The Threat of Offensive AI to Organizations

AI has provided us with the ability to automate tasks, extract information from vast amounts of data, and synthesize media that is nearly indistinguishable from the real thing. However, positive tools can also be used for negative purposes. In particular, cyber adversaries can use AI to enhance their attacks and expand their campaigns. Although offensive AI has been discussed in the past, there is a need to analyze and understand the threat in the context of organizations. For example, how does an AI-capable adversary impact the cyber kill chain? Does AI benefit the attacker more than the defender? What are the most significant AI threats facing organizations today and what will be their impact on the future? In this study, we explore the threat of offensive AI on organizations. First, we present the background and discuss how AI changes the adversary’s methods, strategies, goals, and overall attack model. Then, through a literature review, we identify 32 offensive AI capabilities which adversaries can use to enhance their attacks. Finally, through a panel survey spanning industry, government and academia, we rank the AI threats and provide insights on the adversaries

Cardiopoietic cell therapy for advanced ischemic heart failure: results at 39 weeks of the prospective, randomized, double blind, sham-controlled CHART-1 clinical trial

Cardiopoietic cells, produced through cardiogenic conditioning of patients' mesenchymal stem cells, have shown preliminary efficacy. The Congestive Heart Failure Cardiopoietic Regenerative Therapy (CHART-1) trial aimed to validate cardiopoiesis-based biotherapy in a larger heart failure cohort

Setting up a Company Performance Measurement Methodology for the Aerospace Industry: Deduction from the Automotive Industry

Theories on value creation, co-innovation and co-development and lean enterprise have gained in popularity in recent times. This research has taken aim at extending the investigation on how to quantify companies’ capabilities in creating value for their stakeholders. A theoretical framework was adopted to build the performance measurement method on. This framework identifies five performance indicators of company performance: competition performance, financial performance, manufacturing capability, innovation capability and supply chain relationships. Due to the limited availability of data in aviation industry, use was made of data from the automotive industry. Data from 33 automotive OEMs was collected from which a set of variables was constructed. The behavior and relations of these variables were investigated and eventually five variables were selected, one for each performance indicator. Using multiple regression techniques weight factors were determined for each variable and a linear model was constructed, expressing a company performance index. This linear model allows assessing and comparing the performance of different companies over an arbitrary period of time. For the automotive OEMs this was qualitatively shown to work. The model was then adapted to fit the aerospace OEMs and the weight factors were recalculated. Unfortunately, due to the limited availability of data for aerospace OEMs, it was not possible to obtain great insights into the behavior and relations of the variables for these aerospace companies. Moreover, the weight factors of the linear model could not be determined with much accuracy. To solve this, it is recommended that for future research data collection continues and that in some years the research is redone with more data, allowing statistical analysis to be able to detect smaller effects.Aerospace Engineerin