This analysis compares leading AI risk management frameworks, including NIST, ISO/IEC, OECD, and the EU AI Act, to guide organizations in adopting responsible AI practices. It identifies common principles such as transparency, accountability, fairness, privacy, and security, while noting differences in scope and enforcement. The report emphasizes the importance of lifecycle-based risk management, from design to deployment, and highlights the role of governance structures, technical safeguards, and continuous monitoring. By aligning global standards with operational practices, enterprises can mitigate AI risks, meet regulatory requirements, and build trust in AI-driven systems while enabling innovation.

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