The widespread usage of machine learning (ML) and deep learning (DL) in critical sectors like finance, healthcare, and transportation has raised fears regarding exposure to adversarial attacks. The chapter identifies legal and ethical concerns of evading such threats, in terms of transparency, fairness, and security. It discusses such best practices as Google’s Secure AI Framework, which integrates fairness and privacy values in adversarial defenses, and Microsoft’s Responsible AI Framework, which gives importance to fairness and transparency in cybersecurity. MasterCard AI-enabled fraud detection also embodies compliance with global regulatory standards alongside the prevention of financial crime. Shortcomings like Facebook’s mechanism for detecting disinformation and COMPAS algorithm racial bias are symptomatic of the dangers of not having adequate regulation and transparency. These case studies emphasize the importance of robust ethical regulation and constant improvisation in the face of adaptive threats. The chapter concludes that the alignment of AI ethics codes to international regulations is crucial for the development of responsible, secure, and inclusive AI systems.