Perspectives
Practical perspectives on AI governance, regulation, and trusted AI operations from the Verydion advisory team.
We keep discussing how to comply with the AI Act. Perhaps the better question is: what controls are common across all our regulatory obligations, and how do we implement them once? The case for governance as architecture.
As organizations move from AI experimentation to production deployment, risk management can no longer be an afterthought. This article introduces a risk-based framework for scaling AI safely — proportionate controls, clear accountability, and operational trust by design.
Reflections from CybersecEurope 2026 — the signals, the conversations, and what they mean for organizations navigating the intersection of cybersecurity, AI, and European regulation.
The EU AI Act's high-risk classification triggers a cascade of obligations — from risk management systems and data governance requirements to technical documentation and post-market monitoring. This note explains what organisations deploying high-risk AI systems need to have in place.
Regulatory expectations for model validation have expanded well beyond accuracy and AUC scores. Conceptual soundness, fairness, explainability, and ongoing monitoring are now baseline expectations. This note sets out what a complete model validation program looks like.
DORA's ICT risk management framework applies to AI systems used in financial services — including requirements for resilience testing, incident reporting, and third-party risk management. This note maps DORA obligations to AI-specific operational risks.
Most AI governance frameworks fail not because they are poorly designed but because they are not embedded in operations. This note sets out the design principles for an AI governance operating model that functions in practice — not just on paper.
Not every organisation needs a full-time Chief AI Officer. But every organisation deploying AI in a regulated environment needs senior AI leadership. This note makes the case for fractional AI leadership as a proportionate, practical response.
AI systems inherit the risks embedded in their data. Without robust data governance — ownership, lineage, quality, and access controls — AI governance programs are built on unstable ground. This note explains why data governance must come first.
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