Understanding how to govern AI deployment and use
~27% of the exam · 3 competencies
Governance on the receiving end: deciding whether and how to deploy, assessing systems and vendors, and governing use, monitoring and downstream harms over time.
Use-case context, the differences between model types, and the trade-offs between deployment options — cloud, on-premises or edge; as-is or adapted through fine-tuning, RAG or agentic patterns.
Lessons are coming as they’re published.
Impact assessment of the system to be deployed, vendor and licensing risk, and the particular risks of deploying a proprietary in-house model.
Lessons are coming as they’re published.
Applying policy and ethics in operation: monitoring and retraining, periodic assessment, incident and post-market documentation, downstream-harm reduction, external communication and deactivation controls.
Lessons are coming as they’re published.