AI governance in Australia is the set of policies, oversight structures, and accountability mechanisms that guide how AI systems are designed, deployed, and monitored. It supports responsible AI use by defining decision rights, risk thresholds, reporting, and review processes. For organisations deploying AI across business functions, AI governance helps align AI initiatives with privacy obligations, operational risk expectations, and stakeholder trust requirements.

AI governance is increasingly expected, even when not labelled as a single “mandatory” requirement. Obligations and expectations can arise through privacy law, sector regulations, operational risk requirements, procurement standards, and board governance expectations. In practice, AI governance helps organisations demonstrate accountability, transparency, and risk control over AI systems, especially where automated decisions affect individuals or high-value business processes.

An AI risk assessment reviews an AI initiative end-to-end to identify and manage risk before deployment or expansion. It typically evaluates the purpose and decision impact of the system, data sourcing and quality, model behaviour and explainability, bias and fairness exposure, security and access controls, third-party dependencies, and ongoing monitoring arrangements. The output is a defined risk profile and clear mitigation actions aligned to organisational risk appetite.

AI bias and data ethics assessments examine whether data and model behaviour could create unfair or harmful outcomes for individuals or groups. The assessment considers how data was collected and labelled, how sensitive attributes are handled, where bias can enter the model lifecycle, and how decisions are tested and monitored over time. This helps organisations reduce ethical risk, strengthen transparency, and support defensible decision-making for AI-enabled systems.

An AI governance framework is a practical structure that enables consistent oversight of AI systems across the organisation. It should define accountability and roles, approval and escalation pathways, risk assessment triggers, documentation standards, monitoring and review cadence, and reporting to executives and boards. A well-designed framework connects to existing privacy, cyber, and operational risk governance so AI oversight is integrated rather than siloed.

AI governance should be implemented as soon as AI moves beyond experimentation into production use, or when AI influences customer outcomes, employee decisions, financial processes, or operational controls. It is particularly important when using third-party AI tools, deploying generative AI in business workflows, or introducing automated decision-making. Early governance reduces rework, strengthens accountability, and improves leadership confidence in responsible adoption.

AI governance and privacy obligations are closely linked because AI systems often rely on personal information and can influence decisions about individuals. AI governance helps ensure lawful data handling, transparency, accountability, and appropriate controls over data access, retention, and use. Where AI initiatives materially change data handling practices or introduce new technologies, a privacy impact assessment is commonly recommended to evaluate privacy risk and required safeguards.

Boards and executives oversee AI risk by ensuring AI initiatives have clear accountability, defined risk thresholds, and reporting that is meaningful at leadership level. This includes visibility over where AI is used, the impact of automated decisions, material privacy and ethical risks, third-party dependencies, and how performance and bias are monitored over time. AI governance frameworks provide the structure needed for consistent oversight and escalation.