The concept of risk as a measure for the potential of gaining or losing something of value has successfully been applied in software quality engineering for years, e.g., for risk-based test case prioritization, and in security engineering, e.g., for security requirements elicitation. In practice, both, in software quality engineering and in security engineering, risks are typically assessed manually, which tends to be subjective, non-deterministic, error-prone and time-consuming. This often leads to the situation that risks are not explicitly assessed at all and further prevents that the high potential of assessed risks to support decisions is exploited. However, in modern data-intensive environments, e.g., open online environments, continuous software development or IoT, the online, system or development environments continuously deliver data, which provides the possibility to now automatically assess and utilize software and security risks. In this paper we first discuss the concept of risk in software quality and security engineering. Then, we provide two current examples from software quality engineering and security engineering, where data-driven risk assessment is a key success factor, i.e., risk-based continuous software quality engineering in continuous software development and risk-based security data extraction and processing in the open online web.