Research

It’s projected that by 2027, more than 90% of global organizations will be running containerized applications, while 63% of enterprises already adopted a cloud-native strategy in their businesses in 2023.

By 2025, Gartner has projected that over half of enterprises’ expenditure on software, infrastructure, business process services, and system infrastructure will be directed toward cloud-based solutions. We have seen this trend in enterprise spending both during and after the pandemic, on top of how fast companies are shifting to the cloud.

The cybercriminal abuse of generative AI (GenAI) is developing at a blazing pace. After only a few weeks since we reported on Gen AI and how it is used for cybercrime, new key developments have emerged. Threat actors are proliferating their offerings on criminal large language models (LLMs) and deepfake technologies, ramping up the volume and extending their reach.

The adoption of large language models (LLMs) and Generative Pre-trained Transformers (GPTs), such as ChatGPT, by leading firms like Microsoft, Nuance, Mix and Google CCAI Insights, drives the industry towards a series of transformative changes. As the use of these new technologies becomes prevalent, it is important to understand their key behavior, advantages, and the risks they present.

The increased adoption of technologies like artificial intelligence (AI), machine learning (ML), large language models (LLMs), and high-performance computing (HPC) underscores the growing need to prioritize the security of graphics processing units (GPUs).

Generative AI continues to be misused and abused by malicious individuals. In this article, we dive into new criminal LLMs, criminal services with ChatGPT-like capabilities, and deepfakes being offered on criminal sites.

Distributed energy generation (DEG) is a term used to describe the shift from centralized energy generation, such as power companies, to a source — typically a renewable energy source — closer to the user.

As technologies continue to evolve and expand, organizations experience a technological paradox: Their increasing interconnectivity means that they simultaneously become more distributed. Case in point, robust cloud and networking technologies support today’s widespread adoption of hybrid and remote work arrangements, allowing employees all over the globe to work remotely full time or at least part of the time.

We probed the Azure Machine Learning (AML) service to identify security flaws and vulnerabilities and shed light on the unseen aspects of silent threats in managed services like AML.