Friction between DevOps and Security – Here’s Why it Can’t be Ignored

Note: This post is co-authored by JFrog and Sean Wright and has also been published on Sean Wright’s blog. DevOps engineers and Security professionals are passionate about their responsibilities, with the first mostly dedicated to ensuring the fast release and the latter responsible for the security of their company’s software applications. They have many common …

Tips from a CSO: How to Secure Your Software Supply Chain

Trust is vital to success in our industry. Whether you’re creating and managing software for use internally, by other businesses, or direct-to-consumer, you need to be able to create trust with your end users. This can be accomplished, in part, by showing evidence of security measures, bringing the right people and tactics to the table, …

Qwak and JFrog integration

Advancing MLOps with JFrog and Qwak

Modern AI applications are having a dramatic impact on our industry, but there are still certain hurdles when it comes to bringing ML models to production. The process of building ML models is so complex and time-intensive that many data scientists still struggle to turn concepts into production-ready models. Bridging the gap between MLOps and …

Top JFrog Security Blogs 2023

Top JFrog Security Research Blogs of the Year

With over 29,000 CVEs and 5.5 billion malware attacks recorded in the past year, it’s no wonder that software supply chain security is a top priority for enterprise developers on a global scale. That is also why JFrog Security Research has been instrumental in identifying and analyzing the biggest threats and devising methods to protect …

dome over boxes to show security over software packages

N-Day Hijack: Analyzing the lifespan of package hijacking attacks

Software package hijacking has become a prominent concern for individuals, businesses, and the cybersecurity community at large. We’ve seen this new threat trend rise over the past couple of years, with the potential to severely impact the software supply chain by attackers exploiting software packages to execute malicious code. This blog post details a case …

Speed and Trust in Enterprise Software Development

How to Combine Speed and Trust in Enterprise Software Development

Software development begins with code, which is then integrated, compiled, tested, and in the end distributed to users. This is often the secret sauce of innovation that organizations must protect to keep their competitive edge. With the software application development market growing at almost 30% per year and the average project taking just 4-6 months …

Get Ready for Next. swampUP 2023

Get Ready for Next.
Put DevOps, DevSecOps, and AI to Work.

Our community has always had a “next.” There was the dawn of the computer age, when “next” meant that processing didn’t take up an entire room. There was the “next” of personal computing. Next came laptops, the internet, microservices, cloud-native, cybersecurity, automation and more. The thing that is next is always right around the corner …

5 tips on how Developers, DevOps and security teams can work together

As we all know, team collaboration can sometimes be a bit complicated. Especially when different teams in the organization strive to achieve their own individual goals. This is where new organizational practices, such as DevOps and DevSecOps, have paved the path for us to work together and achieve our mutual goals. Take a look at …

Five Examples of Infection Methods Attackers Use to Spread Malicious Packages

Welcome to the second post in our series on Malicious Software Packages. This post focuses on the infection methods attackers use to spread malicious packages, and how the JFrog Security research team unveiled them. If you missed the first blog, here are some key takeaways: Third-party software packages contain vulnerabilities or malicious code delivered through …

Machine Learning Valohai and JFrog Connect

Continuous Training and Deployment for Machine Learning (ML) at the Edge

Running machine learning (ML) inference in Edge devices close to where the data is generated offers several important advantages over running inference remotely in the cloud. These include real-time processing, lower cost, the ability to work without connectivity and with increased privacy. However, today, implementing an end-to-end ML system for edge inference and continuous deployment …