Expert perspectives on compliance, regulatory frameworks, and industry best practices.
The EU AI Act will test more than whether organisations have written AI policies. It will test whether they can represent AI obligations structurally across transparency, general-purpose AI, governance, documentation, oversight, risk management and wider compliance architecture.
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Enterprises already govern canonical systems in identity, finance, IT, and product data. The same governance patterns apply to regulatory meaning, turning obligations, concepts, and mappings into stable infrastructure rather than drifting documentation.
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Excel persists in compliance because it is the fastest way to create local truth. It also creates ungoverned drift. The real cost is not the spreadsheet itself, but the multiplication of versions, mappings, and interpretations across the enterprise.
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Cross-jurisdiction mapping drift rarely begins with a control failure. It begins when obligation meaning silently fractures across frameworks, teams, and updates. That drift becomes visible during audits, when defensibility and consistency are finally tested.
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Vendor lock-in is usually treated as a negative. Canonical regulatory infrastructure is different. When switching cost is created by governance, structural correctness, stable identifiers, and reproducible mappings, it reflects standardization rather than arbitrary dependency.
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There are many regtech tools, but very few structural regulatory infrastructure platforms. This layer stays uncrowded because it is slower, harder, and more trust dependent to build than monitoring, workflow, or document based compliance tooling.
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Most regulatory repositories are readable but not production-grade. This article explains why systems integration requires stable identifiers, versioning, integrity constraints, provenance, and reproducible exports rather than mostly consistent datasets.
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AI can accelerate compliance work through summarisation, extraction, and mapping suggestions. But systems of record need more than plausible outputs. They need deterministic structure that is stable, auditable, versioned, and reproducible across runs.
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Most compliance tooling still treats regulation as text. But text is not a stable interface for cross framework comparison. Regulation needs an encoding layer that makes meaning structured, governed, versioned, and reproducible across jurisdictions.
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Control libraries do not usually bloat because teams are careless. They bloat because overlapping obligations are translated into duplicated controls without structural constraints. The result is hidden cost across audits, evidence, tooling, and control ownership.
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Regulation is growing, but compliance cost often grows faster. The difference lies in duplication, drift, and repeated interpretation across overlapping frameworks, not just in the volume of new regulatory requirements.
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GRC platforms are built to execute compliance, not normalize overlapping obligations across frameworks and jurisdictions. Structural duplication needs a layer beneath GRC that stabilizes meaning before controls, evidence, and workflows are applied.
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