Artificial Intelligence Governance and Algorithmic Auditing Training Course

Course Introduction

Artificial intelligence is rapidly transforming how organisations make decisions, deliver services, manage risks, analyse information and automate operational activities. As AI systems become embedded in business and public-sector processes, organisations require effective governance, accountability and assurance mechanisms to ensure that algorithms operate responsibly, transparently and consistently with strategic, legal and ethical expectations.

This course provides a comprehensive framework for understanding how internal auditors, risk professionals, compliance specialists and technology assurance teams can evaluate AI governance and algorithmic controls. It examines the lifecycle of AI systems, from business justification and data preparation through model development, deployment, monitoring, modification and retirement, with particular attention to governance responsibilities and audit evidence.

Participants will explore how to assess algorithmic risks involving bias, discrimination, explainability, transparency, privacy, security, data quality, model performance and inappropriate automation. The programme also addresses the governance of generative AI, machine learning applications, automated decision-making and emerging AI technologies, helping auditors understand how traditional assurance principles can be adapted to increasingly complex technology environments.

The training emphasises practical audit methodologies for evaluating AI policies, governance structures, accountability arrangements, risk assessments, model inventories, data controls, documentation, validation processes and ongoing monitoring. Participants will learn how to develop risk-based audit procedures that provide meaningful assurance without treating AI models as isolated technical artefacts disconnected from business processes, people, controls and organisational objectives.

Particular attention is given to the rapidly evolving regulatory and professional environment surrounding artificial intelligence. Participants will consider how emerging AI governance expectations affect audit scope, control design, documentation, accountability and reporting, while learning how to distinguish regulatory obligations from voluntary frameworks, technical standards and recognised good practices.

By completing this programme, participants will be better prepared to evaluate whether AI-enabled processes are appropriately governed, controlled, monitored and aligned with organisational objectives. The course combines governance principles, algorithmic auditing techniques, risk assessment, data analytics, model assurance, ethical considerations and emerging technology issues to strengthen practical AI assurance capabilities.

Duration

10 days

Who Should Attend

• Internal auditors responsible for reviewing artificial intelligence, automated decision-making and technology-enabled business processes.

• IT auditors and information systems assurance professionals assessing AI platforms, applications, models and supporting technology controls.

• Risk managers developing enterprise-level approaches for identifying and managing artificial intelligence and algorithmic risks.

• Compliance and regulatory professionals responsible for monitoring AI-related obligations, policies, controls and governance requirements.

• Data governance professionals overseeing data quality, data lineage, information usage and accountability for AI-enabled processes.

• Cybersecurity professionals assessing security risks associated with AI systems, models, datasets, interfaces and technology infrastructure.

• Model risk management professionals involved in model validation, performance monitoring, documentation and independent assurance.

• Legal, privacy and ethics professionals addressing algorithmic accountability, responsible AI and automated decision-making risks.

• Senior managers and executives responsible for AI strategy, technology governance, digital transformation and organisational risk oversight.

• Audit committee members and governance professionals seeking to understand AI-related assurance, accountability and oversight requirements.

• Fraud, forensic and investigation professionals examining AI-enabled fraud, manipulation, synthetic content and algorithmic abuse.

• Quality assurance professionals responsible for testing the reliability, consistency and effectiveness of AI-enabled processes.

• Data scientists, machine learning professionals and AI developers who need to understand governance, control and audit expectations.

• Professionals responsible for implementing responsible AI programmes, AI governance frameworks and algorithmic risk management processes.

Course Objectives

• Develop a comprehensive understanding of artificial intelligence governance principles and how they apply to organisational strategy, risk management, accountability and assurance.

• Evaluate AI governance structures, policies, committees, roles and responsibilities to determine whether accountability for AI-related decisions is clearly established.

• Identify and assess algorithmic risks arising from bias, discrimination, opacity, poor data quality, model weaknesses, security vulnerabilities and inappropriate automation.

• Apply risk-based auditing techniques to AI systems throughout their lifecycle, including design, development, testing, deployment, monitoring, modification and retirement.

• Assess the effectiveness of AI policies, standards, procedures and control frameworks in supporting responsible, secure, transparent and accountable artificial intelligence use.

• Examine data governance controls supporting AI models, including data quality, lineage, provenance, classification, access, privacy, integrity and appropriate usage requirements.

• Evaluate model development and validation processes to determine whether AI systems are appropriately tested, documented, independently challenged and monitored.

• Apply practical techniques for auditing algorithmic fairness, explainability, transparency and accountability while recognising the technical and contextual limitations of such assessments.

• Assess governance and control arrangements surrounding generative AI, large language models, automated content generation and enterprise use of emerging AI applications.

• Develop effective audit procedures, evidence requirements, findings and recommendations for artificial intelligence governance and algorithmic assurance engagements.

• Evaluate AI monitoring, incident management, change control and performance assurance mechanisms to identify emerging risks and control deterioration.

• Strengthen professional judgement and communication capabilities for reporting AI governance weaknesses and providing actionable assurance to senior management and governance bodies.

Comprehensive Course Outline

Module 1: Foundations of Artificial Intelligence Governance and Algorithmic Auditing

• Artificial intelligence concepts, applications, organisational use cases and the evolving role of algorithmic systems in decision-making.

• Core principles of AI governance, accountability, responsible AI, human oversight and organisational responsibility.

• Foundations of algorithmic auditing, assurance objectives, audit scope and differences between traditional technology audits and AI-focused engagements.

• Key AI risks, assurance challenges and practical considerations for auditors evaluating complex and continuously changing systems.

Module 2: AI Governance Frameworks, Policies and Accountability

• AI governance structures, oversight committees, executive responsibilities and accountability models for enterprise artificial intelligence.

• AI policies, standards, procedures and governance requirements covering acceptable, prohibited and controlled uses of AI.

• Roles and responsibilities of business owners, developers, data scientists, risk teams, compliance functions, auditors and senior management.

• AI governance maturity assessment, accountability mechanisms and integration with enterprise governance, risk and compliance frameworks.

Module 3: AI Risk Assessment and Algorithmic Audit Planning

• Identification of AI risks across business processes, technology platforms, data environments and automated decision-making activities.

• Risk-based AI audit planning, engagement scoping, materiality considerations and prioritisation of high-impact algorithmic systems.

• AI risk registers, risk taxonomies, control mapping and assessment of inherent and residual algorithmic risk.

• Developing AI audit objectives, criteria, procedures, evidence requirements and testing strategies for complex technology environments.

Module 4: AI Lifecycle Governance and Model Development Controls

• Governance controls across AI conception, design, development, testing, deployment, operation, modification and retirement.

• Model development documentation, requirements management, assumptions, design decisions and traceability of significant changes.

• Development environment controls, version management, code governance and segregation of duties within AI development activities.

• Model approval, deployment authorisation and governance checkpoints supporting controlled introduction of AI into business processes.

Module 5: Data Governance, Quality and AI Training Data Controls

• Data quality dimensions, completeness, accuracy, consistency, relevance and representativeness in artificial intelligence environments.

• Data lineage, provenance, sourcing, transformation and documentation controls supporting trustworthy algorithmic outcomes.

• Training, testing and validation dataset governance, including sampling, partitioning, contamination risks and data integrity considerations.

• Data access, classification, retention, privacy and authorised-use controls for information incorporated into AI systems.

Module 6: Algorithmic Fairness, Bias and Discrimination Auditing

• Sources of algorithmic bias arising from datasets, design assumptions, proxy variables, implementation decisions and operational environments.

• Methods for identifying potential disparate impacts, discriminatory outcomes and unfair treatment within automated decision processes.

• Fairness assessment approaches, contextual limitations, threshold selection and interpretation of algorithmic fairness indicators.

• Governance responses to identified bias, including remediation, human review, escalation, documentation and ongoing monitoring requirements.

Module 7: Explainability, Transparency and Algorithmic Accountability

• Explainability and interpretability concepts for machine learning models and automated decisions affecting customers, employees and stakeholders.

• Transparency requirements for AI use, decision processes, data sources, model limitations and accountability responsibilities.

• Audit techniques for evaluating whether AI decisions can be appropriately understood, challenged and communicated to relevant stakeholders.

• Documentation, audit trails, model cards, decision records and other evidence supporting transparent and accountable AI governance.

Module 8: AI Model Validation, Performance and Reliability Assurance

• Model validation principles, independent review, challenge processes and controls for evaluating AI model suitability.

• Performance measurement, accuracy, robustness, stability, drift and deterioration risks across different operational environments.

• Scenario testing, stress testing, sensitivity analysis and validation procedures for assessing AI behaviour under changing conditions.

• Model limitations, uncertainty, confidence measures and governance processes for responding to unreliable or unexpected AI outputs.

Module 9: AI Security, Privacy and Technology Risk Auditing

• Security risks affecting AI models, datasets, development environments, interfaces, infrastructure and operational technology platforms.

• Adversarial manipulation, prompt injection, data poisoning, model exploitation and other emerging AI-enabled security vulnerabilities.

• Privacy risks associated with personal information, sensitive data, model inputs, outputs, retention and secondary use of AI-generated information.

• Security and privacy controls for AI environments, including access management, monitoring, encryption, logging and incident response.

Module 10: Generative AI, Large Language Models and Emerging AI Risks

• Governance considerations for generative AI, large language models, automated content generation and enterprise AI assistants.

• Hallucinations, unreliable outputs, prompt risks, confidential information exposure and inappropriate reliance on generative AI systems.

• Generative AI acceptable-use controls, human oversight, output validation, content provenance and responsible deployment practices.

• Auditing rapidly evolving generative AI environments where model behaviour, vendors, applications and risks may change continuously.

Module 11: AI Third-Party, Vendor and Supply Chain Assurance

• AI vendor risk assessment covering technology providers, model developers, cloud platforms, datasets, APIs and external AI services.

• Contractual governance, service-level expectations, audit rights, security requirements and transparency obligations for third-party AI providers.

• Third-party model documentation, assurance reports, testing evidence and control representations used in AI assurance engagements.

• Fourth-party dependencies, concentration risks, technology supply chains and resilience considerations within outsourced AI ecosystems.

Module 12: AI Monitoring, Continuous Auditing and Algorithmic Analytics

• Continuous monitoring of AI performance, control effectiveness, model behaviour, exceptions, incidents and emerging algorithmic risks.

• AI audit analytics, automated testing, anomaly detection and data-driven techniques for identifying unusual model or process outcomes.

• Key risk indicators, performance indicators, alert thresholds and escalation mechanisms for ongoing AI governance assurance.

• Continuous auditing approaches for environments where AI models, datasets, configurations and business uses change frequently.

Module 13: AI Ethics, Human Oversight and Responsible Automation

• Ethical principles for artificial intelligence, including accountability, human dignity, transparency, proportionality and responsible innovation.

• Human-in-the-loop, human-on-the-loop and human-over-the-loop governance models for automated and semi-automated decisions.

• Automation risks, excessive reliance on algorithms, deskilling, decision displacement and inappropriate delegation of professional judgement.

• Ethical escalation, governance challenge mechanisms and assurance considerations for high-impact AI applications.

Module 14: AI Compliance, Regulatory Assurance and Governance Standards

• Regulatory and legal considerations affecting artificial intelligence governance, automated decision-making, privacy and accountability.

• Mapping organisational AI controls against applicable regulatory obligations, recognised standards, internal requirements and governance frameworks.

• AI documentation, compliance evidence, accountability records and audit trails supporting regulatory and governance assurance.

• Managing regulatory uncertainty, jurisdictional differences and evolving AI requirements while maintaining proportionate risk-based controls.

Module 15: AI Audit Evidence, Reporting and Remediation Management

• Gathering, evaluating and documenting reliable audit evidence from AI models, datasets, governance records, technical documentation and business processes.

• Developing AI audit findings that clearly explain control weaknesses, algorithmic risks, business implications and supporting evidence.

• Reporting AI assurance results to management, audit committees and governance bodies using clear, balanced and decision-useful communication.

• Remediation planning, management actions, follow-up procedures and validation of corrective measures for identified AI governance deficiencies.

Module 16: Advanced AI Assurance, Emerging Technologies and Future Audit Strategy

• Advanced assurance approaches for autonomous AI agents, multimodal systems, AI-enabled robotics and increasingly complex algorithmic ecosystems.

• Emerging risks involving synthetic data, autonomous decision-making, AI-generated content, model ecosystems and rapidly evolving foundation models.

• Building an AI assurance roadmap integrating governance, risk, compliance, cybersecurity, privacy, data governance and internal audit activities.

• Future-ready AI auditing strategies, assurance innovation, continuous learning and preparation for increasingly autonomous technology environments.

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.