Course Description
Equip your teams with practical, responsible data governance practices for AI initiatives. This micro-course shows how to trace where data comes from, respect user rights and consent, minimise sensitive information, and handle cross-border data risks so AI projects stay compliant, ethical, and resilient.
Who Should Take This Course
Ideal for business leaders, project managers, data owners, analysts, compliance and risk professionals, and technical stakeholders who influence how data is collected, shared, and used in AI systems. Perfect for organisations upskilling staff to align AI work with policy, regulation, and customer trust.
Prerequisites
No prior experience needed. Helpful but optional: basic familiarity with data lifecycle concepts and your organisation’s privacy or security policies.
What You Will Learn
- How to document data provenance and lineage to make AI decisions auditable.
- Practical consent management approaches and how to operationalise data subject rights.
- When and how to use synthetic data, plus tactics for effective data minimisation.
- How data sovereignty affects storage and processing, with guardrails for cross-border transfers.
- Ways to embed privacy-by-design and governance controls into AI workflows and vendor contracts.
- A clear action plan and checklist to improve your team’s AI data governance maturity.