As mentioned in the previous chapter, when it comes to open source libraries, the selection is overwhelming. Keeping track of these, as well as which libraries are safe to use, is a time-consuming manual task for a technician. The risk of not discovering a security breach is also very real, and as a security engineer, we see that maintaining overall security is nearly impossible without automation, and on top of that, AI and machine learning.
There are numerous solutions on the market today that guide customers toward open source library usage recommendations. However, as the percentage of projects introducing libraries continues to grow, it is critical to spend as little time as possible identifying potential risk. Commercial vendors that conduct risk assessments on third parties are typically updated on rules, but also on potentially risky committers. This ensures that when you perform a scan for third-party libraries, you will receive an early notification and, additionally, a recommendation on which library to use.
One of the most significant enhancements to security is the ability to eliminate all manual work by automating as much of the delivery pipeline as possible. Additionally, malicious and potentially insecure libraries should be removed. It's not just about identifying problems; it's also about isolating and mitigating damage to existing solutions.
As a vulnerability scanning vendor, Snyk identifies increased visibility within the DevSecOps chain as a critical component of improved security. This means that by utilizing machine learning for deployment, you may be able to anticipate a potential security breach prior to deploying solutions. Which, once again, is critical to success.