Academic Foundations

The analysis behind the advisory.

Greyline's strategic work is grounded in original graduate research into EU AI policymaking — conducted at the Technical University of Munich before the EU AI Act entered enforcement.

Most advisors interpret the EU AI Act. Greyline understands why it was written the way it was — and where that reasoning breaks down.

Thesis Details

Institution: Technical University of Munich (TUM)
Programme: MSc Science and Technology Studies
Focus: EU AI Act — institutional logic, risk framing, regulatory blind spots

The Research

Governing the Uncertain: Systemic Risk and Sociotechnical Imaginaries in EU AI Policy

This research examined how systemic risk is conceptualised and regulated in EU AI policymaking — not what the EU AI Act says, but how regulators think about AI risk at a foundational level.

Using STS frameworks, the research conducted in-depth discourse and policy analysis across the EU AI Act and GPAI documents, examining:

How regulatory language constructs and frames AI risk
The institutional imaginaries that shape what regulators see as governable — and what they do not
The narrative shifts in EU AI policymaking across successive drafts
The blind spots embedded in the Act's risk classification logic

What This Means in Practice

From academic analysis to strategic advantage

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Anticipation over reaction

Understanding the institutional logic allows organizations to anticipate where enforcement will focus before it occurs — rather than reacting to guidance after the fact.

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Governance that survives regulatory evolution

Frameworks built on underlying risk logic, not just current compliance text, remain robust as the Act's secondary legislation and enforcement guidance continues to develop.

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MENA applicability