AI Governance and Oversight for Government Entities Training Course

AI Governance and Oversight for Government Entities is a 5-day advanced course on setting up and running the governance of artificial intelligence inside a government entity, from roles and policies to risk assessment, model oversight and audit. It is designed for leaders of AI offices, oversight and compliance functions. You complete and take back the Entity AI Governance Playbook. The course is delivered by Mercury Training Center.

About This Course

Government entities now run AI systems across services and back offices, yet many cannot say who approves them, how risk is judged or who checks them after launch. To follow this course, you should already manage digital, data, risk or compliance work. You practice by drafting governance documents, assessing real AI use cases and reviewing audit evidence.

Who It Is For

• Leaders responsible for running an entity's AI office or AI adoption agenda
• Officers responsible for responsible AI, ethics review and compliance of AI systems
• Staff responsible for assessing AI systems before approval and monitoring them in operation
• Risk, audit and internal control teams responsible for assurance over digital and AI services
• Managers responsible for procuring AI solutions and holding vendors to account

This course is not for readers who want an introduction to what AI can do for public services, who are better served by a general public-sector AI course, nor for engineers who build and train models, who are better served by a technical machine learning course.

Competencies You Will Build

• Governance Structure Design: you define AI roles, committees, decision rights and escalation paths for your entity
• AI Risk Assessment: you classify AI systems by risk and impact and set the controls each level requires
• Lifecycle Oversight: you place approval, testing and monitoring checkpoints across the AI system lifecycle
• Vendor Accountability: you write AI requirements into procurement and hold suppliers to evidence of compliance
• AI Assurance and Audit: you plan and run reviews that test whether AI systems behave as approved
• Performance Reporting: you report AI outcomes, incidents and compliance status to senior leadership

What You Will Be Able to Do

By the end of the course you will be able to:

• From an entity's current AI activities, build an AI system inventory that records owner, purpose, data and risk level for each system
• Given a proposed AI system, complete a risk and impact assessment and decide the approval route and required safeguards
• Using ethics principles of fairness, transparency, privacy, safety and accountability, draft an AI use policy with clear do and do-not rules
• Given an AI vendor proposal, write procurement clauses covering data use, explainability, testing evidence and exit terms
• Using monitoring and incident data, prepare an oversight report that flags drift, complaints and corrective actions
• From an audit scope, design an AI audit plan with test procedures, evidence requests and a findings template

Course Content

Day 1: Foundations of Entity-Level AI Governance

• AI Governance Models and Their Fit for Government Entities
• AI Office Mandate, Committees and Decision Rights
• Responsible AI Officer and AI System Assessor Roles and Reporting Lines
• AI Ethics Principles Translated into Entity Policy Statements
• AI Governance Maturity Assessment for a Government Entity

Day 2: AI Inventory, Risk and Impact Assessment

• AI System Inventory and Register Design
• Risk Classification of AI Systems by Impact on Citizens and Services
• Algorithmic Impact Assessment for Public-Facing AI Systems
• Data Protection and Privacy Checks in AI Use Cases
• Approval Routes and Control Requirements by Risk Level

Day 3: Lifecycle Oversight and Procurement Controls

• AI System Lifecycle Checkpoints from Design to Retirement
• Pre-Deployment Testing for Accuracy, Bias and Robustness
• Human Oversight Design for AI-Supported Government Decisions
• AI Procurement Requirements, Vendor Due Diligence and Contract Clauses
• Third-Party AI and Generative AI Use Rules for Staff

Day 4: Monitoring, Audit and Reporting

• Operational Monitoring of Model Drift, Errors and Complaints
• AI Incident Management and Escalation Protocols
• AI Audit Planning, Test Procedures and Evidence Collection
• ISO/IEC 42001 AI Management System Structure as a Governance Reference
• AI Governance Dashboards and Reporting to Senior Leadership

Day 5: Applied AI Governance Practice

• Exercise Building an AI Inventory for a Service Directorate
• Exercise Running a Risk and Impact Assessment on a Proposed System
• Exercise Reviewing a Vendor Contract Against AI Requirements
• Exercise Preparing an Oversight Report from Monitoring Data
• Completing the Entity AI Governance Playbook

Case Studies and Exercises

The following are suggested activities used during the course.

• Case study: a social services agency using an AI tool to prioritize benefit reviews; you assess its impact on applicants and set the approval conditions.
• Case study: a transport authority procuring an AI traffic system; you identify gaps in the vendor contract and write the missing clauses.
• Exercise: a health ministry chatbot generating citizen complaints; you read the monitoring data and decide on corrective action and escalation.
• Exercise: a municipal licensing department with several unregistered AI tools; you build the inventory and rank the systems for audit.

What You Take Back

You return with the Entity AI Governance Playbook, shaped around your own entity. In your first month back, you use it to propose the governance roles, start the AI system inventory and run the first risk and impact assessment on a system already in use.

• Governance structure, roles and AI use policy
• AI system inventory and risk classification template
• Risk and impact assessment and procurement checklist
• Monitoring report and AI audit plan templates

Quick Answers (FAQ)

What should I know before AI governance and oversight training for government entities?

You should already manage digital, data, risk, audit or compliance work in a public body. No programming background is needed; the course works with governance documents, assessments and monitoring data rather than code.

How does AI governance and oversight training differ from general public-sector AI training?

General public-sector AI training explains what AI can do for services. This course assumes AI is already in use and focuses on governing it inside an entity: roles, risk assessment, procurement controls, monitoring and audit.

Who should own AI governance in a government entity?

Ownership usually sits with a senior leader supported by an AI office or committee, a named responsible AI officer and system owners in each unit. Risk and audit functions provide independent assurance over how systems are approved and run.

What do I take back from AI governance and oversight training?

You take back the Entity AI Governance Playbook, with a governance structure, AI use policy, system inventory, risk and impact assessment, procurement checklist, monitoring report and audit plan.

For Your Manager

After the course, the team member will be able to set up AI governance roles, assess the risk of AI systems, write AI controls into procurement and oversee systems in operation. They return with the Entity AI Governance Playbook, first used to start the entity's AI system inventory and assess one system already in use.