NEVADA | PROFESSIONAL EXPERIENCE

Program Professional Framework

AI Governance Professional (AIGP)

Program Overview

As organizations deploy machine learning and generative AI in customer services, credit decisions and operations, they need professionals who can govern these systems across their full lifecycle. This program covers the foundations of AI and its risks, the main legal and policy frameworks including the EU AI Act, the NIST AI Risk Management Framework, ISO/IEC 42001 and the OECD AI Principles, and their interaction with data protection laws such as the Saudi PDPL and GDPR. It then addresses governance of AI development and deployment: impact assessments, documentation, testing for bias and robustness, vendor oversight, monitoring and incident handling, with reference to the SDAIA AI Ethics Principles and other Gulf guidance.

Learning Outcomes

✓Classify AI use cases by risk level using the EU AI Act categories and internal criteria.
✓Conduct an AI impact assessment covering fairness, privacy, safety, transparency and accountability.
✓Map organizational AI governance controls to the NIST AI RMF functions of govern, map, measure and manage.
✓Define documentation requirements such as model cards, data sheets and decision logs for an AI system.
✓Design post-deployment monitoring and an AI incident response process with clear roles.
✓Evaluate third-party AI vendors against contractual, data protection and transparency requirements.

Target Audience

AI governance, risk and compliance officers
Data protection officers and privacy counsel
Data science and AI product leads responsible for deployment
Internal auditors reviewing AI and algorithmic systems

Detailed Program Modules

01Foundations of AI and AI Risk

Machine learning, deep learning and generative AI basics
Sources of harm: bias, opacity, error and misuse
Principles of trustworthy and responsible AI

02Laws, Standards and Frameworks

EU AI Act risk tiers and obligations
NIST AI RMF, ISO/IEC 42001 and OECD AI Principles
SDAIA AI Ethics Principles and Gulf data protection laws

03Governing AI Development

Data governance, provenance and quality
Impact assessments and risk classification
Testing for bias, robustness and explainability

04Governing AI Deployment

Human oversight and decision accountability
Transparency notices and user information
Third-party and vendor AI due diligence

05Monitoring and Incident Management

Model drift and performance monitoring
AI incident reporting and escalation
Audits and periodic reviews of AI systems

06Building the AI Governance Program

Policies, inventory and roles
Governance committees and approval gates
Training, culture and continuous improvement

Training Methodology

Interactive expert-led sessions grounded in workplace examples

Case studies and practical exercises drawn from the region

Group workshops with peer review of decisions and deliverables

Formative feedback against clear performance criteria

Applied Project & Assessment

Applied Project

Participants complete a governance file for one real or proposed AI use case: risk classification, impact assessment, control mapping to NIST AI RMF, documentation checklist, monitoring plan and a recommendation to an AI governance committee.

Assessment

Short diagnostic at the start, practical tasks linked to each learning outcome, and evaluation of the applied project against published criteria.

University Certificate & International Accreditation

Professional Certificate accredited by Twintech International University College of Technology in AI Governance Professional (AIGP)

International accreditation: American Association of Innovation (AAI)