GenAI Governance

Learn how AI governance uses rules, guidelines, and processes to develop and deploy AI ethically and responsibly. Balance innovation with accountability, transparency, fairness, privacy, and trust in generative AI governance.

Discover generative AI, a subset of machine learning, how it differs from traditional AI, and why Gen AI governance matters as neural networks generate complex outputs.

Celebrate reaching this milestone and recognize your commitment, then access playback controls, subtitles, offline viewing, Q&A, an AI assistant, and a downloadable certificate to showcase your progress.

Differentiate analytical AI, which analyzes data for insights and predictions, from generative AI, which creates new text, images, and code using large language models and multimodal tools.

Explore how generative ai transforms industries, balancing benefits with six key risks—harmful content, copyright and legal exposure, data privacy, sensitive disclosures, data provenance, and explainability—and responsible governance.

examine bias and fairness in generative ai outputs, explainability challenges, and security and misuse prevention, including deepfakes, misinformation, and copyright concerns.

Explore governance, risk management, and compliance for generative AI, balancing growth with accountability. Learn how governance frameworks, risk assessment, and regulatory standards like GDPR guide responsible deployment.

Data governance defines rules, policies, and frameworks to manage data responsibly, accessibly, reliably, and securely, with roles like data owners and stewards and compliance with GDPR and HIPAA.

Learn how clear data governance policies drive efficiency, scalability, cost reduction, better decisions, and enhanced customer experience by ensuring accurate, centralized data management.

Manage risks in generative AI by securing data, protecting privacy, safeguarding reputation, staying compliant with laws, and mitigating bias when using third-party models and datasets.

Learn practical risk mitigation for generative AI, including data minimization, de-identification, bias detection, human oversight, and controls like access, encryption, audits, continuous monitoring, incident response, and regular reporting.

Identify and mitigate third-party risk through due diligence, solid contracts, and ongoing monitoring. Evaluate security protocols, data governance, and compliance to protect data, ensure accountability, and sustain trust.

Explore data lifecycle management as a core element of AI risk management, from acquisition and storage to disposal, emphasizing ethical sourcing, quality, security, and privacy.

Apply access control, encryption at rest and in transit, differential privacy, and federated learning to protect sensitive data in generative systems.

Enforce access policies through authentication and authorization, and apply identity governance to control who can use generative AI. Educate users on responsible use, data privacy, and approved applications.

Apply the EU AI Act's risk-based governance for high-risk systems, enforce safety and transparency, prohibit harmful practices, and align with UNESCO's ethics for fairness and accountability in generative AI.

Explore the NIST AI risk management framework, a voluntary, flexible guide built on governance, map, measure, and manage to identify, assess, and mitigate AI risk.

Discover toolkits and frameworks for responsible ai, including IBM Watson governance, Microsoft AI principles, and AWS responsible ai, with end-to-end lifecycle management, monitoring, risk management and compliance, and deployment flexibility.

Complete the course and download your certificate of completion after the final milestone, recognizing you are among the top 5%, with options to check missing lectures and contact support.