The Definitive Guide to Choosing a Micro-Segmentation Solution

whitePaper | May 15, 2023

The future of AI is hybrid. As generative AI adoption grows at record-setting speeds1 and drives higher demand for compute,2 AI processing must be distributed between the cloud and devices for AI to scale and reach its full potential – just like traditional computing evolved from mainframes and thin clients to today’s mix of cloud and edge devices. A hybrid AI architecture distributes and coordinates AI workloads among cloud and edge devices, rather than processing in the cloud alone. The cloud and edge devices, such as smartphones, vehicles, PCs, and IoT devices, work together to deliver more powerful, efficient, and highly optimized AI.

whitePaper | August 17, 2020

For years, employees drove to an office, opened their computer, logged onto a corporate network with their username and password and got on with their workday. But working standard hours in a fixed office location is becoming more and more rare. In fact, a report by Gallup found that 43 percent of American employees work remotely.1 This growing mobile workforce requires the same, secure access to resources as their onsite counterparts — without connecting to the corporate network. And both onsite and remote employees need secure ways to access the expanding number of applications and resources that are hosted in the cloud. To accommodate, enterprise technology and IT practices must adapt.

whitePaper | May 9, 2022

This document is published by the World Economic Forum as a contribution to a project, insight area or interaction. The findings, interpretations and conclusions expressed herein are a result of a collaborative process facilitated and endorsed by the World Economic Forum but whose results do not necessarily represent the views of the World Economic Forum, nor the entirety of its Members, Partners or other stakeholders

whitePaper | July 21, 2020

Back in 2016, the most popular app download on Christmas Day was Fitbit, dominating Apple’s App Store. The rapid adoption of Fitbit and other wearable fitness trackers has underpinnings in psychological motivation theories. Realtime data monitoring provides a powerful incentive that is capable of changing human behavior. Since 2016, the wearable technology market has continued to evolve, including the acquisition of Fitbit by Google, which was announced in November 2019.

whitePaper | January 28, 2022

Since more and more decisions that used to be made by humans are nowadays made either with the help of Artificial Intelligence (AI) or by AI alone, it is essential that the AI algorithms are “fair.” In this whitepaper, we discuss issues surrounding the fairness of AI applications, with a special focus on how it can be assessed independently and subsequently certified. We explain why an AI application cannot be classified – and subsequently certified – as “fair” or “unfair” in a general sense and propose an approach that makes it possible to classify it as “fair” or “unfair” under the (application)-specific definition of fairness.

whitePaper | January 5, 2020

Machine learning (ML) is an application of artificial intelligence (AI) that allows systems to automatically learn and improve from exposure to more data without being explicitly programmed. ML focuses on the development of computer programs that can access data and use it to learn for themselves.1 While AI represents the broader concept of machines being able to carry out tasks in an intelligent way, machine learning is a current application of AI based on the idea that we can give machines access to data, and they can use that data to learn for themselves.