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Artificial Intelligence (AI)

Why AI Risk Management Must Address Vendor Change Controls to Prevent Operational Disruption

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.

AI Risk Management Enables Success

The Digital Transformation Agency’s Microsoft 365 Copilot trial shows how a bounded experiment can produce evidence about benefits and limitations. OECD adoption research and UK Government assurance guidance point to an operating model that helps organisations test, scale or stop AI responsibly.

Why AI Risk Management Must Focus on Third-Party Evidence Verification, Not Vendor Promises

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.

Turning AI Risk Assessments from Roadblocks into Business Accelerators

The FCA's 2026 AI Live Testing cohort and Supercharged Sandbox show how Barclays, Experian, Lloyds Banking Group, UBS and other firms are building evidence through controlled testing. NIST's tailorable AI RMF Playbook provides a practical basis for proportionate triage.

AI Risk Management Must Anchor on Clear Business Accountability from Day One

The Air Canada chatbot decision shows why organisations remain responsible for automated outcomes. The AICD and Human Technology Institute's 2026 Director's Guide adds practical board questions for assigning AI decision rights and oversight.

Managing AI Model Risk Beyond Traditional Frameworks: A New Approach for Business Leaders

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