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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.

In the previous parts of this series, we have learned about cryptography, what makes quantum computers unique, and how quantum computers break this cryptography. In the fourth and final part of our study on post-quantum cryptography, we will look at quantum-resistant algorithms that could replace our existing cryptography.  

We have been looking at the hacktivism landscape to help the United Nations Interregional Crime and Justice Research Institute (UNICRI) understand this cybercriminal phenomenon for their paper on the topic. The UNICRI paper was released today at a launch event held at the United Nations headquarters where we gave an overview on emerging trends in the underground.

This article discusses the importance of properly identifying and protecting AI model files and their associated assets, such as labels, from malicious or even unintended tampering.

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).

Tools that aggregate access into multiple different environments, such as API gateways, pose a security risk for all these environments upon breach. In this article, we continue our journey through the security issues of the API Gateway landscape. Our new research focuses on another popular API gateway — Kong.