Did you know that nine out of 10 companies detected software supply chain risks in the past 12 months? The increase in the number of dependencies in a supply chain has extended the attack surface for adversaries. It has also caused threat actors to shift their focus from the downstream chain affecting just end users to the upstream chain affecting vendors, customers, and end users alike.
AI has become a hot topic thanks to the recent headlines around the large language model (LLM) AI with a simple interface — ChatGPT. Since then, the AI field has been vibrant, with several major actors racing to provide ever-bigger, better, and more versatile models. Players like Microsoft, NVidia, Google, Meta, and open source projects have all published a list of new models. In fact, a leaked Google document makes it seem that these models will be ubiquitous and available to everyone soon.
Today, an intricate web of tools, programs, and individuals collaborates to bring applications to life. This interconnected network, the software supply chain, encompasses the various entities and processes that shape the software development lifecycle (SDLC), including developers, dependencies, network interfaces, and DevOps practices. Given the diverse nature of these components, ensuring the security of each element becomes paramount.
When developing applications, organizations rely heavily on the software development lifecycle (SDLC) to engrain security into the development process early and continuously. The SDLC lays out how to build security into early steps as developers are creating and testing applications. As such, organizations are able to embed security practices when it matters most.
According to CrowdStrike Report, a 50% increase has been analyzed in active intrusions and cyber-attacks in 2022. And the number may increase in 2023 too. With more and more applications becoming a target of hackers, it getting complex for developers to identify relevant security approaches. Development teams are somewhere unable to select the best mechanism, which would be compatible, high-performing, and strong enough to prevent attacks.
Are you or your development team tired of using application security tools that generate countless results, making it difficult to identify which vulnerabilities pose actual risks? Do you struggle with inefficient or incorrect prioritization due to a lack of context? What adds insult to injury is that traditional CVSS scoring methods ignore critical details like software configurations and security mechanisms.
Spotify’s engineering team recently published a blog discussing their use of Snyk to maintain security testing in the SDLC. The following is a recap of that blog written by Engineering Manager, Edina Muminovic. Spotify, a company known for employing thousands of world-class developers, needed to redraw its software development lifecycle, or SDLC.
With all the remote works, online businesses, and digital lifestyle, applications (software) have become an integral part of our lives. In contrast, the growing rate of data breaches and cyber-attacks exploiting minor glitches in application functionality has diverted attention to application security which is still underrated in the era of phenomenal technological advancement.
Vulnerabilities found in application platforms and third-party libraries have drawn growing attention to application security in the last few years, putting pressure on DevOps teams to detect and resolve vulnerabilities in their Software Development Life Cycle (SDLC). Take the NVD (National Vulnerability Database), which tracks and records all significant vulnerabilities published and disclosed by software vendors.
