In modern network environments focused on cloud technology, organizations have undergone a significant transformation in the development and deployment of their IT assets. The introduction of cloud technology has simplified and expedited the deployment process, but it often lacks centralized change management. The cloud's shared responsibility model enables quick deployment and scaling but can pose security risks if not properly managed and understood.
We just introduced what we believe is a unique application of real-time, deep learning (DL) algorithms to network prevention. The announcement is hardly our foray into artificial intelligence (AI) and machine learning (ML). The technologies have long played a pivotal role in augmenting Cato’s SASE security and networking capabilities, enabling advanced threat prevention and efficient asset management. Let’s take a closer look.
Back in 2015, we published an article about the third party risks that are introduced into a home network. Now, eight years later, it is a good time to revisit the landscape of the home network. If we think about the technology in most homes in 2015, it was fairly sparse, consisting only of a router with an internet connection. The speed of most home internet connections was well below 100Mbps.