AI Audit Standards, Algorithmic Logic & Assurance Practices: 3.5 CPD Self-Paced Course (SP0605)
Move beyond auditing financial outcomes alone and learn how to provide assurance over the AI systems, data and decision-making processes shaping modern organisations.
This practical, self-paced course gives auditors, accountants, audit managers, finance and assurance professionals the knowledge to assess AI governance, algorithmic logic, data lineage, model risks and AI-enabled controls while applying professional judgment, evidence-based evaluation and human accountability.
✔ Understand how AI is changing audit and assurance practices
✔ Evaluate AI systems, data lineage, controls and algorithmic decision processes
✔ Assess AI risks including bias, model limitations and generative AI outputs
✔ Apply professional judgment to provide transparent, evidence-based AI assurancePrepare for the future of assurance - develop the skills to assess AI-enabled systems, evaluate emerging risks, and apply audit judgment to evolving technology environments.
Table of Contents
- Expanding Audit Assurance Over Algorithmic Systems and Models
- Who Is This CPD Course For and Why Take It?
- Key Competencies & Strategic Outcomes
- Course Curriculum & On-Demand Learning Modules
- Meet the Trainer
- FAQs – Frequently Asked Questions
- Fees & Registration Details
Expanding Audit Assurance Over Algorithmic Systems and Models
Artificial intelligence is changing the scope of audit and assurance. As organisations increasingly rely on AI-enabled systems for analysis, decision-making and operational processes, auditors must expand their focus beyond financial outcomes to understand the data, models, controls and governance structures that influence AI-driven decisions.
As part of our specialised Self-Paced AI & Digital Assurance Series, AI Audit Standards, Algorithmic Logic & Assurance Practices (SP0605) explores how audit and assurance practices are adapting to an AI-enabled environment and what these developments mean for the evolving role of the auditor.
While foundational legal requirements are set in EU AI Act Compliance & Governance for Finance Professionals (SP0601) and human-in-the-loop controls are covered in EU AI Act Human Oversight, Bias & Explainability (SP0602), this module dives deep into technical verification, model performance testing, and algorithmic logic.
Designed for auditors, accountants, audit managers, finance, risk and assurance professionals, this course examines how professionals can assess AI systems and review data lineage. Building directly upon the execution techniques taught in AI in Audit Lifecycle, Anomaly Detection & NLP Tools (SP0603), you will explore emerging assurance approaches including algorithm assurance, model risk assessment, and safeguards for generative AI outputs.
Throughout the course, the emphasis remains on professional judgment, transparency and evidence. AI can expand the auditor’s toolkit, but it does not replace the need for human review, challenge, accountability and assurance over the integrity of AI-supported decision-making.
Who Is This CPD Course For and Why Take It?
AI is expanding the responsibilities of auditors and assurance professionals. Organisations increasingly need confidence not only in financial information, but also in the AI systems, data processes and controls that influence business decisions. This course helps professionals understand how assurance practices are evolving and how auditors can respond to the challenges created by AI-enabled environments.
By taking this course, you will:
Understand the Future of AI Assurance
Explore how audit and assurance practices are adapting as organisations increasingly use artificial intelligence in operational and decision-making processes.
Expand the Auditor’s Role Beyond Financial Statements
Understand why auditors may increasingly need to assess the integrity, reliability and governance of AI-enabled systems and processes.
Assess AI Systems and Algorithmic Logic
Learn how auditors can evaluate AI models, decision pathways and system logic while applying appropriate professional skepticism.
Evaluate Data Lineage and AI Transparency
Understand the importance of tracing data sources, transformations and inputs that influence AI-generated outputs and decisions.
Identify AI Risks and Control Considerations
Explore how AI risks such as bias, inaccurate outputs, weak governance and insufficient controls can be identified and addressed.
Strengthen AI-Related Audit Procedures
Learn how AI governance, model controls, monitoring processes and AI-specific risks can be incorporated into audit planning and execution.
Prepare for Emerging Assurance Opportunities
Gain insight into developing areas such as external AI assurance, AI governance reviews and multidisciplinary approaches to assessing AI systems.
Looking for structured live training on secure professional workflows?
If you prefer interactive guidance on implementing secure administrative and compliance practices, join our live-online AI Tools for Accountants & Lawyers Course (H1066) (10 CPDs, HRDA subsidized).
Key Competencies & Strategic Outcomes
By the end of this course, you will be able to evaluate key assurance considerations associated with AI-enabled systems, assess emerging AI risks and apply professional judgment when auditing technology-driven decision processes.
Describe how AI is expanding the auditor’s responsibilities beyond traditional financial information review toward assessing technology, data and decision-making processes.
Recognise areas where organisations may require assurance over AI systems, including governance, controls, data quality, model performance and transparency.
Evaluate how governance frameworks, policies and controls support reliable, accountable and responsible AI deployment.
Understand how auditors can examine the origin, movement and quality of data used by AI systems and assess whether inputs are appropriate and reliable.
Explore approaches for assessing whether AI systems operate consistently, accurately and according to their intended purpose.
Identify how bias, incomplete data or inappropriate assumptions can influence AI outputs and create assurance concerns.
Evaluate AI-flagged anomalies and system outputs critically, considering context, evidence and whether further investigation is required.
Understand risks associated with generative AI outputs and consider controls such as review procedures, validation processes and fallback approaches.
Recognise when AI assessments may require collaboration between auditors, technology specialists, data professionals and other experts.
Maintain a focus on evidence, transparency and professional accountability when evaluating AI-enabled processes and decisions.
Course Curriculum & On-Demand Learning Modules
Begin your learning journey with an introduction to Evolving Audit Standards and Assurance Practices and an overview of how the course is structured. This section will help you navigate the Moodle learning environment, understand the course requirements and resources, and prepare to get the most from your self-paced professional learning experience.
Explore how audit and assurance practices are evolving in response to artificial intelligence through an interactive SCORM-based learning experience designed for auditors, accountants and assurance professionals. Through AI-enabled audit scenarios and practical examples, you will examine AI governance, algorithmic logic, data lineage, model performance, bias considerations, AI controls, generative AI risks, resilience, fallback procedures and emerging assurance approaches. A downloadable PDF is also provided to support note-taking, reflection and future reference throughout the course.
Test your understanding of AI assurance, AI governance, algorithmic risk, data lineage, AI controls and evolving audit practices through the final online assessment. Achieve a score of 70% or higher to successfully pass the course and obtain your certificate of completion.
Your feedback helps support the continuous improvement of our professional learning programmes. This optional section gives you the opportunity to share your experience of the course, its content and learning activities, helping Centre 8 Education and Research Organisation continue developing relevant, practical and high-quality education for accounting and finance professionals.
Meet the Trainer
Fees & Registration Details
FAQs – Frequently Asked Questions
AI assurance refers to the process of evaluating whether artificial intelligence systems operate reliably, transparently and according to defined objectives, controls and governance requirements. AI assurance considers areas such as data quality, model performance, risk management, transparency, controls and the reliability of AI-generated outputs.
AI audit standards refer to the evaluation criteria, control frameworks, and testing methodologies used by auditors to assess the design, implementation, and operational effectiveness of artificial intelligence systems. Because specific localized AI standards are still emerging, practitioners rely on established frameworks like ISAE 3000 and guidance updates from the International Auditing and Assurance Standards Board (IAASB).
ISAE 3000 (International Standard on Assurance Engagements 3000) provides the overarching international standard for performing assurance engagements on non-financial information. It is widely used by independent practitioners to issue formal Type 1 or Type 2 assurance reports on enterprise AI governance, data privacy controls, and algorithmic risk management systems.
Auditors maintain professional skepticism by avoiding uncritical reliance on vendor performance claims or “black-box” outputs. They independently challenge underlying assumptions, verify training data quality, and require transparent rationale for automated decisions. To explore balancing technical capabilities against compliance skepticism, read Autonomous AI in Finance Risk Governance: Separating Capability from Hype.
An AI audit is an assessment of an artificial intelligence system’s governance, controls, data, performance and risks. It may examine whether an AI system is appropriately designed, monitored, documented and operating consistently with its intended purpose.
Auditors can assess AI systems by examining areas such as governance frameworks, data quality, data lineage, model documentation, controls, monitoring processes, output reliability and human oversight arrangements. The specific approach depends on the AI system’s purpose, complexity and risk profile.
Algorithm assurance involves evaluating whether algorithms operate as intended, produce reliable outputs and are supported by appropriate controls and governance. It may include reviewing logic, testing performance, assessing limitations and considering risks such as bias or unexpected behaviour.
Data lineage helps auditors understand where AI system data originates, how it is transformed and how it influences outputs. Understanding data lineage is important because unreliable, incomplete or inappropriate data can affect the quality and reliability of AI-generated results.
Auditors can consider whether AI systems produce consistent and appropriate outputs, whether training and operational data is suitable, whether bias risks have been identified and whether governance processes exist to monitor and address potential issues.
AI controls can be considered throughout the audit lifecycle, including planning, risk assessment, testing, evaluation of governance processes and reporting. Auditors may assess areas such as access controls, monitoring, documentation, validation procedures and human review processes.
External AI assurance involves an independent assessment of an organisation’s AI systems, governance and controls. Similar to other assurance activities, it aims to provide stakeholders with confidence that AI systems are appropriately managed, reliable and aligned with relevant requirements.
AI is unlikely to replace auditors but will change how audit and assurance work is performed. AI can support analysis and identify patterns at scale, but auditors remain responsible for applying professional judgment, evaluating evidence, challenging results and providing accountable assurance conclusions.