In this module, participants turn their focus to the critical foundation of all AI systems: data. While algorithms often draw attention, it is the underlying data that determines an AI system’s integrity, compliance, and data risk exposure. Boards and senior leaders must therefore treat data governance not as a technical function but as a strategic risk management domain central to ethical and defensible AI deployment.
By completing Module 3, participants will:
• understand why data, rather than algorithms, is one of the core sources of AI risks,
• identify hidden data vulnerabilities such as bias, consent gaps, provenance issues, and unauthorized use,
• build a comprehensive Data Provenance Framework that documents sources, quality, and defensibility,
• assess third-party and vendor AI risks systematically using a risk heatmap approach,
• implement robust, defensible data governance for AI aligned with GDPR and emerging global regulations and standards,
• prepare structured incident response procedures for potential data breaches and misuse involving AI.
To strengthen implementation, this module provides practical, customizable frameworks, templates and/or other materials that enable boards to operationalize sound data governance. Participants can apply tools covering:
• a data provenance documentation framework,
• a vendor and third-party AI risk heatmap,
• a governance checklist tailored for data in AI contexts,
• a self-assessment tool for identifying internal data risks,
• a structured incident response playbook for data-related AI events,
• a reflective worksheet to support continuous learning and governance maturity.
Next is your main slide deck:
Disclaimer
Next are your compendium of and individual frameworks, templates and other materials. You can customize per your organization’s contextual needs: