AI Governance, Risk & Compliance Frameworks
Overview
As AI spreads across business functions, organisations need clear rules for how systems are approved, monitored and controlled. This course provides a complete governance toolkit: policies, roles, risk assessment methods, controls and assurance practices that keep AI safe, compliant and aligned with organisational values.
Who should attend
Risk, compliance, audit and legal professionals, data protection officers, and AI programme managers.
Training methodology
Facilitated discussion, practical frameworks, case studies from different sectors, group exercises, short demonstrations of AI tools, and an action plan for each participant's organisation.
Objectives
By the end of this course, participants will be able to:
- Design an AI governance framework and policy
- Define roles and accountabilities across the AI lifecycle
- Assess and classify AI risks
- Implement controls, documentation and monitoring
- Prepare for audits and regulatory reviews
Course outline
Day 1Foundations of AI Governance
- Why AI needs specific governance
- Lifecycle of an AI system
- Leading frameworks and standards
- Governance maturity check
- Governance principles and values
- Mapping AI in your organisation
Day 2Policies and Accountability
- AI policy structure
- Roles: owners, reviewers, approvers
- AI inventory and registers
- Drafting a policy outline
- AI committee terms of reference
- Approval workflows
Day 3Risk Assessment
- Risk categories and tiers
- Impact assessments
- Bias, robustness and transparency
- Assessment workshop
- Privacy and security risks
- Risk register for AI
Day 4Controls and Monitoring
- Pre-deployment testing
- Human oversight
- Performance and drift monitoring
- Third-party AI controls
- Model documentation and records
- Change and retirement of systems
Day 5Assurance and Compliance
- Internal audit of AI
- Regulatory expectations
- Incident management
- Governance implementation plan
- Reporting to the board
- Continuous improvement of governance
