Audit of Artificial Intelligence and Automated Decision Systems Course

Course Introduction

Artificial intelligence and automated decision systems are transforming how organizations operate, analyze information, and deliver services. As these technologies rapidly evolve, they introduce unprecedented risks, complexities, and accountability challenges that demand highly specialized audit capabilities. The Audit of Artificial Intelligence and Automated Decision Systems Course provides professionals with the advanced skills needed to evaluate the reliability, fairness, transparency, and governance of AI-driven systems across diverse operational environments.

This course examines how AI models learn, make predictions, and influence high-stakes decisions, offering participants a deep understanding of the technical and ethical risks embedded within automated systems. Participants explore how algorithmic bias, data quality gaps, opaque model logic, and inadequate monitoring mechanisms can undermine organizational integrity and expose institutions to regulatory, reputational, and operational threats. Through a structured and practical curriculum, the course empowers auditors to assess AI systems with rigor and independence.

As organizations integrate machine learning, predictive analytics, and intelligent automation into critical workflows, the need for robust AI governance frameworks becomes essential. The course addresses the emerging international standards, assurance practices, and compliance requirements that guide responsible AI deployment. Participants learn to evaluate the alignment of AI systems with legal standards, internal policies, and industry best practices, ensuring that automation enhances rather than threatens organizational trust.

A core focus of the program is developing expertise in algorithmic transparency and explainability. Participants gain hands-on insights into documenting model logic, evaluating training data, reviewing system behavior, and assessing whether the outputs generated by AI systems are fair, reliable, and traceable. The course equips professionals with methodologies to interrogate model performance and identify hidden risks that traditional audit approaches may overlook.

Practical case studies simulate real-world AI audit scenarios, allowing participants to apply audit procedures to machine learning systems, automated decision engines, robotic process automation, and predictive scoring models. By practicing risk identification, control evaluation, evidence gathering, and reporting techniques, participants build confidence in executing high-quality AI assurance engagements that reflect modern technology realities.

By the end of this course, participants will be prepared to lead comprehensive audits of AI and automated systems, ensuring these technologies operate responsibly, transparently, and in accordance with regulatory and ethical expectations. They will have the expertise to safeguard organizations from unintended AI risks and support the deployment of trustworthy, accountable automated technologies.

Duration

10 days

Who should attend

• Internal auditors and audit managers
• External auditors and assurance professionals
• Risk management and compliance officers
• IT auditors and cybersecurity specialists
• AI governance and ethics professionals
• Data scientists and analytics managers involved in model oversight
• Digital transformation and innovation leaders
• Regulators and supervisory authorities
• Consultants in technology risk and assurance
• Professionals involved in algorithmic decision-making oversight
• Quality assurance and process control professionals
• Policy and governance advisors in tech-driven sectors

Course objectives

• Equip participants with in-depth understanding of AI model lifecycles to assess risk exposures from data sourcing, model training, validation, deployment, and monitoring.
• Strengthen the ability to apply structured audit methodologies tailored to AI and automated decision systems, ensuring effective evaluation of governance and controls.
• Develop expertise in identifying algorithmic bias, fairness concerns, and discriminatory outcomes, and applying audit techniques to assess model integrity.
• Enhance participants’ skills in evaluating data quality, lineage, and preprocessing steps that directly impact AI model accuracy, reliability, and compliance.
• Provide the ability to examine AI explainability mechanisms and determine whether model decisions can be justified, documented, and interpreted by auditors.
• Increase capacity to assess cybersecurity and resilience risks that affect AI systems, including adversarial attacks, data poisoning, and model manipulation.
• Build competence in reviewing AI governance structures, accountability frameworks, and oversight mechanisms implemented by organizations.
• Strengthen analytical capabilities to validate model performance metrics, detect drift, and assess long-term operational reliability of AI systems.
• Equip auditors with tools to evaluate automated decision workflows and confirm their alignment with ethical, regulatory, and policy expectations.
• Improve proficiency in documenting AI audit findings, communicating risks to stakeholders, and recommending corrective actions for responsible AI adoption.
• Develop strategies to assess compliance with emerging AI regulations, standards, and global guidelines that govern automated decision-making technologies.
• Prepare participants to lead comprehensive AI assurance engagements that safeguard organizational integrity and enhance trustworthy automation.

Course outline

Module 1: Foundations of AI and Automated Decision Systems

• Understanding core AI concepts, machine learning processes, and how automated systems support organizational decision-making
• Examining how training data, model architecture, and algorithms influence system behavior and risk exposure
• Identifying types of automated decision systems used across industries and their associated governance challenges
• Exploring cross-disciplinary roles involved in AI development, deployment, and assurance

Module 2: AI Risk Landscape and Risk Assessment Techniques

• Assessing inherent, control, and residual risks unique to AI-driven processes and decision frameworks
• Identifying vulnerabilities such as bias, poor data quality, opaque logic, and adversarial manipulation
• Evaluating the risk impact of automated decisions using qualitative and quantitative methods
• Integrating AI risks into enterprise-wide risk management and audit planning

Module 3: Governance, Accountability, and Regulatory Compliance

• Reviewing responsibilities of AI governance structures and stakeholders managing automated decision systems
• Understanding global AI regulations and compliance expectations shaping responsible AI use
• Assessing policies and procedures supporting ethical, fair, and transparent automation
• Evaluating organizational readiness for AI regulatory audits and supervisory examinations

Module 4: Data Governance and Data Quality Controls

• Examining data lineage, sourcing, and transformation processes that impact AI model reliability
• Identifying data quality weaknesses such as bias, imbalance, or incomplete datasets
• Assessing data protection, confidentiality, and privacy controls relevant to automated systems
• Evaluating controls supporting continuous data quality monitoring across the model lifecycle

Module 5: Model Development, Training, and Validation

• Reviewing model development documentation, design logic, and algorithm selection criteria
• Evaluating model training processes, assumptions, and development methodologies for accuracy and fairness
• Assessing validation practices and testing procedures used to ensure model robustness
• Identifying weaknesses in feature engineering, model tuning, and performance evaluation

Module 6: Explainability, Transparency, and Model Interpretability

• Understanding explainability techniques that provide insights into AI decision mechanisms
• Evaluating transparency tools and documentation that support auditability of automated systems
• Identifying risks associated with opaque “black box” models in high-stakes environments
• Reviewing interpretability frameworks and assessing their adequacy for regulatory compliance

Module 7: Algorithmic Bias and Fairness Assessment

• Understanding types of algorithmic bias and how they emerge from data, models, or system design
• Assessing fairness metrics and audit techniques to measure discriminatory outcomes
• Reviewing model outputs to identify disparate impacts on protected groups
• Evaluating mitigation strategies and model adjustments to support ethical automation

Module 8: System Security, Resilience, and Adversarial Risk

• Assessing security threats such as data poisoning, adversarial inputs, and model extraction
• Evaluating robustness of AI systems against cyberattacks and operational disruption
• Reviewing controls safeguarding model integrity and sensitive data
• Testing AI resilience and recovery measures for continuity of automated decisions

Module 9: Automated Decision Workflow and Control Evaluation

• Analyzing end-to-end automated workflows for accuracy, traceability, and override capabilities
• Identifying potential errors, inefficiencies, or unintended consequences within automated decision logic
• Evaluating human-in-the-loop controls managing automated processes and exceptions
• Reviewing alignment between automated decisions and organizational risk appetite

Module 10: Performance Monitoring, Model Drift, and Lifecycle Management

• Evaluating ongoing monitoring controls that track model accuracy, stability, and operational behavior
• Identifying signs of concept drift, performance degradation, or shifting data patterns
• Reviewing model retraining protocols and lifecycle management processes
• Assessing long-term oversight mechanisms supporting sustained model performance

Module 11: AI Ethics, Transparency, and Accountability Auditing

• Understanding ethical principles guiding responsible AI and automated decisions
• Evaluating transparency mechanisms that allow stakeholders to understand system behavior
• Assessing accountability frameworks defining roles, responsibilities, and decision ownership
• Integrating ethical considerations into audit conclusions and recommendations

Module 12: Documentation, Audit Evidence, and Assurance Reporting

• Gathering audit evidence that supports findings related to model performance and governance
• Reviewing documentation standards for AI audits and assurance engagements
• Writing audit reports that communicate risks, findings, and recommendations clearly
• Ensuring audit trails accurately reflect assessment of model behavior and controls

Module 13: RPA, Intelligent Automation, and System Controls

• Understanding risks associated with robotic process automation and intelligent workflow tools
• Evaluating control structures supporting automated processes and system dependencies
• Assessing operational effectiveness and reliability of automation technologies
• Identifying potential failure points and oversight weaknesses in automated workflows

Module 14: Cloud, Infrastructure, and Platform Risk

• Evaluating cloud-based AI environments and infrastructure supporting automated systems
• Identifying risks related to platform reliability, access controls, and system integration
• Assessing third-party provider risks and shared responsibility models
• Reviewing security, governance, and compliance expectations in cloud AI deployments

Module 15: Emerging AI Technologies and Future Risk Themes

• Exploring risks posed by generative AI, autonomous systems, and self-learning models
• Assessing implications of evolving regulatory frameworks and global AI policies
• Evaluating new audit methodologies suitable for next-generation AI systems
• Preparing organizations for future compliance and assurance challenges

Module 16: Practical AI Audit Case Studies and Simulation

• Applying audit procedures to real-world AI systems through guided case studies
• Conducting risk assessment, control testing, and model evaluation in simulated scenarios
• Preparing audit reports detailing findings, risks, and corrective recommendations
• Strengthening practical audit confidence through hands-on AI assurance exercises

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.