Stress-Tested Compliance: Counterfactual Simulators for Anomaly-Resilient Regulatory Operations

A counterfactual simulation framework that perturbs both regulatory conditions and operational data to probe brittleness in AI-driven compliance systems (e.g., AML, cloud, energy). Think of it as red-teaming for compliance: regulators and firms can preview how their Compliance-as-Code stacks behave under surprise regulatory updates, cross-border data shifts, or novel laundering typologies. This simulator explicitly explores deviations from expectation—regulatory “zero days.” It parameterizes scenarios with AGORA’s taxonomy of risks and governance strategies to generate counterfactual regulatory changes (e.g., sudden cross-jurisdictional constraints on data residency, new sectoral obligations like EU MDR-style post-market surveillance) and mirrors sectoral threat environments drawn from energy and cloud governance. The approach yields quantitative robustness metrics (time-to-compliance, false-positive drift, control coverage degradation) and design patterns for resilient agentic controls, guiding RL-based policy engines toward robust, not just optimal, policies. The impact is a pre-deployment “crash test” for regulatory systems that helps regulators and firms prioritize controls that maintain compliance under shocks, reducing systemic risk when regulations or threat landscapes change rapidly.

References:

1. Compliance-as-Code 2.0: Orchestrating Regulatory Operations with Agentic AI. Aman Sardana, Swaminathan Sethuraman, Priya Dharshini Kalyanasundaram (2024). Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023.
2. Introducing the AI Governance and Regulatory Archive (AGORA): An Analytic Infrastructure for Navigating the Emerging AI Governance Landscape. Zachary Arnold, Danielle Schiff, K. Schiff, Brian Love, Jennifer Melot, Neha Singh, Lindsay Jenkins, Ashley Lin, Konstantin Pilz, Ogadinma Enweareazu, Tyler Girard (2024). AAAI/ACM Conference on AI, Ethics, and Society.