Designing Internal Governance, Compliance, and Audit Mechanisms for Responsible AI in Firms: Navigating Evolving External Regulation

Abstract

Firms deploying AI face legal, reputational, fairness, and trust-related risks that hinder adoption. This paper examines how organizations can design internal governance, compliance, and audit mechanisms to manage AI risks while adapting to changing external regulation. We integrate policy analysis, design science, action research, and comparative case studies across industries and countries to (1) identify effective internal processes (audit trails, red‑teaming, AI impact assessments) for preventing harms, (2) analyze how differing national regulations reshape corporate AI governance strategies, and (3) propose KPIs and board-level monitoring frameworks for AI risk. We present a prescriptive governance design—the Responsible AI Management System (RAIMS)—and evaluate it against industry cases from finance, healthcare, and technology sectors in three jurisdictions (EU, US, Singapore). We conclude with actionable recommendations for firms, policymakers, and auditors.