The world has entered a digital era where people and machines leave digital footprints in websites, social media, cameras, sensor logs, and mobile devices. For example, manufacturing and operating systems collect streaming data through sensors and the Internet of Things (IoT); vehicles generate vast amounts of trajectory and sensor log data in aviation and surface transportation. Whether from natural, technological, or adversarial hazards, risks arise from various root causes, including human errors. Risk analysis has opportunities to integrate big data, natural language processing, computer vision, and machine learning methods in the digital era.This special issue features papers presented at the Conference on Risk Analysis, Decision Analysis and Security, Buffalo/Niagara Falls, NY, July 30-August 2, 2019 (organized by Drs. Jun Zhuang and Chen Wang). Collectively, the papers describe cutting-edge research on the possibilities of harnessing high-volume, high-dimensional, multisource, and multimodal data to give insights for risk assessment, communication, and management, as well as cover perspectives discussing the scope and limitations of big data risk analytics. The applications include food safety, cyber security, disaster mitigation, and recovery, misinformation and disinformation in social media, aviation safety, insurance fraud detection, health risk for emergency responders, autonomous driving, privacy risk management, and service failure prediction in transportation. The data utilized by these studies range from streaming data (e.g., from online social media, flight data recorders, and sensors of operating equipment), event data (e.g., fraud records of insurance, logs of stress leaves of emergency responders, and food safety incidents), to expert judgments. The methodologies cover a broad spectrum of analytic modeling, statistical inference, machine learning, expert elicitation, and combinations.Welburn and Strong propose an analytic framework to describe the systemic cyber risk resulting from cascading common cause or independent failures following a cyber incident. They apply the sector-level input-output analysis in economics to assess the aggregate losses associated with firm-level cyber incidents. Their model is validated using a cyber-attack case with known damages. The model can help determine cyber insurance premiums and make cybersecurity policies.Allodi et al. propose a model of a "work averse" attacker in a cybersecurity setting where the attacker is inclined to adopt existing toolkits if they can cause enough harm to the
