The digitization of public administrations in Germany is making slow progress. At the same time, more and more innovative IT solutions are available on the market for solving practical business problems, e.g. web-based file sharing applications that are offered by external cloud service providers. Due to data protection regulations and uncertainties regarding information security issues, the adoption and operation of public cloud services within public administrations is a challenging task. As part of our research, we constructed a three-phase process model that uses a web-based tool approach, in order to support chief information officers to manage security audits of various public cloud services that are used by different organizational units. To ensure the efficient, transparent and comprehensive conduction of cloud security audits, we developed graphical visualization components that illustrate the information security maturity level in relation to multiple security requirements of the analyzed public cloud services. We have successfully evaluated our proposed tool visualization under real conditions within a public administration. Furthermore, we discussed several use cases and the user experience with different experts in this application domain.

Research studies have shown that especially enterprises in European countries are afraid of losing outsourced data or unauthorized access. Despite various existing cloud security mechanisms companies are currently hesitating to adopt cloud resources. This phenomenon is also known as cloud adoption dilemma. We think that data classification is a promising technique that should be considered in the context of cloud security, supporting cloud migration processes. By using classification techniques enterprises are able to control which documents are suited for Cloud Computing and which cloud service providers are sufficient for protecting sensitive documents. In this work we present an efficient concept that involves enterprises' employees and authorities, making it possible to apply powerful security policies in a simple way. We make use of a well-established machine learning algorithm in our developed tool, identifying security levels for different types of documents. Thus, cloud migration processes can become more transparent and enterprises obtain the ability to discuss more openly about adopting innovative cloud services.