Training Course on AI Governance and Regulatory Compliance for Directors

Training Course on AI Governance and Regulatory Compliance for Directors

Introduction

In today's rapidly evolving technological landscape, Artificial Intelligence (AI) is transforming every sector, offering unprecedented opportunities for innovation and efficiency. However, the pervasive adoption of AI also introduces complex ethical, legal, and operational risks that demand robust AI governance frameworks and meticulous regulatory compliance. Training Course on AI Governance and Regulatory Compliance for Directors is specifically designed to equip directors and senior leaders with the essential knowledge and strategic insights to confidently navigate the intricate world of AI, ensuring responsible development, deployment, and oversight within their organizations. By focusing on ethical AI, risk management, and legal frameworks like the impending EU AI Act, this program empowers board members to fulfill their fiduciary duties and drive sustainable value in the AI era.

The imperative for effective AI governance extends beyond mere compliance; it is a critical driver of organizational resilience, reputation protection, and sustainable innovation. Boards must proactively establish clear policies, define accountability, and integrate AI risk assessment into their enterprise-wide strategies. This comprehensive training will delve into practical methodologies and global best practices, providing directors with the tools to build trustworthy AI systems, mitigate algorithmic bias, ensure data privacy, and foster a culture of responsible AI stewardship. Through expert-led discussions and real-world case studies, participants will gain a deep understanding of how to translate theoretical principles into actionable governance strategies, safeguarding their organizations against emerging AI-related challenges and unlocking the full potential of this transformative technology.

Course Duration

5 days

Course Objectives

1. Understand the core concepts, capabilities, and limitations of Artificial Intelligence and its sub-fields, particularly Generative AI, for informed strategic decision-making.
2. Comprehend the intricacies of emerging AI regulations globally, including the EU AI Act, NIST AI RMF, and other key frameworks impacting corporate governance.
3. Develop and implement comprehensive AI governance frameworks tailored to organizational needs, ensuring ethical and responsible AI adoption.
4. Proactively identify and assess various AI risks, including algorithmic bias, data privacy breaches, cybersecurity vulnerabilities, and ethical dilemmas, with effective mitigation strategies.
5. Embed ethical AI principles (fairness, transparency, explainability, accountability) into the entire AI lifecycle, fostering public trust and responsible innovation.
6. Understand the critical role of robust data governance in powering reliable and ethical AI systems, including data quality, security, and privacy compliance.
7. Gain insights into the AI system lifecycle, from development to deployment, and establish effective oversight mechanisms for quality and compliance.
8. Recognize and fulfill the board's evolving fiduciary duties concerning AI oversight, risk management, and strategic alignment.
9. Formulate clear and actionable AI policies and internal procedures for responsible AI use, aligning with legal and ethical standards.
10. Champion and embed a proactive culture of responsible AI throughout the organization, from executive leadership to operational teams.
11. Implement effective AI risk assessment methodologies and internal audit processes to monitor compliance and performance of AI systems.
12. Understand the emerging landscape of AI liability and legal recourse, protecting the organization from potential legal challenges and penalties.
13. Balance the pursuit of AI innovation with rigorous governance, ensuring long-term value creation and competitive advantage while adhering to ethical and regulatory boundaries.

Organizational Benefits

• Proactive adherence to evolving global AI regulations, minimizing legal risks and avoiding substantial fines (e.g., up to 7% of global turnover under the EU AI Act).
• Demonstrating a commitment to ethical and responsible AI builds stakeholder trust, enhances brand image, and fosters positive public perception.
• Systematic identification, assessment, and mitigation of AI-related risks, leading to greater operational resilience and reduced potential for financial and reputational damage.
• Organizations with robust AI governance frameworks are better positioned to innovate responsibly, adopt AI technologies more rapidly, and gain a sustainable edge in the market.
• A clear commitment to ethical and compliant AI practices signals strong corporate governance, attracting and retaining investors.
• Efficient allocation of resources towards compliant and ethical AI initiatives, preventing costly reworks and legal battles.
• Encouraging AI innovation within defined ethical and regulatory boundaries, ensuring that new technologies align with organizational values and societal expectations.
• Attracting and retaining top AI talent who are increasingly seeking organizations committed to responsible AI development and deployment.

Target Audience

1. Board Directors (Executive & Non-Executive)
2. Senior Executives & C-Suite Leaders (CEO, CTO, CIO, CRO, CLO)
3. Heads of Legal and Compliance Departments
4. Chief Risk Officers
5. Company Secretaries
6. Heads of Innovation and Digital Transformation
7. Audit Committee Members
8. Government and Regulatory Officials overseeing AI policy

Course Outline

Module 1: Foundations of AI and the Governance Imperative

• Understanding AI: Machine Learning, Deep Learning, and the rise of Generative AI.
• The transformational impact of AI on business models and society.
• Why AI governance is critical: Beyond technical challenges to ethical and societal implications.
• Distinction between AI ethics, AI governance, and AI regulation.
• The board's role in setting the tone for responsible AI.
• Case Study: The challenges faced by a large tech company due to unchecked AI bias in a hiring algorithm, leading to reputational damage and legal action. Discussion on how robust governance could have prevented this.

Module 2: Global AI Regulatory Landscape & The EU AI Act

• Overview of the evolving global AI regulatory landscape: Key legislative trends and jurisdictional differences.
• Deep dive into the EU AI Act: Risk-based classification, prohibited AI, high-risk AI systems, and general-purpose AI.
• Compliance obligations for providers and deployers of AI systems under the EU AI Act.
• Other significant frameworks: NIST AI Risk Management Framework (RMF), OECD AI Principles, and national strategies.
• Navigating regulatory divergence and harmonization efforts.
• Case Study: Analysis of a company's compliance strategy in light of the EU AI Act's coming into force, focusing on a high-risk AI application in the healthcare sector.

Module 3: Ethical AI Principles and Practical Implementation

• Core ethical principles of AI: Fairness, accountability, transparency, explainability, human oversight, privacy, and robustness.
• Identifying and mitigating algorithmic bias: Detection tools and techniques.
• Designing for explainable AI (XAI): Methods for making AI decisions understandable.
• The human-in-the-loop: Ensuring meaningful human oversight in AI systems.
• Establishing an organizational AI ethics committee or review board.
• Case Study: Examining a financial institution's implementation of an ethical AI framework to ensure fairness in credit scoring, including the use of explainability tools and human review processes.

Module 4: AI Risk Management & Assessment for Directors

• Categorizing and assessing AI-specific risks: Operational, legal, reputational, security, and strategic risks.
• Integrating AI risk assessment into the enterprise risk management (ERM) framework.
• Developing an AI risk register and establishing risk appetite.
• Cybersecurity for AI: Protecting AI models and data from malicious attacks.
• Incident response planning for AI system failures or breaches.
• Case Study: A multinational corporation's response to a significant AI system failure that impacted critical business operations, highlighting the importance of pre-emptive risk assessments and robust incident response plans.

Module 5: Data Governance as the Backbone of AI Governance

• The indispensable link between data governance and AI governance.
• Ensuring data quality, integrity, and lineage for reliable AI models.
• Data privacy and protection regulations: GDPR, CCPA, and their implications for AI.
• Ethical data sourcing and synthetic data generation.
• Data retention, anonymization, and data subject rights in an AI context.
• Case Study: A retail company facing a data privacy breach due to inadequate data governance practices related to their customer recommendation AI system. Discussion on necessary controls.

Module 6: Building AI Governance Frameworks & Policies

• Key components of a robust AI governance framework: Structures, roles, responsibilities, and processes.
• Developing an organizational AI policy and code of conduct.
• Establishing clear lines of accountability for AI decision-making.
• Vendor management for AI solutions: Due diligence and contractual clauses.
• Implementing internal controls and audit trails for AI systems.
• Case Study: How a manufacturing firm established a cross-functional AI governance committee and developed internal policies to guide the adoption of AI in their production lines.

Module 7: Board Oversight, Reporting & Future Trends

• The board's role in challenging management on AI strategy and governance.
• Key performance indicators (KPIs) for measuring AI governance effectiveness.
• Reporting AI risks and compliance status to the board and stakeholders.
• Forecasting future trends in AI regulation and technology.
• Preparing for AI audits and regulatory scrutiny.
• Case Study: A board meeting simulation where directors review and challenge management's AI strategy and risk report, identifying gaps and proposing improvements.

Module 8: Practical AI Governance Implementation & Case Studies

• Translating governance principles into actionable implementation plans.
• Developing a roadmap for AI governance maturity within the organization.
• Best practices for integrating AI governance into existing corporate governance structures.
• Interactive problem-solving scenarios based on real-world AI governance dilemmas.
• Q&A and open discussion on specific organizational challenges.
• Case Study: A detailed examination of a successful AI governance implementation in a leading financial services firm, highlighting key challenges overcome and lessons learned.
• Case Study: Discussing the implications of a recent regulatory enforcement action against a company for an AI-related ethical violation, emphasizing preventative measures.

Training Methodology

This training will employ a highly interactive and practical methodology, designed to engage directors and foster deep understanding:

• Expert-Led Presentations: Concise and insightful presentations from leading AI governance and legal experts.
• Interactive Discussions: Facilitated discussions and debates to encourage peer learning and sharing of perspectives.
• Real-World Case Studies: In-depth analysis of actual corporate scenarios and regulatory actions to illustrate concepts and best practices.
• Group Exercises & Workshops: Practical exercises, including developing AI risk registers and drafting policy components.
• Q&A Sessions: Dedicated time for participants to ask questions and receive tailored advice.
• Pre-Course Reading Materials: Comprehensive materials provided in advance to maximize learning during the sessions.
• Post-Course Resources: Access to templates, checklists, and recommended readings for ongoing reference.
• Blended Learning (Optional): Integration of online modules for foundational knowledge and in-person sessions for practical application and networking.

Register as a group from 3 participants for a Discount

Send us an email: info@datastatresearch.org or call +254724527104 

 

Certification

Upon successful completion of this training, participants will be issued with a globally- recognized certificate.

Tailor-Made Course

 We also offer tailor-made courses based on your needs.

Key Notes

a. The participant must be conversant with English.

b. Upon completion of training the participant will be issued with an Authorized Training Certificate

c. Course duration is flexible and the contents can be modified to fit any number of days.

d. The course fee includes facilitation training materials, 2 coffee breaks, buffet lunch and A Certificate upon successful completion of Training.

e. One-year post-training support Consultation and Coaching provided after the course.

f. Payment should be done at least a week before commence of the training, to DATASTAT CONSULTANCY LTD account, as indicated in the invoice so as to enable us prepare better for you.