Passwordless authentication for end users is taking the world by storm, offering organizations and individuals alike unprecedented security, user experience, and efficiency benefits. By all indications, the next generation of authentication for end users has finally arrived, sending the password the way of the dodo. Although they don’t get anywhere near the same hype, advanced authentication strategies for APIs are as critical as passwordless authentication for end-users.

Our Threat Intelligence team has observed an emerging obfuscation technique, specifically used to make Natural Language Processing (NLP) detection capabilities less effective. Broadly, malicious actors are adding additional characters, break lines, and legitimate links to the end of a phishing email in an attempt to disguise their malicious payloads amongst the noise and evade NLP detection.

Bumblebee is a highly sophisticated downloader malware cybercriminals use to gain access to corporate networks and deliver other payloads such as Cobalt Strike beacons and ransomware. The Google Threat Analysis Group first discovered the malware in March 2022 and named it Bumblebee based on a User-Agent string it used. The Netskope Threat Labs team discovered what seems to be a new infection chain leading to Bumblebee malware infection, and our findings corroborate those shared by other researchers.

Modern software development teams will have individual preferences about whether to use IDEs or which testing framework or coding convention to adopt. However, for teams that want to deliver high-quality software at a rapid pace, continuous integration and continuous delivery (CI/CD) is a must-have. Mature, high-performing dev teams lean heavily on their CI/CD pipeline. Because of this heavy dependence on CI/CD, ensuring the security of your CI/CD pipeline is incredibly important.

Cyber threats against AI systems are on the rise, and today’s AI developers need a robust approach to securing AI applications that address the unique vulnerabilities and attack patterns associated with AI systems and ML models deployed in production environments. In this blog, we’re taking a closer look at two specific tools that AI developers can use to help detect cyber threats against AI systems.

In our previous blog, Data in use and why you need to keep an eye on file activities, we discussed why you should monitor how your data is being used, threats to data in use, and how you can protect it. But, when your data is on the move, you need other security capabilities to secure it. In this blog, we’ll explore data in motion. Gone are the days when you’d simply store your data in your local database and share it with peers when required.