AI curriculum
Course outline for AI Governance
AI adoption needs decision ownership and evidence, not policy statements alone. Develop a governance record that connects risk, controls, oversight, and review responsibilities.
About AI Governance
AI adoption needs decision ownership and evidence, not policy statements alone. Develop a governance record that connects risk, controls, oversight, and review responsibilities.
AI Governance Course Objectives
- Classify risks for a sample AI use case.
- Define policy controls and accountable owners.
- Specify evidence and review requirements.
Pre-requisites
- Familiarity with organizational decision processes.
- Basic awareness of AI use cases; coding is not required.
Lab Setup
- Computer with a document editor or spreadsheet.
- Use a fictional organization and synthetic governance records; no model or compliance service account is required.
Detailed Course Outline
Proposed modulesModule 1: Governance design
- Governance frameworks
- Risk classification
- Policies
- Human oversight
Practical outcome: Draft a use-case risk record and oversight policy.
Module 2: Accountability and evidence
- Accountability
- Transparency
- Auditability
- Evidence
Practical outcome: Assign owners and define reviewable control evidence.
Practical exercise
- Create a governance dossier for a sample AI pilot and review unresolved control gaps.
How we train
Contact us for full course details, including duration, delivery options and lab requirements.
Ask about exercises, instructor feedback and the prior knowledge you need. Tool-specific courses marked provisional may become modules in a broader course.
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