Constitutional AI Governance for Enterprise Analytics: A Self- Regulating Architecture for Autonomous Policy Enforcement, Ethical Constraint Optimization, and Verifiable Compliance in AIDriven Decision Systems

Abstract

Enterprise AI systems increasingly influence operational and analytical decisions, yet policy compliance is often treated as an external review layer rather than an internal property of system behavior. Constitutional AI and responsible AI governance research suggest that trustworthy enterprise systems require explicit rule structures, ethical constraint handling, and verifiable enforcement rather than static post hoc oversight. The main gap is the lack of architectures that can autonomously enforce constitutional rules, balance competing ethical and operational constraints, and maintain auditable compliance across AIdriven workflows. This matters because technically effective systems can still become organizationally unreliable if governance weakens across decision stages. This article presents a self-regulating constitutional AI governance architecture for enterprise analytics that integrates policy representation, autonomous enforcement, ethical constraint optimization, and verifiable compliance tracing within the decision loop. The results show reduced policy violations across enforcement cycles and stronger compliance stability and ethical constraint satisfaction under stricter governance modes throughout the AI decision workflow. The study shows that constitutional governance can operate as an active architecture for trustworthy enterprise AI decision systems.