Personalization and adaptation to the user profile capability are the hottest issues to ensure ambient assisted living and context awareness in nowadays environments. With the growing healthcare and wellbeing context aware applications, modeling security policies becomes an important issue in the design of future access control models. This requires rich semantics using ontology modeling for the management of services provided to dependant people. However, current access control models remain unsuitable due to lack of personalization, adaptability and smartness to the handicap situation. In this paper, we propose a novel adaptable access control model and its related architecture in which the security policy is based on the handicap situation analyzed from the monitoring of user's behavior in order to grant a service using any assistive device within intelligent environment. The design of our model is an ontology-learning and evolving security policy for predicting the future actions of dependent people. This is reached by reasoning about historical data, contextual data and user behavior according to the access rules that are used in the inference engine to provide the right service according to the user's needs.