What Can You Achieve with This Training?
Develop the ability to independently assess AI system governance, reliability, and control environments.
Systematically evaluate AI risks, algorithmic bias, data integrity, transparency, and security vulnerabilities.
Prepare risk-based AI audit plans by defining audit objectives, scope, resources, and work programmes.
Assess model cards, system maps, data quality, fairness, and model performance through structured audit evidence.
Strengthen your skills in reporting audit findings, tracking corrective actions, and verifying remediation.
Prepare for the PECB Certified AI Auditor certification examination while developing professional capabilities in AI auditing.
The Certified AI Auditor Training provides professionals with the knowledge and practical skills required to assess, audit, and govern artificial intelligence systems through a structured and risk-based approach.
As organisations increasingly integrate AI into their operations, independent assessment of these technologies becomes essential for addressing concerns relating to reliability, accountability, ethical use, data integrity, security, and regulatory compliance.
This course prepares participants to contribute to independent assurance over AI systems, evaluating whether appropriate governance arrangements, risk management practices, and controls are in place.
The programme begins with the fundamentals of AI Assurance, introducing core AI concepts, the AI system lifecycle, organisational AI strategy, decision-making governance, and accountability structures.
Participants then examine AI risk assessment, control environment evaluation, lifecycle controls, and risk-based audit planning. They learn how to define audit objectives and scope, develop an AI Risk and Control Matrix, allocate resources, and prepare structured audit work programmes.
The practical elements focus on gathering and evaluating audit evidence, testing AI controls, assessing model governance, and preparing audit reports.
The course also considers AI-specific assurance areas such as bias, fairness, transparency, data quality, model performance, robustness, traceability, and ongoing monitoring.
The final stage addresses communicating audit findings, recommending corrective actions, monitoring remediation, and verifying improvements through follow-up activities.
Throughout the training, knowledge checks, practical exercises, and quizzes reinforce key concepts and support preparation for the PECB certification examination.
Exams and Certification
Participants complete a formal certification examination administered by PECB after the course.
