Microsoft Vulnerabilities Report 2022

whitePaper | November 23, 2022

The modern distributed workplace—including enterprises with thousands of branches and remote workers—requires updated approaches to cloud, networking, and security infrastructure. Cloud migration has opened the door to a huge increase in sophisticated applications, requiring networks that can carry traffic to an increasingly remote workforce. In turn, this creates a unique set of new requirements for security, including special capabilities to handle unpredictable locations of users, applications, and traffic flows.

whitePaper | August 18, 2020

This year, back to school for college students means more than just buying supplies, finding the right dorm and making sure to get into the proper classes. The COVID-19 pandemic has changed the annual fall ritual into one where campuses need to make sure they are safe from infection while providing a healthy environment for learning, growth and community.

whitePaper | September 28, 2022

Mobile apps typically communicate with several backend services over the Internet. In some cases those services may have been developed specifically for that mobile app, but in most cases the app will be dependent on services developed by a 3rd party. This whitepaper discusses how communication with those services can be properly secured, and how Approov’s new runtime secrets protection feature enables significantly enhanced security to be added with a minimum of development effort.

whitePaper | June 7, 2022

Telecoms need to be able to incorporate new technologies and next-generation connectivity such as 5G to customers and end users. To achieve these ambitious goals, they need to optimize their networks – make them more intelligent if you will. Some of the tools needed include artificial intelligence (AI), machine learning (ML) and artificial intelligence operations (AIOps). This document will explore what intelligent networking means to telecoms, vendors and customers, and how AI and ML technologies and tools can be used, the cultural shifts the industry needs to make it a success, and what to bear in mind when deploying machine learning across a telecom network.

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.

whitePaper | August 25, 2022

Businesses are in different stages with their artificial intelligence (AI) technologies. Even today, after many years of evaluating AI, only one third of organizations have actually put AI into production (31%1 ). Organizations that started somewhat later are currently prototyping their AI solutions (20%). Companies that only recently came on board with AI are experimenting with AI technologies for their business cases (25%). And a relatively large portion (24%) have only just begun evaluating AI for their business cases.