AI Model Risk Management and Algorithmic Audit Course

Introduction

Artificial intelligence and machine learning systems are embedded in critical decision-making processes across finance, healthcare, insurance, cybersecurity, human resources, and public sector governance. While these technologies offer significant efficiency and predictive capabilities, they also introduce complex risks related to bias, transparency, accountability, model drift, and unintended consequences. The AI Model Risk Management and Algorithmic Audit Course is designed to equip professionals with advanced skills to assess, govern, and audit AI systems to ensure they are reliable, explainable, and aligned with ethical and regulatory standards.

As organizations deploy increasingly complex algorithms to automate decisions, concerns around model fairness, data integrity, explainability, and performance stability have become central to governance frameworks. This course provides a structured approach to identifying, assessing, and mitigating risks associated with AI models throughout their lifecycle. Participants will learn how to evaluate model development processes, validate outputs, and ensure that algorithmic systems operate within acceptable risk thresholds while maintaining compliance with evolving regulatory expectations.

The course emphasizes the importance of model risk governance frameworks that integrate auditability, transparency, and accountability into AI-driven systems. Participants will gain practical knowledge of model validation techniques, stress testing methodologies, performance monitoring frameworks, and governance controls required to manage AI systems effectively. Real-world case studies will demonstrate how algorithmic failures can lead to financial losses, reputational damage, regulatory penalties, and systemic risk exposure.

With the rapid adoption of generative AI, large language models, predictive analytics, and automated decision engines, organizations face new challenges in understanding how algorithms make decisions and how those decisions impact stakeholders. This course explores emerging risks such as AI hallucination, adversarial attacks, data poisoning, model drift, and automated bias amplification. Participants will gain insights into how to design robust oversight mechanisms that ensure AI systems remain trustworthy and secure.

Regulators and global financial authorities are increasingly focusing on model governance, algorithmic accountability, and AI transparency requirements. This course examines global standards and regulatory frameworks that govern AI risk management, including model validation requirements, audit expectations, and ethical AI guidelines. Participants will learn how to align AI systems with governance principles, compliance obligations, and enterprise risk management frameworks to ensure responsible deployment of intelligent systems.

The AI Model Risk Management and Algorithmic Audit Course combines advanced data science principles, risk management frameworks, auditing methodologies, and governance best practices to prepare professionals for the future of AI oversight. Participants will develop practical skills in model validation, algorithmic auditing, risk assessment, and AI governance strategy development. The course ultimately enables organizations to deploy AI systems confidently while minimizing risk exposure and maximizing transparency, fairness, and reliability.

Who Should Attend

• Model Risk Managers
• Data Scientists and Machine Learning Engineers
• AI Governance Officers
• Internal and External Auditors
• Risk Management Professionals
• Compliance and Regulatory Officers
• Financial Analysts and Quantitative Analysts
• Cybersecurity and Data Security Professionals
• AI Product Managers
• Software Engineers working with AI systems
• Banking and Fintech Risk Officers
• Insurance Risk and Actuarial Professionals
• ESG and Ethics Officers
• Legal and Regulatory Advisors
• Technology Risk Consultants
• Digital Transformation Leaders

Duration

10 Days

Course Objectives

• Develop advanced understanding of AI model risk management frameworks and algorithmic audit principles for modern intelligent systems.
• Strengthen participants’ ability to identify, assess, and mitigate risks associated with machine learning and AI-driven decision-making systems.
• Equip professionals with practical skills in model validation, performance testing, and algorithmic behavior analysis.
• Enhance capabilities in detecting bias, fairness issues, and ethical risks in AI systems across different use cases.
• Build expertise in implementing model governance frameworks that ensure transparency, accountability, and regulatory compliance.
• Improve understanding of model lifecycle management including development, deployment, monitoring, and retirement stages.
• Strengthen competencies in auditing AI systems for robustness, reliability, and resilience against adversarial threats.
• Equip learners with techniques for evaluating data quality, training datasets, and feature engineering risks.
• Enhance knowledge of global regulatory frameworks governing AI governance and algorithmic accountability.
• Develop strategic skills for continuous monitoring of AI systems to detect model drift and performance degradation.
• Strengthen leadership capabilities in establishing AI governance structures and cross-functional oversight mechanisms.
• Build expertise in communicating AI risk findings to stakeholders, regulators, and executive leadership effectively.

Comprehensive Course Outline

Module 1: Foundations of AI Model Risk Management

• Introduction to AI model risk concepts and principles
• Types of AI models and associated risk categories
• Model lifecycle and risk exposure points
• Importance of governance in AI systems

Module 2: AI Governance and Regulatory Frameworks

• Global AI governance standards and guidelines
• Regulatory expectations for algorithmic accountability
• Ethical AI principles and compliance requirements
• Industry-specific AI governance frameworks

Module 3: Model Development Risk

• Risks in data selection and preprocessing
• Feature engineering and model design vulnerabilities
• Training process risks and model overfitting
• Documentation and transparency in model development

Module 4: Data Risk and Integrity

• Data quality and data bias issues
• Data poisoning and adversarial data risks
• Data lineage and traceability frameworks
• Data governance for AI systems

Module 5: Model Validation Techniques

• Model validation methodologies and best practices
• Back-testing and out-of-sample testing approaches
• Stress testing AI models under extreme conditions
• Independent model validation frameworks

Module 6: Algorithmic Bias and Fairness

• Identifying and measuring bias in AI systems
• Fairness metrics and mitigation techniques
• Ethical implications of algorithmic decision-making
• Regulatory expectations for fairness compliance

Module 7: Model Performance Monitoring

• Continuous monitoring of AI model performance
• Key performance indicators for AI systems
• Model drift detection and correction strategies
• Real-time analytics and monitoring tools

Module 8: Explainability and Transparency

• Interpretable AI and explainability techniques
• Model transparency requirements for compliance
• SHAP, LIME, and other explainability tools
• Communicating model outputs to stakeholders

Module 9: Adversarial AI Risks

• Adversarial attacks and system vulnerabilities
• Model manipulation and security risks
• Defense mechanisms against adversarial threats
• AI system robustness and resilience testing

Module 10: Model Governance Frameworks

• Designing enterprise AI governance structures
• Roles and responsibilities in model governance
• Policy development for AI risk management
• Governance reporting and oversight mechanisms

Module 11: Algorithmic Audit Methodologies

• Principles of algorithmic auditing
• Audit planning and execution for AI systems
• Evidence collection and documentation standards
• Reporting algorithmic audit findings

Module 12: AI Risk in Financial Services

• Credit scoring and lending model risks
• Fraud detection algorithm risks
• Trading and investment algorithm oversight
• Regulatory compliance in financial AI systems

Module 13: AI in Cybersecurity and Fraud Detection

• AI-based cybersecurity risk analysis
• Fraud detection model risks and limitations
• Threat intelligence and AI integration
• Monitoring AI-driven security systems

Module 14: Machine Learning Operations (MLOps) Risk

• Deployment risks in MLOps pipelines
• Version control and model update risks
• Automation risks in AI deployment systems
• Governance in continuous deployment environments

Module 15: Emerging AI Risks and Technologies

• Generative AI and large language model risks
• Deepfake technology and synthetic media risks
• Quantum computing implications for AI security
• Future AI governance challenges

Module 16: Strategic AI Risk Leadership

• Building AI risk-aware organizational culture
• Executive oversight of AI systems
• Strategic integration of AI governance
• Future trends in AI model risk management

Training Approach

The instructor led trainings are delivered using a blended learning approach and comprises of presentations, guided sessions of practical exercise, web-based tutorials and group work. Our facilitators are seasoned industry experts with years of experience, working as professional and trainers in these fields.

All facilitation and course materials will be offered in English. The participants should be reasonably proficient in English.

Certification

Upon successful completion of the training, participants will be awarded a certificate of completion by Steady Development Center.

Training Venue

The training will be held online. We also offer training for a group at requested location all over the world. The course fee covers the course tuition, tutorials and all required training manuals. Any other personal expenses are catered by the participant.
For registration and further enquiries, contact us on:

• Tel: +254 701 180 097
• Email: training@steadytrainingcenter.com

Tailor-Made Option

This course can be customized to suit the specific needs of your organization and be delivered on-line to any convenient location.

Terms Of Payment

Upon agreement by both parties’ payment should be made to Steady Development Center’s official account at least 3 working days before training begins to facilitate adequate preparation.

Our Upcoming Training Schedule