Introduction - Syllabus

Cybersecurity risk management is a necessary tool for decision making for all management levels from tactical to strategic and creating a common understanding between people from diverse domains or having different priorities. This course adopts a multidisciplinary perspective. It creates a common understanding of risk for a diverse set of students which are coming from different disciplines such as technical, social, economics, law, and politics to remove communication barriers between strategic, operational, and tactical level decision makers. The course covers related government and industry regulations and standards along with best practices frequently used to assess, analyze and manage cyber risks, along with the fundamental methods of risk management. Also, applications of cybersecurity risk management on emerging topics such as Internet of things and cloud systems are discussed along with traditional applications areas

Management of Extremes in the Configuration of Interoffice Telephone Switch & Priority Systems

This paper describes how to enable diverse enterprise customers for voice-data switch to achieve in configuration a balance among users, features, and perceived reliability subject to extremes of traffic. The analysis entailed the simulation of the voice-data switch with embedded priority system, generation of latency times for various configurations and transaction traffic rates, and the development of a framework and theoretical propositions for configuration of super-saturated systems. It was shown that the concept of tolerance levels defined in the risk of extreme events can be applied for embedded priority systems and was the basis for the application of the zone-configuration evaluation diagram

Systemic Analysis of the use of Artificial Intelligence (AI) In Regulating Terrorist Content on Social Media Ecosystem Using Functional Dependency Network Analysis (FDNA)

This research is a systemic analysis of emerging risks to the use Artificial Intelligence (AI) in regulating terrorist content on social media ecosystems using Functional Dependency Network Analysis (FDNA), a proven system-design-and-analysis tool). The research has three phases: 1) framing the problem by identifying and describing AI ecosystem elements as intended, implied and explicit objectives, discernible attributes, and performance indictors; 2) describing the idealized problem-solved scenario, which includes detailing ‘success’ states of the ecosystem; and 3) systemic risk analysis including identifying failure scenarios for each element and establishing causalities among elemental attributes leading to failure scenarios. This research contributes toward a sustainable and more robust solution to the issue of regulating one particular form of malicious content on social media platforms (i.e., terrorist content) based not on one perspective but on the entire ‘ecosystem’ using FDNA

Introduction to the Course

This PDF document describes the course, Developing Technology Foresight: Case Study of AI in InsurTech, and includes learning outcomes and a course outline

Module 2: Case studies of AI and InsurTech

Instructional Module 2 for course, Developing Technology Foresight: Case Study of AI in InsurTech

InsurTech and Actuarial

Questions regarding InsurTech and actuarial work

Module 1: Introduction to Technology Foresight, Risk Management, and InsurTech

Instructional Module 1 for course, Developing Technology Foresight: Case Study of AI in InsurTech

Systemic Methodology for Cyber Offense and Defense

This paper describes a systemic method towards standardization of a cyber weapon effectiveness and effectiveness prediction process to promote consistency and improve cyber weapon system evaluation accuracy – for both offensive and defensive postures. The approach included theoretical examination of existing effectiveness prediction processes for kinetic and directed energy weapons, complemented with technical and social aspects of cyber realm. The examination highlighted several paradigm-shifts needed to transition from purely kinetic-based processes and transition into the realm of combined kinetic and cyber weapons. Components of the new method for cyber weapons are cyber payload assessment, effects identification, and target assessment. The ultimate outcome of method is the ‘Probability of Kill’ for a cyber weapon paired with a threat and within a given situation. This probability is a function of factors such as intelligence gathered on the latency of information, access points, hardware and software configurations, accuracy and completeness of network map, understanding of operations tempo; likelihood that vulnerabilities being exploited are patched and IT’s ability to detect and respond to the delivery of the cyber payload; and probability that the payload will achieve the desired mission effects. Aside from the use of this method for offensive purposes, it can also be mirrored as cyber defense and can serve as basis for developing cyber defense strategies, such as focused counter intelligence on access points, hardware and software configurations, and network map and architecture, comprehensive patching to assure most current and complete patches are deployed, and well trained and equipped IT with ability to detect and respond to cyber payloads

InsurTech and Claims

Questions related to InsurTech and claims

Module 3: Technology Foresight and InsurTech

Instructional Module 3 for course, Developing Technology Foresight: Case Study of AI in InsurTech