The Compliance Elasticity Hypothesis: When Automation Helps—or Hurts—Under Regulatory Complexity

A formal model of “compliance elasticity” that predicts whether AI automation increases or decreases effective compliance as complexity rises, tested via natural experiments and difference-in-differences using sectoral adoptions of AI auditing and cloud policy enforcement. This reconciles conflicting findings where audit tech improves compliance but complexity undermines effectiveness, and where new regulations create integration conflicts despite efficiency gains. The project models thresholds where automation flips from beneficial to brittle and identifies policy levers (e.g., standardization vs. autonomy). It builds on continuous auditing constructs, governance frameworks, and unification strategies to measure operational complexity, control density, and regulatory heterogeneity, validated across finance/AML, energy, and public programs. Produces policy-relevant thresholds (e.g., below certain heterogeneity, agentic controls outperform; above it, unify controls first then automate) and shows when explainability mandates improve outcomes by reducing brittleness costs. The impact is a theory-driven blueprint for sequencing governance reforms—standardize, simplify, then automate—informing regulators on structuring regimes and firms on deploying advanced RegTech.

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