Nowadays mobile users are constantly being connected and increasingly asked to express their personal preferences in the digital world. User preferences deal with simple device settings options, like notification alarms, as well as ethical relevant choices relating to the user behavior, privacy ones included (e.g., concerning the unauthorized disclosure and mining of personal data, as well as the access to restricted resources). All these preferences define the user, they are the building blocks of her digital identity and will be increasingly important given the growing rise of autonomous technologies and their ethical implications. The settings that enable these preferences are often hard to locate and hard to understand, even in popular apps and operating systems. Moreover, they can expose privacy, be employed for profiling or exploited for malicious activities. In this landscape, we devise the introduction of a \textit{Personal Preferences Automation Module} (PPAM) capable of automatically inferring, applying and enforcing user choices in multiple scenarios ranging from speeding up simple time consuming tasks to the management of ethical sensitive choices. The wide range of sensors and devices that can be found in mobile domain makes it a privileged context in which to be able to implement the system we are describing. In this paper, we present two application scenarios and describe the proposed approach at work on it.