AI Standards and Compliance Frameworks Training Course

AI Standards and Compliance Frameworks Training Course

Introduction

AI Standards and Compliance Frameworks Training Course provides a comprehensive understanding of the rapidly evolving landscape of Artificial Intelligence (AI) governance, regulatory compliance, international AI standards, risk management, and responsible AI implementation. As organizations increasingly adopt Generative AI, Machine Learning (ML), automated decision systems, and AI-powered business solutions, the need for structured AI compliance frameworks, governance models, ethical AI practices, and standardized controls has become critical. This course equips professionals with practical knowledge of global AI standards, regulatory requirements, compliance assessment techniques, and implementation strategies aligned with emerging frameworks such as ISO AI standards, NIST AI Risk Management Framework (AI RMF), OECD AI Principles, EU AI Act requirements, and responsible AI governance models.

Designed for technology leaders, compliance professionals, policymakers, auditors, and business stakeholders, this training enables participants to build effective AI governance programs, AI assurance processes, compliance monitoring systems, and organizational AI policies. Through real-world case studies, interactive exercises, and industry best practices, learners will develop the capability to assess AI risks, establish compliance controls, manage AI lifecycle governance, and ensure trustworthy, transparent, secure, and accountable AI adoption across organizations.

Course Duration

5 days

Course Objectives

By the end of this course, participants will be able to:

1. Understand the foundations of AI standards, governance frameworks, and regulatory compliance models. 
2. Analyze global AI regulations, policies, and emerging compliance requirements. 
3. Implement AI governance frameworks aligned with organizational objectives and industry standards. 
4. Apply ISO AI management standards for responsible AI development and deployment. 
5. Utilize the NIST AI Risk Management Framework (AI RMF) for AI risk assessment and mitigation. 
6. Develop AI compliance strategies supporting ethical, transparent, and accountable AI systems. 
7. Conduct AI maturity assessments and identify compliance gaps. 
8. Establish AI lifecycle controls covering design, development, deployment, and monitoring. 
9. Create effective AI audit, assurance, and compliance monitoring programs. 
10. Evaluate AI systems against privacy, security, fairness, and explainability requirements. 
11. Design AI policies and procedures supporting responsible innovation. 
12. Manage AI vendor governance and third-party AI compliance risks. 
13. Build future-ready AI compliance capabilities aligned with evolving global standards. 

Target Audience

1. Chief Information Officers (CIOs) and Technology Executives 
2. AI Governance and Responsible AI Professionals 
3. Compliance Officers and Risk Managers 
4. Data Protection and Privacy Professionals 
5. Internal Auditors and AI Assurance Teams 
6. Software Engineers and AI Developers 
7. Legal Professionals and Regulatory Advisors 
8. Government Policymakers and Digital Transformation Leaders 

Course Modules

Module 1: Foundations of AI Standards and Compliance Frameworks

• Introduction to AI governance, standards, and compliance ecosystems 
• Evolution of global AI regulatory landscapes 
• Importance of AI standardization for trustworthy AI adoption 
• Key principles of responsible AI governance 
• Roles and responsibilities in AI compliance management 
• Case Study: Global Technology Company AI Governance Model

Module 2: Global AI Regulations and Policy Frameworks

• Overview of international AI regulatory approaches 
• Understanding the EU AI Act and risk-based AI regulation 
• OECD AI Principles and global responsible AI practices 
• Government AI strategies and policy development 
• Preparing organizations for future AI regulatory changes 
• Case Study: European AI Act Compliance Preparation

Module 3: ISO AI Standards and Management Systems

• Introduction to ISO AI-related standards 
• AI management system requirements 
• Implementing structured AI governance processes 
• AI quality management and operational controls 
• Certification readiness and compliance documentation 
• Case Study: AI Management System Implementation

Module 4: NIST AI Risk Management Framework (AI RMF)

• Understanding AI risk management principles 
• AI RMF functions: Govern, Map, Measure, and Manage 
• Identifying AI risks throughout the lifecycle 
• Developing AI risk mitigation strategies 
• Integrating AI RMF into enterprise governance 
• Case Study: Financial Services AI Risk Program

Module 5: AI Compliance Risk Assessment and Auditing

• AI compliance assessment methodologies 
• Developing AI audit frameworks 
• Measuring AI governance maturity 
• Identifying compliance gaps and vulnerabilities 
• Creating AI assurance reports and improvement plans 
• Case Study: Healthcare AI Compliance Audit 

Module 6: Responsible AI Controls and Ethical Compliance

• Implementing fairness and bias management controls 
• Ensuring AI transparency and explainability 
• Establishing accountability mechanisms 
• Managing AI privacy and security obligations 
• Creating responsible AI operating models 
• Case Study: AI Hiring Platform Compliance Review 

Module 7: AI Vendor Governance and Third-Party Compliance

• Managing external AI solution providers 
• AI supplier risk assessment frameworks 
• Contractual AI compliance requirements 
• Evaluating AI vendor security and governance practices 
• Monitoring third-party AI lifecycle risks 
• Case Study: Enterprise Generative AI Vendor Assessment

Module 8: Building Enterprise AI Compliance Programs

• Designing AI governance operating models 
• Creating AI policies, standards, and procedures 
• Establishing compliance monitoring mechanisms 
• Developing AI governance roadmaps 
• Preparing organizations for future AI innovations 
• Case Study: Enterprise AI Transformation Program 

Training Methodology

• Interactive lectures and presentations.
• Group discussions and brainstorming sessions.
• Hands-on exercises using real-world datasets.
• Role-playing and scenario-based simulations.
• Analysis of case studies to bridge theory and practice.
• Peer-to-peer learning and networking.
• Expert-led Q&A sessions.
• Continuous feedback and personalized guidance.

Register as a group from 3 participants for a Discount

Send us an email: info@datastatresearch.org or call +254724527104 

Certification

Upon successful completion of this training, participants will be issued with a globally- recognized certificate.

Tailor-Made Course

 We also offer tailor-made courses based on your needs.

Key Notes

a. The participant must be conversant with English.

b. Upon completion of training the participant will be issued with an Authorized Training Certificate

c. Course duration is flexible and the contents can be modified to fit any number of days.

d. The course fee includes facilitation training materials, 2 coffee breaks, buffet lunch and A Certificate upon successful completion of Training.

e. One-year post-training support Consultation and Coaching provided after the course.

f. Payment should be done at least a week before commence of the training, to DATASTAT CONSULTANCY LTD account, as indicated in the invoice so as to enable us prepare better for you.