Course Introduction
The Artificial Intelligence Risk and Control Assurance Course is a forward-looking professional program designed to equip participants with the skills required to evaluate, manage, and assure risks associated with AI systems. As organizations rapidly adopt artificial intelligence to drive innovation and efficiency, the need for robust governance, risk management, and control assurance frameworks has become critical.
This course provides a strong foundation in AI technologies, including machine learning, data-driven algorithms, and automated decision-making systems. Participants will learn how AI models are developed, deployed, and monitored, and how risks can emerge from data quality issues, model bias, lack of transparency, and system vulnerabilities.
A key focus of the program is AI risk assessment and control assurance, including evaluation of model governance, validation processes, ethical considerations, and compliance requirements. Learners will explore how auditors and risk professionals assess AI systems for fairness, accountability, reliability, and alignment with organizational objectives.
Participants will also gain practical knowledge in auditing AI systems, including model validation techniques, data integrity checks, algorithm transparency reviews, and performance monitoring. The training highlights how organizations can implement effective control frameworks to manage AI risks and ensure trustworthy outcomes.
The course further explores emerging issues such as explainable AI, generative AI risks, regulatory developments, AI ethics, and the impact of automation on governance structures. Learners will understand how evolving AI technologies introduce new challenges that require adaptive audit and assurance approaches.
By the end of the course, participants will be able to assess AI risks, evaluate control effectiveness, and conduct assurance reviews of AI systems. The program prepares professionals to strengthen governance, enhance transparency, and build trust in AI-driven decision-making processes.
Duration
5 days
Who Should Attend
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Internal auditors responsible for auditing AI systems and automated processes
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Risk management professionals assessing AI-related risks and control frameworks
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Data scientists and AI developers seeking to understand governance and assurance requirements
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Compliance officers overseeing regulatory requirements related to AI usage
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IT auditors evaluating advanced analytics and machine learning systems
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Cybersecurity professionals assessing AI-driven threats and vulnerabilities
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Governance professionals responsible for enterprise risk and control frameworks
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Business analysts working with AI-driven decision systems
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Technology consultants advising on AI governance and risk management
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Senior executives overseeing digital transformation and AI strategy
Course Objectives
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Equip participants with a comprehensive understanding of artificial intelligence risk management and control assurance frameworks to evaluate AI systems, identify risks, and ensure robust governance, transparency, and compliance across AI-driven operations
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Develop the ability to assess risks associated with machine learning models, data quality, and algorithmic bias
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Enable learners to conduct structured assurance reviews of AI systems and automated decision processes
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Strengthen skills in evaluating AI model governance, validation, and performance monitoring frameworks
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Train participants to assess ethical and regulatory risks related to AI deployment
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Build competency in identifying control weaknesses in AI systems and recommending improvements
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Enhance understanding of explainable AI and transparency requirements in algorithmic decision-making
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Prepare professionals to evaluate data governance frameworks supporting AI systems
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Enable participants to communicate AI risk and audit findings effectively to stakeholders
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Develop leadership capability in strengthening AI governance and control assurance frameworks
Comprehensive Course Outline
Module 1: Foundations of Artificial Intelligence and Risk
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Introduction to artificial intelligence concepts, machine learning models, and their role in transforming business operations and decision-making processes across industries
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Overview of AI risk categories including operational, ethical, and regulatory risks
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Understanding the lifecycle of AI systems from development to deployment
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Role of auditors in AI risk management and assurance
Module 2: AI Governance and Control Frameworks
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Evaluation of AI governance structures and policies within organizations
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Assessment of control frameworks for managing AI-related risks
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Identification of gaps in governance and oversight mechanisms
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Strengthening accountability and control assurance in AI systems
Module 3: Data Quality and Data Governance in AI
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Evaluation of data sources, quality, and integrity used in AI models
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Assessment of data governance frameworks supporting AI systems
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Identification of risks related to biased or incomplete datasets
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Strengthening data management practices for reliable AI outputs
Module 4: AI Model Risk and Validation Techniques
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Evaluation of AI model development methodologies and validation processes
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Assessment of model accuracy, reliability, and performance metrics
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Identification of model risks including overfitting and bias
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Strengthening validation and testing frameworks for AI systems
Module 5: Algorithm Transparency and Explainability
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Evaluation of explainable AI techniques and transparency requirements
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Assessment of algorithm interpretability in decision-making systems
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Identification of risks in opaque or black-box AI models
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Strengthening transparency and accountability in AI systems
Module 6: Ethical and Regulatory Considerations in AI
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Evaluation of ethical risks in AI including bias and discrimination
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Assessment of compliance with emerging AI regulations and standards
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Identification of governance gaps in ethical AI deployment
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Strengthening ethical frameworks and compliance controls
Module 7: AI Security and Cyber Risk Management
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Evaluation of cybersecurity risks affecting AI systems and data
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Assessment of adversarial attacks and AI system vulnerabilities
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Identification of risks in automated decision systems
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Strengthening security controls for AI infrastructure
Module 8: Generative AI and Emerging Technologies Risks
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Evaluation of risks associated with generative AI and advanced models
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Assessment of misuse, misinformation, and data leakage risks
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Identification of emerging threats in evolving AI technologies
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Strengthening governance for innovative AI applications
Module 9: AI Performance Monitoring and Continuous Assurance
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Evaluation of ongoing monitoring frameworks for AI system performance
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Assessment of drift detection and model recalibration processes
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Identification of performance degradation and operational risks
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Strengthening continuous assurance practices for AI systems
Module 10: AI Audit Simulation and Capstone Project
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End-to-end simulation of AI risk assessment and control assurance processes
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Practical evaluation of AI systems, data governance, and model performance
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Development of AI audit reports with findings and recommendations
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Presentation of AI assurance outcomes demonstrating applied expertise
Training Approach
This course will be delivered by our skilled trainers who have vast knowledge and experience as expert professionals in the fields. The course is taught in English and through a mix of theory, practical activities, group discussion and case studies. Course manuals and additional training materials will be provided to the participants upon completion of the training.
Tailor-Made Course
This course can also be tailor-made to meet organization requirement. For further inquiries, please contact us on: Email: training@upskilldevelopment.com Tel: +254 721 331 808
Training Venue
The training will be held at our Upskill Training Centre. We also offer training for a group (at a discount of 10% to 50%) at requested location all over the world. The Onsite course fee covers the course tuition, training materials, two break refreshments, buffet lunch, airport transfers, Upskill gift package, and guided tour.
Visa application, travel expenses, dinners, accommodation, insurance, and other personal expenses are catered by the participant
Certification
Participants will be issued with Upskill certificate upon completion of this course.
Airport Pickup and Accommodation
Airport pickup and accommodation is arranged upon request. For booking contact our Training Coordinator through Email: training@upskilldevelopment.com, +254 721 331 808
Terms of Payment:
Unless otherwise agreed between the two parties’ payment of the course fee should be done 3 working days before commencement of the training so as to enable us to prepare better.
