Third-party AI

NIST’s August 2026 TEVV-Athlon draft makes real-world AI evaluation a current governance issue. Businesses should connect every test to pre-agreed acceptance thresholds, a named decision owner and clear retest triggers.

ASD’s Australian Cyber Security Centre and the UK National Cyber Security Centre show why AI supplier assurance must cover the full lifecycle and extended supply chain. Moffatt v Air Canada demonstrates that business accountability remains with the organisation using the automated service.

The UK Information Commissioner’s Office expects AI transparency and supply-chain due diligence to evolve as processing purposes become clearer. NIST’s AI Risk Management Framework shows how privacy risk should be documented, measured and monitored throughout the AI lifecycle.

Microsoft Azure AI Foundry and Amazon Bedrock show how model retirement can shorten notice periods, stop requests and require code changes. The EU's DORA framework shows why notification, objection and exit rights must connect to a tested operational response.

The Australian Cyber Security Centre's procurement and AI supply-chain guidance shows why vendor assurance must be refreshed when services change. OAIC guidance adds a clear requirement for organisations to conduct privacy due diligence on commercially available AI products.

This piece looks at the questions Australian business and risk leaders should be asking before their own AI model drifts

APRA's April 2026 letter named third-party AI risk as the biggest gap in bank and insurer oversight — and weeks later, a compromised account at AI tooling vendor Context.ai gave attackers a path into Vercel's infrastructure. This piece uses both to show why vendor assurance paperwork isn't AI risk management, and what to ask instead.

When Air Canada's chatbot invented a bereavement discount, a Canadian tribunal made the airline pay $812.02 for it. New data from the Cyber Risk Institute's Treasury-backed AI framework and Ncontracts' 2026 Third-Party Risk Management Survey show why every organisation using vendor AI needs the same accountability before the mistake is theirs.