GitLab Duo Enterprise will bring AI assistance to finding and fixing vulnerabilities and other aspects of the software development life cycle. GitLab has unveiled GitLab 17, a major update of its devsecops platform that brings a CI/CD catalog of reusable pipeline components and an AI impact dashboard. The company also announced GitLab Duo Enterprise, an AI-powered assistant that helps detect vulnerabilities in code and resolve CI/CD bottlenecks. GitLab Duo Enterprise, the subject of a virtual launch event on June 24, combines the developer-focused AI capabilities of GitLab Duo Pro, which include code suggestions and code explanation, with enterprise-oriented AI capabilities for other aspects of the software development life cycle, such as detecting and fixing security vulnerabilities and summarizing issue discussions and merge requests. GitLab Duo Enterprise will also resolve CI/CD bottlenecks and failures and enhance team collaboration, GitLab said. GitLab 17, released May 16, introduces a CI/CD catalog that allows GitLab users to discover, reuse, and contribute pre-built CI/CD components. Users also can create a private catalog to distribute customized pipelines to automate workflows. The new AI impact dashboard in GitLab 17 is intended to help organizations understand the impact of the GitLab Duo AI programming assistant on developer productivity. For example, users can compare AI usage trends with software development metrics like lead time, cycle time, DORA, and vulnerabilities. GitLab also offered a list of improvements coming to the devsecops platform: A native secrets manager to allow users to store sensitive credentials. Static application security testing (SAST) integrations to help improve accuracy, reduce false positives, and resolve application-layer risks. Analytics capabilities to understand user behavior patterns, measure product performance, and prioritize feature enhancements. Enterprise agile planning capabilities, including enhanced epics, custom fields in issues, and roadmaps. A model registry for data scientists to develop AI/ML models on the same platform where engineers build and deploy code. Related content analysis Beyond the usual suspects: 5 fresh data science tools to try today The mid-month report includes quick tips for easier Python installation, a new VS Code-like IDE just for Python and R users, and five newer data science tools you won't want to miss. By Serdar Yegulalp Jul 12, 2024 2 mins Python Programming Languages Software Development analysis Generative AI won’t fix cloud migration You’ve probably heard how generative AI will solve all cloud migration problems. It’s not that simple. Generative AI could actually make it harder and more costly. By David Linthicum Jul 12, 2024 5 mins Generative AI Artificial Intelligence Cloud Computing news HR professionals trust AI recommendations HireVue survey finds 73% of HR professionals trust AI to make candidate recommendations, while 75% of workers are opposed to AI making hiring decisions. By Paul Krill Jul 11, 2024 3 mins Technology Industry Careers how-to Safety off: Programming in Rust with `unsafe` What does it mean to write unsafe code in Rust, and what can you do (and not do) with the 'unsafe' keyword? The facts may surprise you. By Serdar Yegulalp Jul 11, 2024 8 mins Rust Programming Languages Software Development Resources Videos