The rapid adoption of AI systems across various sectors has raised significant legal and ethical concerns, particularly in the U.S., where a comprehensive governance framework remains elusive. This paper explores the challenges and opportunities in developing ethical compliance models for AI governance, focusing on transparency, fairness, accountability, and alignment with privacy laws. It begins by examining the landscape of U.S. privacy regulations and the ethical dilemmas posed by AI systems, including bias and lack of transparency. Existing governance models and their deficiencies are analyzed alongside lessons from international approaches. The paper proposes a conceptual framework for ethical AI compliance, emphasizing the integration of privacy-by-design principles, algorithmic accountability, and collaborative stakeholder engagement. Case studies from U.S. businesses illustrate real-world governance challenges and best practices, offering actionable insights for policymakers and organizations. The conclusion highlights policy recommendations and future research directions to ensure responsible AI development, balancing innovation with societal values.