Explore the basics of artificial intelligence in the context of auditing, including its benefits, risks, and the need for independent evaluation.

Learn about international and industry-specific governance frameworks, compliance standards, and best practices for AI oversight.

Understand each phase of the AI lifecycle—development, deployment, and monitoring—and identify where auditors should intervene.

Gain hands-on techniques for evaluating the transparency, fairness, and explainability of machine learning models.

Explore into data integrity, sourcing, preprocessing, and data governance in AI systems from an auditing perspective.

Assess how AI systems generate outcomes and how to audit their logic, biases, and impact on users and business operations.

Conduct risk assessments and evaluate resilience against cyber threats, adversarial attacks, and performance degradation.

Examine ethical principles, bias mitigation, fairness in decision-making, and transparency reporting.

Learn how to document audit findings, recommend improvements, and integrate feedback loops for AI lifecycle enhancement.