Every crisis begins with a moment of disbelief. The thing that wasn’t supposed to happen suddenly has, and the assumptions that felt so comfortable a day earlier now feel paper-thin. That’s when risk management either shows up or falls apart.
In this article, Graeme Keith explores the deeper purpose of risk modeling—not as a mathematical exercise in prediction, but as a disciplined way of thinking. Drawing parallels from military planning to decision science, Keith examines why the act of modeling itself often yields greater value than the models it produces. Through reflections on clarity, logic, and the pursuit of usefulness over perfection, he argues that modeling is as much about understanding uncertainty as it is about managing it.
In his latest article, Ayoub Fandi breaks down how organisations can overcome fragmented risk and compliance systems by building a unified central data layer. He explains how this approach enables consistency, clarity, and smarter decision-making across modern GRC ecosystems that are too often siloed by tools and disconnected data.
If 2024 reminded us of anything, it’s that the threat landscape never stands still. In every breach headline, there’s a familiar pattern: an organization falls not because of its own failure, but because a trusted partner left a back door open.
AI adoption continues to accelerate across industries, promising efficiency gains, enhanced decision-making, and new revenue streams. However, organizations are increasingly exposed to operational risks that, if unmanaged, can result in financial losses, regulatory penalties, reputational damage, and ethical violations. These risks are not confined to deployment—they permeate every stage of the AI lifecycle, from data collection to continuous monitoring. Effective AI governance requires a holistic understanding of these risks and the implementation of proactive risk management strategies.
In this candid and thought-provoking piece, Norman Marks challenges conventional definitions of risk and risk management, arguing that most frameworks fail to resonate with how real-world decisions are made. Drawing from his decades of executive experience and referencing the ideas of Grant Purdy and Roger Estall, Marks reframes “risk” as simply “what might happen”, a practical, plain-English approach that bridges the gap between theory and management reality.
It was supposed to be a step toward global unity. The G7’s Hiroshima AI Process was meant to signal the dawn of an international consensus on how to govern artificial intelligence. Instead, it’s become a reminder that the world’s biggest powers are not building one system of AI governance, but several. Each reflects a different philosophy of risk, control, and trust. And for compliance and risk leaders, that’s where the real work begins.