  Integrating security activities into the software development lifecycle to
detect security flaws is essential for any project. These activities produce
reports that must be managed and looped back to project stakeholders like
developers to enable security improvements. This so-called Feedback Loop is a
crucial part of any project and is required by various industrial security
standards and models. However, the operation of this loop presents a variety of
challenges. These challenges range from ensuring that feedback data is of
sufficient quality over providing different stakeholders with the information
they need to the enormous effort to manage the reports. In this paper, we
propose a novel approach for treating findings from security activity reports
as belief in a Knowledge Base (KB). By utilizing continuous logical inferences,
we derive information necessary for practitioners and address existing
challenges in the industry. This approach is currently evaluated in industrial
DevOps projects, using data from continuous security testing.
