Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

The NIST Artificial Intelligence Risk Management Framework (AI RMF) is a recent framework developed by The National Institute of Standards and Technology (NIST) to guide organizations across all sectors in the use of artificial intelligence (AI) and its systems. As AI continues to become implemented in nearly every sector — from healthcare to finance to national defense — it also brings new risks and concerns with it.

In today’s digital age, where cars have evolved into data centers on wheels within the Internet of Things (IoT) landscape, ensuring cybersecurity in the automotive industry has become paramount. The emergence of connected cars, a significant subset of IoT, brings with it a host of cybersecurity challenges, prompting regulators to take swift action. One such pivotal step is the establishment of WP.29 standards, revolutionizing automotive cybersecurity.

On Friday April 12, Palo Alto disclosed that some versions of PAN-OS are not only vulnerable to remote code execution, but that the vulnerability has been actively exploited to install backdoors on Palo Alto firewalls. A patch is expected to be available on April 14th. The advisory from Palo Alto is here. Palo Alto has marked this vulnerability as critical and NVD has scored it a 10.0 with CVSSv3. Wallarm currently detects attacks against this vulnerability with no additional configuration required.

Simply put, APIs (short for application programming interface) are how machines, cloud workloads, automation and other non-human entities communicate with one another. They also represent an access point to highly sensitive company data and services. Almost every organization uses these machine interfaces, and their usage is only growing because they are essential to digital transformation and automation initiatives.

As large language models (LLMs) continue to push the boundaries of natural language processing, their widespread adoption across various industries has highlighted the critical need for robust security measures. These powerful AI systems, while immensely beneficial, are not immune to potential risks and vulnerabilities. In 2024, the landscape of LLM security tools has evolved to address the unique challenges posed by these advanced models, ensuring their safe and responsible deployment.

The recent discovery of a backdoor in XZ Utils (CVE-2024-3094), a data compression utility used by a wide array of various open-source, Linux-based computer applications, underscores the importance of open-source software security. While it is often not consumer-facing, open-source software is a critical component of computing and internet functions, such as secure communications between machines.