Practitioner perspectives from 28+ years at the intersection of data governance, regulatory compliance and emerging AI obligations. No theory โ only what works in practice.
The EU AI Act entered into force in August 2024, with obligations for high-risk AI systems kicking in from August 2026. With fewer than 12 months remaining, the gap between regulatory expectation and organisational readiness has never been wider. A practitioner's guide to the five actions that actually move the needle.
Double materiality is the conceptual heart of CSRD โ but the practical implementation is tripping up even experienced sustainability teams. The data governance dimension is where most go wrong.
Three different teams. Three different definitions of "material impact". One board report. The semantic fragmentation problem that sits at the root of most compliance failures โ and how metadata standardisation solves it.
Autonomous agents are being deployed faster than governance frameworks can catch up. Here are the five questions every audit committee should put on the agenda before the next AI system is approved.
The EU AI Act doesn't exist in isolation from GDPR. For DPOs, understanding where the obligations intersect โ and where they diverge โ is essential to building a coherent AI privacy governance programme.
Organisations are increasingly using synthetic ESG data for scenario modelling and CSRD preparation โ but most are doing it without any metadata governance, lineage tracking or audit readiness. Here's what good looks like.
DataConscious AI's integrated governance framework โ mapping data governance, AI governance and agentic AI governance to EU AI Act, CSRD, GDPR and regulatory reporting. The architecture behind all our services.
External auditors reviewing CSRD disclosures are finding the same five template failures repeatedly. Understanding what they are โ and fixing them before submission โ could save your organisation significant reputational cost.
The terms are often used interchangeably โ but they serve different purposes and have different compliance implications. Getting this distinction right shapes how you architect your data governance programme.
The theory of EU AI Act conformity assessments is well documented. What's less clear is what the practical audit process looks like for a high-risk AI system in a regulated financial services context. Here's a real-world walkthrough.