Principles Of Governance In Generative Ai
Published 10/2024
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 2.94 GB | Duration: 16h 57m
Navigating Risks, Compliance, and Ethics for Responsible Generative AI
What you'll learn
The Fundamentals of Generative AI (GenAI): Understand the core concepts and transformative potential of GenAI technology.
The Importance of Governance in AI: Explore why governance frameworks are essential for managing AI innovations responsibly.
Risk Identification and Management: Learn to identify, assess, and mitigate risks associated with deploying GenAI systems.
Third-Party Risk Management: Gain insight into evaluating and monitoring external partnerships to reduce third-party risks.
Vendor Compliance Strategies: Develop skills to ensure that vendors align with governance and security policies.
Data Leakage Prevention: Understand the risks of data leakage and explore methods to protect sensitive information in AI workflows.
Data Governance Frameworks: Learn how to define data ownership, stewardship, and retention policies for AI systems.
Regulatory Compliance in AI: Explore key regulations affecting GenAI, including strategies for managing compliance across jurisdictions.
Access Control Implementation: Gain practical insights into role-based access controls to secure GenAI applications.
User Awareness and Training Programs: Discover effective strategies for developing user training and awareness initiatives.
Monitoring User Behavior: Learn how to monitor GenAI system usage to detect anomalies and prevent misuse.
Identity Governance for AI Systems: Understand how to manage user identities and authentication securely in AI platforms.
Incident Response Planning: Develop strategies to respond effectively to AI-related incidents and conduct post-incident analysis.
Ethical Considerations in GenAI: Explore the ethical challenges in AI governance, focusing on transparency, fairness, and bias mitigation.
Governance of Approved Applications: Learn how to evaluate and update approved GenAI tools to align with evolving policies.
Future Trends in GenAI Governance: Gain insights into emerging technologies, AI regulation trends, and the future of AI governance practices.
Requirements
No Prerequisites.
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.
Overview
Section 1: Course Resources and Downloads
Lecture 1 Course Resources and Downloads
Section 2: Introduction to Generative AI (GenAI) Governance
Lecture 2 Section Introduction
Lecture 3 What is Generative AI?
Lecture 4 Case Study: Bridging Creativity and Ethics in Digital Art and Music
Lecture 5 The Importance of Governance in GenAI
Lecture 6 Case Study: Navigating GenAI Governance
Lecture 7 Overview of GenAI Risks
Lecture 8 Case Study: Navigating Ethical and Practical Challenges in Generative AI
Lecture 9 Key Stakeholders in GenAI Governance
Lecture 10 Case Study: Navigating GenAI in Healthcare
Lecture 11 Governance Frameworks for GenAI
Lecture 12 Case Study: Building Ethical AI
Lecture 13 Section Summary
Section 3: Understanding Third-Party Risk Management in GenAI
Lecture 14 Section Introduction
Lecture 15 Defining Third-Party Risk
Lecture 16 Case Study: Navigating Third-Party Risks
Lecture 17 Identifying and Assessing Third-Party Risks
Lecture 18 Case Study: Managing Third-Party Risks in Generative AI
Lecture 19 Mitigating Third-Party Risks in GenAI Applications
Lecture 20 Case Study: Enhancing Third-Party Risk Management in AI
Lecture 21 Vendor Compliance in GenAI Systems
Lecture 22 Case Study: Mastering Vendor Compliance
Lecture 23 Continuous Monitoring of Third-Party Relationships
Lecture 24 Case Study: Enhancing GenAI Innovation
Lecture 25 Section Summary
Section 4: Data Leakage Protection in GenAI Systems
Lecture 26 Section Introduction
Lecture 27 Understanding Data Leakage in GenAI
Lecture 28 Case Study: Addressing Data Leakage in Generative AI
Lecture 29 Data Leakage Risks in Generative AI Models
Lecture 30 Case Study: Navigating Data Privacy Challenges in Generative AI
Lecture 31 Protecting Sensitive Data in GenAI Workflows
Lecture 32 Case Study: Balancing Innovation and Security
Lecture 33 Data Rights Management in GenAI
Lecture 34 Case Study: Balancing GenAI Innovation and Data Rights
Lecture 35 Preventing Data Exfiltration in GenAI
Lecture 36 Case Study: Strategies for Protecting Sensitive Data in GenAI
Lecture 37 Section Summary
Section 5: Regulatory Compliance in Generative AI
Lecture 38 Section Introduction
Lecture 39 Overview of Regulatory Compliance for AI Systems
Lecture 40 Case Study: Navigating AI Governance
Lecture 41 Key Regulations Affecting GenAI Governance
Lecture 42 Case Study: Navigating GenAI Innovation
Lecture 43 Compliance Strategies for GenAI Applications
Lecture 44 Case Study: Navigating Compliance Challenges in GenAI
Lecture 45 Managing Compliance Across Jurisdictions
Lecture 46 Case Study: Navigating AI Innovation and Compliance
Lecture 47 Reporting and Documentation for Regulatory Audits
Lecture 48 Case Study: Navigating Compliance
Lecture 49 Section Summary
Section 6: Enforcing Access Policies for GenAI Applications
Lecture 50 Section Introduction
Lecture 51 Access Control Fundamentals for GenAI
Lecture 52 Case Study: Adaptive Access Control Strategies for GenAI
Lecture 53 Implementing Role-Based Access in GenAI
Lecture 54 Case Study: Enhancing Security
Lecture 55 Restricting Unauthorized Access to GenAI Tools
Lecture 56 Case Study: Enhancing GenAI Security
Lecture 57 Enforcing Data Access Policies
Lecture 58 Case Study: Navigating Data Governance in GenAI
Lecture 59 Access Review and Revocation Processes
Lecture 60 Case Study: Optimizing Access Management for GenAI Security
Lecture 61 Section Summary
Section 7: User Awareness and Training for GenAI
Lecture 62 Section Introduction
Lecture 63 The Role of User Training in GenAI Governance
Lecture 64 Case Study: Navigating Ethical Challenges in GenAI
Lecture 65 Developing Effective GenAI User Awareness Programs
Lecture 66 Case Study: Empowering Ethical AI Use
Lecture 67 Common User Missteps in GenAI Usage
Lecture 68 Case Study: Strategic GenAI Integration
Lecture 69 Best Practices for Training on GenAI Use Policies
Lecture 70 Case Study: Navigating Ethical AI Implementation and Training Challenges
Lecture 71 Monitoring and Updating User Training Programs
Lecture 72 Case Study: Enhancing GenAI Integration
Lecture 73 Section Summary
Section 8: Approved and Disapproved GenAI Applications
Lecture 74 Section Introduction
Lecture 75 Identifying Safe GenAI Tools
Lecture 76 Case Study: Navigating Bias and Ethics
Lecture 77 Evaluating GenAI Applications for Governance Compliance
Lecture 78 Case Study: Navigating AI Governance
Lecture 79 Risks of Unapproved GenAI Applications
Lecture 80 Case Study: Navigating Ethical AI
Lecture 81 Approval Processes for GenAI Tools
Lecture 82 Case Study: TechNova's Journey to Responsible GenAI Deployment
Lecture 83 Updating and Communicating Approved Applications
Lecture 84 Case Study: TechNova's Journey in Responsible Innovation and Governance
Lecture 85 Section Summary
Section 9: Identity Governance in GenAI Systems
Lecture 86 Section Introduction
Lecture 87 Understanding Identity Governance for AI
Lecture 88 Case Study: Balancing Privacy, Compliance, and Ethics in Identity Management
Lecture 89 Managing User Identities in GenAI Platforms
Lecture 90 Case Study: Navigating Identity Management Challenges in GenAI
Lecture 91 Ensuring Secure Authentication in GenAI Applications
Lecture 92 Case Study: Balancing Authentication, User Convenience, and Privacy
Lecture 93 Identity Lifecycle Management in GenAI
Lecture 94 Case Study: Navigating Identity Lifecycle Management in Generative AI Systems
Lecture 95 Addressing Identity Risks in GenAI
Lecture 96 Case Study: Identity Governance Challenges in GenAI
Lecture 97 Section Summary
Section 10: Risk Modeling and Management for GenAI
Lecture 98 Section Introduction
Lecture 99 Introduction to Risk Modeling in GenAI
Lecture 100 Case Study: Navigating Risks in Generative AI
Lecture 101 Identifying Key Risks in GenAI Operations
Lecture 102 Case Study: Balancing Innovation, Bias Mitigation, and Workforce Stability
Lecture 103 Quantifying and Prioritizing GenAI Risks
Lecture 104 Case Study: Balancing Innovation with Ethical Risk Management at TechNova
Lecture 105 Strategies for Mitigating GenAI Risks
Lecture 106 Case Study: Navigating Ethical and Operational Challenges in GenAI Deployment
Lecture 107 Monitoring and Adapting Risk Models
Lecture 108 Case Study: TechNova's Holistic Approach to Risk Management and Innovation
Lecture 109 Section Summary
Section 11: Data Governance for Generative AI Systems
Lecture 110 Section Introduction
Lecture 111 The Importance of Data Governance in GenAI
Lecture 112 Case Study: Navigating Data Governance and Ethics in GenAI
Lecture 113 Defining Data Ownership and Stewardship in GenAI
Lecture 114 Case Study: Navigating Data Governance Challenges in GenAI
Lecture 115 Data Integrity and Accuracy in GenAI Systems
Lecture 116 Case Study: TechNova's Journey to Ethical and Reliable AI Data Management
Lecture 117 Policies for Data Retention and Deletion in GenAI
Lecture 118 Case Study: Balancing Compliance and Innovation
Lecture 119 Auditing Data Governance Practices in GenAI
Lecture 120 Case Study: Enhancing Trust through Comprehensive Data Governance in AI
Lecture 121 Section Summary
Section 12: User Behavior Monitoring in GenAI Systems
Lecture 122 Section Introduction
Lecture 123 Monitoring User Activity in GenAI Platforms
Lecture 124 Case Study: MedSys's GenAI Integration in Healthcare Diagnostics
Lecture 125 Identifying Anomalous Behavior in GenAI Use
Lecture 126 Case Study: Enhancing Anomaly Detection in GenAI Systems
Lecture 127 Tools for Tracking GenAI User Activity
Lecture 128 Case Study: Balancing Ethical AI and Privacy
Lecture 129 Privacy Considerations in User Monitoring
Lecture 130 Case Study: Balancing Innovation and Privacy
Lecture 131 Responding to Suspicious Behavior in GenAI
Lecture 132 Case Study: Balancing Trust, Privacy, and Collaborative Defense Strategies
Lecture 133 Section Summary
Section 13: Acceptable Use Policies for GenAI Applications
Lecture 134 Section Introduction
Lecture 135 Defining Acceptable Use for GenAI
Lecture 136 Case Study: Crafting Responsible GenAI Use
Lecture 137 Crafting Comprehensive Use Policies for GenAI
Lecture 138 Case Study: Developing Responsible GenAI Policies
Lecture 139 Educating Users on Acceptable Use Policies
Lecture 140 Case Study: Crafting a Balanced AUP
Lecture 141 Enforcing Acceptable Use Guidelines
Lecture 142 Case Study: Ethical Governance Strategies for GenAI
Lecture 143 Revising Acceptable Use Policies
Lecture 144 Case Study: Navigating AI Ethics
Lecture 145 Section Summary
Section 14: Incident Response and Management for GenAI Systems
Lecture 146 Section Introduction
Lecture 147 Defining GenAI Incidents
Lecture 148 Case Study: Navigating GenAI Challenges
Lecture 149 Incident Response Planning for GenAI Applications
Lecture 150 Case Study: Enhancing GenAI Safety
Lecture 151 Key Steps in Managing GenAI Incidents
Lecture 152 Case Study: TechNova's Strategic Response to GenAI Incident
Lecture 153 Post-Incident Analysis and Reporting
Lecture 154 Case Study: Enhancing AI Governance
Lecture 155 Lessons Learned from GenAI Incidents
Lecture 156 Case Study: Ensuring AI Accountability
Lecture 157 Section Summary
Section 15: Ethical Considerations in GenAI Governance
Lecture 158 Section Introduction
Lecture 159 Ethical Challenges in Generative AI
Lecture 160 Case Study: Navigating Ethical Challenges of GenAI in Newsrooms
Lecture 161 Ensuring Transparency and Fairness in GenAI
Lecture 162 Case Study: Balancing Innovation and Ethics
Lecture 163 Bias Mitigation in GenAI Outputs
Lecture 164 Case Study: Tackling Bias in Generative AI
Lecture 165 Responsible AI Practices and GenAI Governance
Lecture 166 Case Study: TechNova's Journey Towards Responsible Innovation and Governance
Lecture 167 Ethical Audits for GenAI Systems
Lecture 168 Case Study: Navigating Ethical Challenges in Generative AI
Lecture 169 Section Summary
Section 16: Future Trends and Innovations in GenAI Governance
Lecture 170 Section Introduction
Lecture 171 Emerging Technologies in GenAI Governance
Lecture 172 Case Study: Blockchain and Ethical AI
Lecture 173 AI Regulation and Policy Trends
Lecture 174 Case Study: Global AI Regulation
Lecture 175 Integrating AI Governance into Broader Corporate Governance
Lecture 176 Case Study: Integrating AI Governance
Lecture 177 Automation and AI Governance Tools
Lecture 178 Case Study: Navigating AI Governance: Transparency, Fairness, and Privacy
Lecture 179 The Future of GenAI Governance Practices
Lecture 180 Case Study: InnovateAI: Crafting a Global Framework for Responsible GenAI
Lecture 181 Section Summary
Business Leaders and Executives seeking to align AI innovation with governance frameworks and ethical practices.,AI and Data Governance Professionals responsible for developing policies and managing risks associated with Generative AI systems.,Compliance Officers and Legal Advisors aiming to understand the regulatory landscape and ensure compliance with AI laws across jurisdictions.,IT Managers and System Administrators involved in the implementation, monitoring, and security of AI platforms.,Risk Management Professionals looking to enhance their skills in assessing and mitigating risks specific to AI technologies.,Educators and Researchers in AI Ethics and Policy interested in the latest governance strategies and frameworks for responsible AI use.,Tech Enthusiasts and Consultants who want to stay ahead of trends in AI governance to better advise businesses and organizations.
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