Data access control is a field that has been a subject of a lot of research for many years, which has resulted in many models being designed. Many of these models are deterministic in nature, following set rules to allow or deny access to a given user. These are sufficient in fairly static environments, but they fall short in dynamic and collaborative settings where permission needs may change or user attributes may be missing. risk-based and probabilistic models were designed to compensate. These take a user profile to determine the risk associated with a particular transaction or fill in any missing attributes. However, they need to be maintained as new security threats emerge. It is argued in this paper that cognitive systems can close this gap by learning security threats on their own, enhancing the security of data in these environments. The benefits and considerations to be made when deploying cognitive systems have also been discussed.
Conference: 2nd IoTBDS - Intl. Conf. on IoT, Big Data and SEcurity in Porto, Portugal