GitLab introduces new cybersecurity and AI development features
GitLab Inc. today introduced a new version of its software development platform with features that will help companies improve their cybersecurity, create machine learning applications, and fix bugs more easily.
New features will be rolled out gradually. Some will be released today, while others will be available next year.
GitLab provides a popular platform that companies use to manage their software development projects. Originally, the platform focused primarily on helping developers manage the code files that make up an application. Over the years, GitLab has added features to automate many related tasks, such as testing code to detect security vulnerabilities, deploying software to production, and detecting application errors.
GitLab was made public last October. The company has many large companies among its customers, such as Nvidia Corp., Siemens AG and others.
The new launch of the platform that GitLab has announced today will improve the company’s value proposition in several areas. An especially central focus of the update is cybersecurity.
Businesses use a method known as dynamic application security testing, or DAST, to scan their applications for vulnerabilities. The method involves launching simulated cyberattacks against an application to determine if it may be vulnerable to piracy. GitLab is replacing the open source DAST tools your platform has used for the task so far with a proprietary engine designed to deliver better performance and more configuration options.
GitLab is also adding other cybersecurity features. The company is launching a feature that can automatically generate a list of all software components in an application, which will facilitate cybersecurity assessments. In addition, GitLab will allow developers to host the code they produce as part of a software development project in a secure cloud environment rather than a local computer to reduce the risk of cyberattacks.
An increasing number of business applications are using machine learning models to enhance their functions. With the recently announced platform updates, GitLab intends to address this use case more directly.
GitLab plans to launch a tool that will allow developers to create different versions of a machine learning model, compare them, and determine which one is the most effective. Another nearby feature will make it easier to manage the data sets that a software team uses to train neural networks. The feature can help transfer training data from external systems to the GitLab platform.
“In today’s highly competitive landscape, organizations are under more pressure than ever to deliver software faster and more securely,” said David DeSanto, GitLab’s vice president of product. “GitLab solves this problem with The One DevOps Platform. Organizations can end their DIY DevOps tool chains.”
Some of the features of the new version of the GitLab platform are based on the technology you got through acquisition from the startup Opstrace Inc. last year. Opstrace created an open source tool that can be used to resolve application errors. GitLab is adding new features that will make it easier for developers to analyze different types of error data, including metrics, logs, and traces, as part of their troubleshooting efforts.
GitLab is also launching a number of other features as part of the release. Some of the new features are designed to make it easier to detect software errors and other common tasks, while other additions focus on more specialized tasks, such as ensuring that an application project complies with internal best practices in cybersecurity. a company.