Assurance becomes the AI advantage

GRC: AI assurance needs evidence, not slogans

APRA's April 2026 industry letter is a useful reset for AI governance teams. It says AI is being adopted quickly across regulated industries, but assurance practices are not keeping pace with the scale, speed and complexity of use cases. The practical lesson is that a board does not need another generic AI policy first. It needs a short evidence pack for each material use case: purpose, owner, risk tier, human review point, monitoring trigger, supplier dependency and recent control test. ASIC's May 2026 cyber letter adds urgency because AI can accelerate cyber threats and raise operational resilience expectations. The June question for GRC leaders is simple: what evidence would prove that AI is controlled in practice?

Source: apra.gov.au

HR: Digital work systems are now work design

NSW's new digital work systems reforms should be read by HR, WHS, operations and technology leaders together. The Act defines digital work systems to include algorithms, artificial intelligence, automation and online platforms, then links them to duties about safe work allocation. That matters beyond gig work. Many organisations already use tools that allocate tasks, prioritise tickets, send prompts, monitor activity or escalate work. Fair Work's right to disconnect guidance also reminds employers that contact outside working hours can be refused unless the refusal is unreasonable. The HR task for June is to audit workflow tools, not just employment policies, because the pressure may come from system settings rather than a manager's message.

Source: legislation.nsw.gov.au

WC: Human review remains the safeguard

Workers compensation teams can use AI to organise information, compare records and improve drafting, but the decision still belongs with an accountable human decision-maker. That distinction is especially important under the SRC Act, where concepts such as injury, significant contribution and reasonable administrative action require careful evidence assessment. The safe pattern is to use de-identified inputs, keep personal information out of open systems, record what the tool was asked to do and check every output against the source material. A practical June control is a simple AI use log for claims work: task, source documents, reviewer, decision impact and privacy check. AI can support consistency, but it must not replace judgement.

Source: legislation.gov.au

Build a material AI use case register that goes beyond tool names. For each use case, record purpose, business owner, data used, supplier dependency, user group, decision impact, risk tier, human review point, review cadence and evidence location. Keep low-risk experimentation visible, but prioritise depth for systems that touch customers, employees, regulated decisions or critical operations.

Create an AI assurance pack for the risk committee. Keep it short and repeatable: current material use cases, control test results, privacy and cyber issues, supplier assurance, incidents or complaints, open remediation and upcoming approvals. The pack should show how risk is being managed, not only that a framework exists. If the evidence is not current, say so and assign an owner.

Add contestability to the control design. The Australian AI adoption guidance and voluntary safety guardrails both emphasise feedback, monitoring and human control. Translate that into practical channels: who can question an AI-assisted output, how concerns are reviewed, when the tool is paused and how lessons are fed back into prompts, training, supplier settings and procedures.