Cloud infrastructures can comprise thousands of interconnected and dynamic resources. This complexity introduces unique challenges to monitoring and securing these architectures. Understanding where user activity originates—and what actions constitute security threats—is a complex task when you’re dealing with the huge volume of logs, metrics, and other telemetry that highly distributed cloud environments generate each day.
In our interconnected world, the value of data is growing with cyber threats also on the rise. This causes the security and protection of data to become crucial. Organizations have become compelled to adopt strict measures to safeguard their data. Implementing security practices in data protection and encryption ensures the confidentiality, integrity, and availability of the data stored in the cloud.
The term Internet of Things (IoT) describes a network of technologies and services where various devices are interconnected and exchange data. These devices can be anything from wearable fitness trackers, smart televisions, and wireless infusion pumps to cars and many others.
AI and machine learning (ML) have hit the mainstream as the tools people use everyday – from making restaurant reservations to shopping online – are all powered by machine learning. In fact, according to Morgan Stanley, 56% of CIOs say that recent innovations in AI are having a direct impact on investment priorities. It’s no surprise, then, that the ML Engineer role is one of the fastest growing jobs.
Open-Source Intelligence (OSINT) can be valuable for an organization and penetration testing engagements in several ways. Today, let me highlight two areas: Leaked Credentials and Files. As part of any security engagement, it is ideal, if not essential, that we look up our target’s leaked credentials and files, as many clients do not have a high level of visibility or awareness in this area.
