AI systems in sensitive areas such as health care, law, and finance increasingly intersect with ethical concerns. Focusing on bias, trust, responsibility, reliability, and sustainability, this chapter uncovers a set of 15 principles enterprises must adopt for embedding ethics into AI systems. It outlines the need to curb algorithmic bias, enhance transparency and explainability, protect privacy and security, enforce accountable systems, and mitigate environmental impact. This chapter illustrates effective AI governance with policies from various countries while also developing frameworks for monitoring ethical compliance. AI trust intensely depends on design and constant stakeholder collaboration; thus, trust is dynamic rather than static. Adopting ethical frameworks isn’t simply complying with policies; it fosters societal trust, drives innovation, and builds organizational resilience.