Analysis of systems-level ethical AI compliance architecture for U.S. corporations: Integrating governance, risk management, and automated accountability

Abstract

The purpose of this study is to analyze the systems-level ethical AI compliance architecture for corporations in the US by integrating governance frameworks, risk management systems, automated accountability mechanisms, and legal alignment strategies. This study examines the rapid transformation of the ways that corporations operate due to these AI technologies, concurrent with ethical, legal, and operational challenges involving algorithmic bias, privacy breaches, cybersecurity risk, and transparency. Results showed that various governance models, including the National Institute of Standards and Technology AI Risk Management Framework (AI RMF), enhance organizational accountability, transparency, and regulatory compliance. Compliance-by-design measures, explainable AI systems, and automated auditing technologies also strengthen AI governance and regulatory adherence. The study also found that integrated ethical AI compliance architectures are critical for innovation in the responsible application of cutting-edge technologies, organizational sustainability, stakeholder trust, and long-term corporate resilience as businesses operate in an increasingly technology-driven environment. Keywords: Artificial Intelligence Governance, Ethical AI Compliance, Risk Management, Automated Accountability, Legal Alignment.