Third-party AI

Grant Thornton's 2026 AI Impact Survey, Forrester and MIT research on failed AI pilots, and the EU AI Act's Article 26 deployer obligations all point to the same gap: boards are funding AI faster than they are assigning who owns it. Here's why business-led ownership, not another control layer, is what actually makes AI risk management work.

Deloitte's $290,000 government report scandal, AICD's warning on AI vendor concentration risk, and the UK's new Critical Third Parties regime all expose the same gap: accountability for AI-enabled outcomes can't be outsourced to the vendor that built the tool. Here's what risk and governance teams should check before relying on vendor AI assurances.

APRA, ASIC and the ACCC have all sharpened the rules on AI risk in 2026 — from APRA's step-change governance letter to ASIC's 'Year of Accountability' and the ACCC's doubled penalties for AI-washing. Here's why leaders should treat AI risk management as a business enabler, not a compliance checkbox.

APRA's April 2026 letter to industry and ASIC's Report 798 both warn that boards are leaning on AI vendor assurances instead of independently verifying them. Here is what Australian organisations should be checking before they trust the compliance pack.

Shadow AI is no longer hypothetical: a NSW government contractor uploaded flood victims' personal and health data to ChatGPT, while APRA and ASIC have both issued 2026 letters demanding stronger AI governance and cyber resilience. This post sets out what boards and risk leaders should be asking right now.

Third-party AI vendor risk is the widest compliance gap in Australian companies, your vendor's model update is now a regulatory event.

APRA's and ASICs AI governance letters have made one thing clear: named business ownership of AI use cases is now the regulatory minimum. Without it, your organisation is carrying unquantified executive risk.

AI initiatives often stall due to unclear ownership and generic vendor assurances. This article explains why business leaders must own AI risks from the start and demand practical, risk-based evidence from third parties to avoid costly delays.