The advent of IoT and cloud services has resulted in collecting and sharing massive amounts of data. From a security perspective, these data represents a valuable target for attackers. As data-driven processes become integrated in the fabric of business, the entire society is becoming increasingly vulnerable to threats to data reliability and availability. Finally, the increase in redundancy of data available for collection, analysis and dissemination have strained traditional rules to protect privacy and confidentiality.The Big Data threat landscape continues to evolve. Opportunistic one-shot attacks have been supplemented by leakages that are more persistent and, in many cases, far more worrisome. This means that we need to start designing Big Data systems not just to prevent attacks and recover from them, but also to detect successful attackers quickly and contain them so that any data leakage can be identified and countered. This talk starts by introducing the emerging Big Data Threat Landscape with reference to some vertical domains and performs a Protection Gap Analysis to list some known vulnerabilities. Then a paradigm of Detect, Contain and Recover is introduced as a practical foundation for managing risks connected to Big Data.
Big Data: Threat Landscape and Protection Gap Analysis
