[GET] Principles of Governance in Generative AI

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Principles of Governance in Generative AI
Navigating Risks, Compliance, and Ethics for Responsible Generative AI
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139 students
17 hours on-demand video
1 article
75 downloadable resources

Description
This course offers a comprehensive exploration of governance frameworks, regulatory compliance, and risk management tailored to the emerging field of Generative AI (GenAI). Designed for professionals seeking a deeper understanding of the theoretical foundations that underpin effective GenAI governance, this course emphasizes the complex interplay between innovation, ethics, and regulatory oversight. Students will engage with essential concepts through a structured curriculum that delves into the challenges and opportunities of managing GenAI systems, equipping them to anticipate risks and align AI deployments with evolving governance standards.

The course begins with an introduction to Generative AI, outlining its transformative potential and the importance of governance to ensure responsible use. Participants will examine key risks associated with GenAI, gaining insight into the roles of various stakeholders in governance processes. This early focus establishes a theoretical framework that guides students through the complexities of managing third-party risks, including the development of vendor compliance strategies and continuous monitoring of external partnerships. Throughout these sections, the curriculum emphasizes how thoughtful governance not only mitigates risks but also fosters innovation in AI applications.

Participants will explore the intricacies of regulatory compliance, focusing on the challenges posed by international legal frameworks. This segment highlights strategies for managing compliance across multiple jurisdictions and the importance of thorough documentation for regulatory audits. The course also covers the enforcement of access policies within GenAI applications, offering insight into role-based access and data governance strategies that secure AI environments against unauthorized use. These discussions underscore the need for organizations to balance security and efficiency while maintaining ethical practices.

Data governance is a recurring theme, with modules that explore the risks of data leakage and strategies for protecting sensitive information in GenAI workflows. Students will learn how to manage data rights and prevent exfiltration, fostering a robust understanding of the ethical implications of data use. This section also introduces students to identity governance, illustrating how secure authentication practices and identity lifecycle management can enhance the security and transparency of AI systems. Participants will be encouraged to think critically about the intersection between privacy, security, and user convenience.

Risk modeling and management play a central role in the curriculum, equipping students with the tools to identify, quantify, and mitigate risks within GenAI operations. The course emphasizes the importance of proactive risk management, presenting best practices for continuously monitoring and adapting risk models to align with organizational goals and ethical standards. This focus on continuous improvement prepares students to navigate the dynamic landscape of AI governance confidently.

Participants will also develop skills in user training and awareness programs, learning how to craft effective training initiatives that empower users to engage with GenAI responsibly. These modules stress the importance of monitoring user behavior and maintaining awareness of best practices in AI governance, further strengthening the theoretical foundation of the course. Through this emphasis on training, students will gain practical insights into how organizations can foster a culture of responsible AI use and compliance.

As the course concludes, students will explore future trends in GenAI governance, including the integration of governance frameworks within broader corporate strategies. The curriculum encourages participants to consider how automation, blockchain, and emerging technologies can support AI governance efforts. This forward-looking approach ensures that students leave with a comprehensive understanding of how governance practices must evolve alongside technological advancements.

This course offers a detailed, theory-based approach to GenAI governance, emphasizing the importance of thoughtful risk management, compliance, and ethical considerations. By engaging with these critical aspects of governance, participants will be well-prepared to contribute to the development of responsible AI systems, ensuring that innovation in GenAI aligns with ethical principles and organizational goals.



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