Big Data: A Twenty-First Century Arms Race. Dieser Artikel ist nicht verfügbar.Big Data: A Twenty-First Century Arms Race

We are living in a world awash in data. Accelerated interconnectivity, driven by the proliferation of internet-connected devices, has led to an explosion of data—big data. A race is now underway to develop new technologies and implement innovative methods that can handle the volume, variety, velocity, and veracity of big data and apply it smartly to provide decisive advantage and help solve major challenges facing companies and governments. For policy makers in government, big data and associated technologies like machine-learning and artificial Intelligence, have the potential to drastically improve their decision-making capabilities. How governments use big data may be a key factor in improved economic performance and national security. This publication looks at how big data can maximize the efficiency and effectiveness of government and business, while minimizing modern risks. Five authors explore big data across three cross-cutting issues: security, finance, and law.Chapter 1In Chapter 1, “The Conflict Between Protecting Privacy and Securing Nations,” Els de Busser, a senior lecturer and senior researcher at The Hague University’s Centre of Expertise Cyber Security, explains the conflicts between the data privacy and protection laws that apply to law enforcement and intelligence agencies versus those that apply to commercial entities in the private sector. The increasing localization of privacy laws has placed strain on cross-border data flows, both for law enforcement and for economic monitors. Exacerbating the problem is the different legal approaches taken in Europe and the United States, with the former tending to adopt more holistic legal frameworks, while the latter adopts more sector-specific frameworks. Chapter 2In Chapter 2, “Big Data: Exposing the Risks from Within,” Erica Briscoe, a senior research scientist and lab chief scientist at the Georgia Tech Research Institute, explores how institutions can leverage big data to decrease their risk from malicious human behavior, such as insider threats. Dr. Briscoe explores how organizations can use big data techniques, including behavior modeling and anomaly detection to identify, monitor, and prevent malicious behavior. In addition, she argues that building and maintaining trust between employers and employees is critical to discouraging malicious behavior and insider threats. To create such a trusting environment, Dr. Briscoe recommends: protecting personally identifiable information to assuage fears that data could be used to negatively affect employees; monitoring both known threats and user behavior concurrently; and fostering a cybersecurity mindset, attained through a leadership-driven effort, that is able to adapt to changing threats. A sidebar exploring the future of trust in an increasingly automated world is also included. Chapter 3In Chapter 3, “Big Data: The Latest Tool in Fighting Crime,” Benjamin Dean, Fellow for Cyber Security and Internet Governance at Columbia University, focuses on how digital technologies and analysis of big data can be used to identify external threats, including the detection and prevention of fraud, money laundering, bribery, terrorism, and other criminal activities. There are a range of big data analytic techniques discussed, ranging from metadata collection and network analysis, to data fusion and predictive analytics. A key recommendation is the need to find and invest in people who have the right knowledge, skills, and abilities to effectively and correctly use these analytic techniques. Additionally, at the strategic level, organizations need to understand how big data analytics fit into a wider organizational strategy. Chapter 4In Chapter 4, “Big Data: Tackling Illicit Financial Flows,” Tatiana Tropina, a senior researcher at the Max Planck Institute for Foreign and International Criminal Law, explores how big data can help tackle the spread of online cybercrime and illicit financial flows—money that is illegally earned,