A method in a computing device for providing secure hyper-personalization, comprising: in a secured virtual container executing on the computing device and isolated from an operating system executing on the computing device: storing feature data comprising personal user data, the personal user data comprising collected ambient light readings; selecting a first set of features from the stored feature data and providing it to a machine learning engine included in the secured virtual container; generating, by the machine learning engine comprising a suitably trained machine learning model, first inference values for a first inference category based at least in part on the first set of features,; and notifying availability of the first inference values corresponding to the first inference category to a broker external to the secured virtual container, wherein the broker is configured to securely interface with the secured virtual container; and in the broker in the computing device: receiving the first inference values from the secured virtual container; and providing, in response to a query for the availability of the first inference values from at least one running process in the operating system, the first inference values to the at least one running process in the operating system, wherein the at least one running process is configured to perform a personalization operation based at least in part on the first inference values, the at least one running process in the operating system being configured to alter display output characteristics to reduce blue light output based on the time of day, inference values that indicate a high probability that the user environment currently has reduced ambient lighting, and other inference values that indicate a high probability that the user has a configuration preference for, or a habit of setting, a low blue light display setting in low ambient lighting at night. in a secured virtual container executing on the computing device and isolated from an operating system executing on the computing device: storing feature data comprising personal user data, the personal user data comprising collected ambient light readings; selecting a first set of features from the stored feature data and providing it to a machine learning engine included in the secured virtual container; generating, by the machine learning engine comprising a suitably trained machine learning model, first inference values for a first inference category based at least in part on the first set of features,; and notifying availability of the first inference values corresponding to the first inference category to a broker external to the secured virtual container, wherein the broker is configured to securely interface with the secured virtual container; and storing feature data comprising personal user data, the personal user data comprising collected ambient light readings; selecting a first set of features from the stored feature data and providing it to a machine learning engine included in the secured virtual container; generating, by the machine learning engine comprising a suitably trained machine learning model, first inference values for a first inference category based at least in part on the first set of features,; and notifying availability of the first inference values corresponding to the first inference category to a broker external to the secured virtual container, wherein the broker is configured to securely interface with the secured virtual container; and in the broker in the computing device: receiving the first inference values from the secured virtual container; and providing, in response to a query for the availability of the first inference values from at least one running process in the operating system, the first inference values to the at least one running process in the operating system, wherein the at least one running process is configured to perform a personalization operation based at least in part on the first inference values, receiving the first inference values from the secured virtual container; and providing, in response to a query for the availability of the first inference values from at least one running process in the operating system, the first inference values to the at least one running process in the operating system, wherein the at least one running process is configured to perform a personalization operation based at least in part on the first inference values, the at least one running process in the operating system being configured to alter display output characteristics to reduce blue light output based on the time of day, inference values that indicate a high probability that the user environment currently has reduced ambient lighting, and other inference values that indicate a high probability that the user has a configuration preference for, or a habit of setting, a low blue light display setting in low ambient lighting at night.