AI Governance Professional Certification Preparatory Course
Prepare for the AI Governance Professional (AIGP) certification by developing practical knowledge of AI governance, responsible AI principles, AI-related laws and regulations, standards and frameworks, AI risk management, AI development, deployment and ongoing governance.
Course Fee
S$1500
Course Information
Course Overview
The AI Governance Professional (AIGP) certification provides the knowledge and skills required to demonstrate competency in AI governance. The course covers AI concepts, responsible AI principles, risks, responsibilities, laws, frameworks, standards, and governance throughout the AI lifecycle. It focuses on the foundations of AI governance, how laws and standards apply to AI, governance of AI development, and responsible AI deployment and use.
AIGP Certification Focus
The AI Governance Professional certification focuses on building a strong foundation in AI concepts, governance principles, benefits, risks, responsibilities, laws, standards and frameworks. It also addresses the governance of emerging AI applications, including generative AI and agentic AI, and provides practical knowledge for responsible AI adoption, development, deployment and ongoing oversight.
Course Objectives
- Understand AI governance and responsible AI principles
- Understand AI laws, privacy and intellectual property
- Understand key AI governance standards and frameworks
- Govern AI design, development, testing and monitoring
- Evaluate responsible AI deployment and associated risks
Prerequisites
- Knowledge of AI governance fundamentals
- Understanding of AI concepts and risks
- Awareness of AI governance responsibilities
- Familiarity with AI laws and standards
- Understanding of AI development and deployment
- Awareness of responsible AI principles
Course Outline
Domain I: Understanding the Foundations of AI Governance
A: AI Foundations and Governance
- Understand AI definitions and types.
- Identify AI risks, harms and impacts.
- Understand AI characteristics requiring governance.
- Apply common responsible AI principles.
- Understand fairness, safety, privacy and accountability.
B: Organizational AI Governance Expectations
- Define AI governance roles and responsibilities.
- Establish cross-functional AI governance collaboration.
- Develop stakeholder training and awareness programs.
- Align AI governance with organizational objectives.
- Differentiate AI developers, providers and users.
C: AI Lifecycle Policies and Procedures
- Establish AI lifecycle oversight and accountability.
- Perform use case assessment and risk management.
- Apply ethics by design principles.
- Govern data, development, training and testing.
- Manage deployment, incidents, documentation and third-party risks.
Domain II: Understanding How Laws, Standards and Frameworks Apply to AI
A: AI Data Privacy Laws
- Understand privacy requirements applicable to AI.
- Apply data minimization and privacy-by-design.
- Understand controllers, processors and data rights.
- Understand cross-border data transfer requirements.
- Manage privacy incidents and breach notifications.
B: Other Existing Laws and AI
- Understand intellectual property laws affecting AI.
- Understand AI training data restrictions.
- Understand nondiscrimination laws affecting AI.
- Understand consumer protection laws affecting AI.
- Understand product liability considerations for AI.
C: AI-Specific Laws and Requirements
- Understand AI risk classification frameworks.
- Understand prohibited and high-risk categories.
- Understand AI risk management requirements.
- Understand transparency and human oversight requirements.
- Understand enforcement, penalties and compliance obligations.
D: AI Standards, Frameworks and Tools
- Understand OECD trustworthy AI principles.
- Understand NIST AI Risk Management Framework.
- Understand NIST AI RMF core functions.
- Understand ISO AI governance standards.
- Apply frameworks supporting trustworthy AI governance.
Domain III: Understanding How to Govern AI Development
A: AI System Design and Building
- Define AI business context and use.
- Perform AI impact assessments and reviews.
- Apply ethical AI design practices.
- Evaluate models, data and operational controls.
- Manage development risks and testing.
B: AI Training and Testing Data
- Establish AI data governance requirements.
- Assess lawful data collection and usage.
- Evaluate data quality, integrity and provenance.
- Govern AI training and testing activities.
- Manage training risks and documentation.
C: AI Release, Monitoring and Maintenance
- Assess readiness for production release.
- Establish continuous AI system monitoring.
- Schedule maintenance, updates and retraining.
- Conduct audits, security and threat testing.
- Manage incidents, documentation and transparency.
Domain IV: Understanding How to Govern AI Deployment and Use
A: AI Deployment Factors and Risks
- Evaluate AI use case context.
- Consider business, ethical and workforce factors.
- Compare AI models and capabilities.
- Evaluate cloud, on-premise and edge deployment.
- Understand fine-tuning, RAG and agentic architectures.
B: AI System Assessment Activities
- Perform AI system impact assessments.
- Evaluate vendor and licensing agreements.
- Identify vendor-related AI risks.
- Assess proprietary AI model risks.
- Evaluate obligations and potential liabilities.
C: AI Deployment and Use Governance
- Apply responsible AI deployment practices.
- Apply data and risk management.
- Provide user training and monitoring.
- Manage incidents, audits and downstream harms.
- Establish controls for system deactivation.
Course Outcomes
- Understand the foundations of AI governance and the principles of responsible AI.
- Understand the application of laws, standards and frameworks to AI governance.
- Govern AI system design, development, data management, training, testing, release, monitoring and maintenance.
- Evaluate AI systems, risks, models, deployment options, vendor agreements and impact assessments.
- Govern responsible AI deployment and use through risk management, monitoring, incident management and appropriate controls.
Key Benefits of AI Governance Professional Training
The AI Governance Professional curriculum provides a broad foundation for professionals responsible for adopting or managing AI. It develops knowledge of AI governance principles, risks, responsibilities, laws, standards, frameworks, AI development, deployment, monitoring and ongoing governance.
How AI Governance Supports Responsible AI
AI governance provides the foundation for effective oversight of AI systems. By applying governance principles across the AI lifecycle, organizations can identify and manage risks, establish accountability, support responsible AI deployment and promote trustworthy AI adoption and innovation.
What You'll Learn
Facilities & Equipment
Virtual Training
- Electronic materials
- IT support for software & hardware
- Administrative support
Face-to-Face Training
- Air-conditioned classroom
- Meals & refreshments provided
- Projector & smart board
- Stationery provided
What Learners Say
Real experiences, real results
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