With the advancement of Artificial Intelligence (AI) technologies in the recent years, the business application of AI models has expanded significantly. Hence, auditors are progressively encountering AI systems, models and algorithms during audit and assurance projects. The growing scientific domains of eXplainable AI (XAI) and Responsible AI raise concerns around the transparency, explainability, and other ethicalities. These concerns, in combination with upcoming legislation, demand audit statements on reliability, integrity, and other aspects of AI models. Where auditing is well-established, AI auditing remains a novel practice. This research includes literature research, exploration of AI audit cases, and interviews with AI experts to discover relevant methods and specificalities of AI audits. Through the methodology of design science, a first structured AI Audit Process is developed and proposed to provide AI auditors with a flexible reference frame to conduct customised AI audits. This research is a step towards the advancement of an AI auditing method and offers valuable insights for science and practice.