Digital technology is the main driver of the transformation process that is already on its way and expected to take up speed. Science and engineering are challenged to realize the significant innovation potential while keeping an eye on economic and societal sustainability. Research methodology in science as well as development practice in engineering provide well-established approaches to risk management and mitigation relating to this technological transformation. Artificial intelligence, though, brings in new features to address which this chapter shall help to deal with. As such we take into view machine learning, automated decision making and autonomous systems, and data utilization. We look upon characteristic risks within the application lifecycle, and on functional, societal, and cybersecurity risks. We derive suggestions for an approach to proactive risk management addressing the lifecycle of Artificial Intelligence applications. Along with a preparatory section on terminological clarification regarding artificial intelligence, data, and risk this paper is intended to build awareness of risk mitigation matters and set the scene for the development of accountable risk management approaches.