build them into the plan from day one. This reduces your risk of experiencing audits and fines.
Develop Policy Before Expanding Use
Resist the temptation to continue implementing new AI initiatives while governance structures are still under development. This will only make your job harder later. Focus on building policies that will keep your AI tools safe and productive. Then, once you begin considering new tools, you’ll be able to compare options against your policies to ensure you’re getting the right one.
Review and Update Regularly
AI is a rapidly developing technology, and regulatory requirements are barely even starting to catch up. In light of this, your governance framework should be a living document. Review and update it regularly. Aim for:
- At least once per year
- After any major changes within your organisation
- After legislative changes
Benefit From Modern Technology Without Introducing New Risks
AI is a powerful tool, but only when used safely and responsibly. Otherwise, it can quickly become dangerous. Human oversight, clear policies, and careful vetting of vendors will allow you to ensure AI systems remain safe, productive, and efficient.
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