AI Audit and Assurance Training Course

AI Audit and Assurance Training Course

Introduction

AI Audit and Assurance Training Course is designed to equip professionals with advanced knowledge and practical skills required to evaluate, monitor, and assure Artificial Intelligence (AI) systems across modern organizations. As enterprises rapidly adopt Generative AI, Machine Learning, AI Governance frameworks, automated decision systems, and intelligent business platforms, the demand for qualified AI auditors who can assess AI risks, algorithmic accountability, data integrity, model performance, cybersecurity controls, and regulatory compliance is increasing globally. This course explores the emerging discipline of AI assurance, combining traditional audit principles with innovative approaches for responsible AI, trustworthy AI, explainable AI (XAI), ethical AI, and AI risk management.

Participants will learn how to design and execute comprehensive AI audit programs, evaluate AI lifecycle controls, conduct algorithmic assessments, validate AI models, and provide independent assurance on AI-enabled operations. Through practical frameworks, industry case studies, and real-world audit scenarios, learners will develop the capability to identify AI vulnerabilities, measure governance maturity, assess compliance with evolving AI regulations, and support organizations in achieving transparent, secure, fair, and accountable AI adoption.

Course Duration

5 days

Course Objectives

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

1. Understand the principles of AI Audit, AI Assurance, and Responsible AI Governance. 
2. Develop effective AI audit frameworks and risk-based audit methodologies. 
3. Evaluate AI lifecycle controls from data collection to model deployment. 
4. Assess algorithmic transparency, explainability, and accountability mechanisms. 
5. Perform AI risk assessments and control evaluations. 
6. Apply international AI governance standards and regulatory requirements. 
7. Conduct audits of Machine Learning and Generative AI systems. 
8. Evaluate data quality, privacy, security, and ethical AI practices. 
9. Identify and mitigate AI bias, fairness risks, and discrimination issues. 
10. Design AI assurance reports and audit documentation. 
11. Implement continuous monitoring for AI performance and compliance. 
12. Strengthen organizational AI maturity and governance capabilities. 
13. Support enterprise adoption of trustworthy, secure, and compliant AI solutions. 

Target Audience

1. Internal auditors and audit managers 
2. IT auditors and cybersecurity professionals 
3. Risk management and compliance officers 
4. AI governance and ethics professionals 
5. Data scientists and machine learning engineers 
6. Information security specialists 
7. Business leaders implementing AI solutions 
8. Regulatory, legal, and technology assurance professionals 

Course Modules

Module 1: Foundations of AI Audit and Assurance

• Introduction to AI auditing concepts and principles 
• Evolution from traditional IT audit to AI assurance 
• AI lifecycle and audit checkpoints 
• Roles of AI auditors in digital transformation 
• Building an AI audit strategy 
• Case Study: Auditing an organization's transition from manual decision-making to AI-powered customer analytics.

Module 2: AI Governance Frameworks and Standards

• Principles of effective AI governance 
• AI accountability and oversight structures 
• Global AI governance frameworks 
• Responsible AI operating models 
• Aligning AI governance with enterprise risk management 
• Case Study: Evaluating AI governance maturity within a multinational organization deploying Generative AI tools.

Module 3: AI Risk Assessment and Control Evaluation

• Identifying AI operational and strategic risks 
• AI risk identification methodologies 
• Control design for AI systems 
• Risk scoring and mitigation strategies 
• Continuous AI risk monitoring 
• Case Study: Assessing risks in an AI-based credit scoring system used by a financial institution.

Module 4: Auditing AI Data Management and Quality

• Data governance principles for AI systems 
• Data accuracy, completeness, and reliability testing 
• Privacy and data protection considerations 
• Training data validation techniques 
• Managing data bias and data drift 
• Case Study: Auditing healthcare AI data pipelines to ensure reliable patient outcome predictions.

Module 5: Algorithmic Auditing and Model Assurance

• Machine learning model evaluation methods 
• Model accuracy and performance testing 
• Explainable AI (XAI) assessment 
• Algorithmic fairness and bias testing 
• Model validation and documentation 
• Case Study: Reviewing an AI recruitment model for fairness, transparency, and compliance.

Module 6: Generative AI Audit and Assurance

• Auditing Large Language Models (LLMs) 
• Generative AI risk management 
• Prompt security and AI misuse risks 
• Evaluating AI-generated content reliability 
• Governance of enterprise AI assistants 
• Case Study: Conducting assurance testing for an organization's internal Generative AI chatbot.

Module 7: AI Cybersecurity, Privacy, and Compliance Auditing

• AI security risk assessment 
• Adversarial attacks and AI vulnerabilities 
• Privacy impact assessments for AI 
• Regulatory compliance auditing 
• Protecting AI systems throughout their lifecycle 
• Case Study: Auditing cybersecurity controls for an AI-powered banking fraud detection platform.

Module 8: AI Audit Reporting and Continuous Assurance

• Designing AI audit programs and procedures 
• Developing AI assurance reports 
• Communicating findings to stakeholders 
• Continuous AI monitoring frameworks 
• Building future-ready AI audit capabilities 
• Case Study: Creating an AI assurance roadmap for a government organization implementing automated services.

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.