Certificate in AI Governance
- Draft an organizational AI policy and acceptable-use standard covering staff use of generative AI tools.
- Build an AI system inventory with owners, purposes, data used and risk tier for each system.
- Design an approval and review workflow with gates for high-risk AI use cases.
- Define terms of reference and decision rights for an AI governance committee.
- Map required controls to the clauses of an ISO/IEC 42001 AI management system.
- Prepare a quarterly AI governance report for senior management with key risk indicators.
- Why Organizations Need AI Governance
- Shadow AI and uncontrolled adoption
- Regulatory and reputational drivers
- Responsible AI principles in the Gulf region
- Policy and Acceptable Use
- Structure of an AI policy
- Rules for staff use of generative AI
- Procurement clauses for AI products
- AI Inventory and Risk Tiering
- Discovering and registering AI systems
- Risk classification criteria
- Linking AI risk to enterprise risk registers
- Governance Structures and Approval Gates
- AI governance committee roles
- Review and approval workflow design
- Escalation and exception handling
- AI Management System Controls
- ISO/IEC 42001 structure and requirements
- Data protection and privacy controls
- Documentation, testing and monitoring evidence
- Assurance and Reporting
- Internal audit of AI governance
- AI governance KPIs and reporting
- Continuous improvement of the framework
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Program Professional Framework
Certificate in AI Governance
Program Overview
Many organizations now run dozens of AI tools, from vendor chatbots to in-house models, without a clear owner or approval process. This course focuses on building the internal governance operating model that brings them under control: an AI policy and acceptable-use rules, an inventory of AI systems, risk tiering and approval gates, roles for a governance committee, and controls aligned with an AI management system under ISO/IEC 42001. Participants learn how to embed AI risk into existing enterprise risk, data protection and procurement processes and how to report AI governance status to executives, drawing on SDAIA and UAE guidance on responsible AI.
Learning Outcomes
- ✓Draft an organizational AI policy and acceptable-use standard covering staff use of generative AI tools.
- ✓Build an AI system inventory with owners, purposes, data used and risk tier for each system.
- ✓Design an approval and review workflow with gates for high-risk AI use cases.
- ✓Define terms of reference and decision rights for an AI governance committee.
- ✓Map required controls to the clauses of an ISO/IEC 42001 AI management system.
- ✓Prepare a quarterly AI governance report for senior management with key risk indicators.
Target Audience
- Governance, risk and compliance managers
- Chief data officers and data governance leads
- IT governance and information security managers
- Legal and policy advisors responsible for technology
Detailed Program Modules
01Why Organizations Need AI Governance
- Shadow AI and uncontrolled adoption
- Regulatory and reputational drivers
- Responsible AI principles in the Gulf region
02Policy and Acceptable Use
- Structure of an AI policy
- Rules for staff use of generative AI
- Procurement clauses for AI products
03AI Inventory and Risk Tiering
- Discovering and registering AI systems
- Risk classification criteria
- Linking AI risk to enterprise risk registers
04Governance Structures and Approval Gates
- AI governance committee roles
- Review and approval workflow design
- Escalation and exception handling
05AI Management System Controls
- ISO/IEC 42001 structure and requirements
- Data protection and privacy controls
- Documentation, testing and monitoring evidence
06Assurance and Reporting
- Internal audit of AI governance
- AI governance KPIs and reporting
- Continuous improvement of the framework
Training Methodology
Applied Project & Assessment
Applied Project
Participants produce an AI governance starter kit for their organization: AI policy outline, inventory template populated with sample systems, risk tiering criteria, approval workflow, committee terms of reference and a first management report.
Assessment
Short diagnostic at the start, practical tasks linked to each learning outcome, and evaluation of the applied project against published criteria.


