Everything so far, from attacks to controls to regulation, only works if someone owns it. This final module is about the organization around the technology: who decides which AI systems are allowed, how personal data is protected, and what happens when something goes wrong.
AI governance sets the structure: a steering committee, clear accountability, policies, and a way to sort AI use cases by risk. Privacy asks what personal data goes into training, prompts, and retrieval, and what comes back out, in line with laws such as GDPR. And incident response prepares the team for the day an AI system leaks data, behaves unsafely, or is attacked: how to detect it, contain it, communicate about it, and learn from it.
Watch the lessons in order. When you finish, move on to the Projects to apply what you have learned across the course.
An overview of what AI governance is, why it matters, and how an organization can build a framework for responsible AI.
Where an AI governance program should begin: a steering committee that owns accountability, and a way to tier AI use cases by risk.
How GDPR and data privacy requirements apply to AI systems, and why personal data needs particular care in AI projects.
How AI incidents differ from traditional security incidents, and the incident response lifecycle that auditors expect to see for AI systems.