[{"data":1,"prerenderedAt":823},["ShallowReactive",2],{"/en-us/blog/3-gitlab-features-to-level-up-devsecops-workflows":3,"navigation-en-us":42,"banner-en-us":462,"footer-en-us":472,"blog-post-authors-en-us-Salman Ladha":711,"blog-related-posts-en-us-3-gitlab-features-to-level-up-devsecops-workflows":726,"blog-promotions-en-us":761,"next-steps-en-us":813},{"id":4,"title":5,"authorSlugs":6,"authors":8,"body":10,"category":11,"categorySlug":11,"config":12,"content":16,"date":25,"description":17,"extension":26,"externalUrl":27,"featured":15,"heroImage":19,"isFeatured":15,"meta":28,"navigation":15,"path":29,"publishedDate":25,"rawbody":30,"seo":31,"slug":14,"stem":36,"tagSlugs":37,"tags":40,"template":13,"updatedDate":27,"__hash__":41},"blogPosts/en-us/blog/3-gitlab-features-to-level-up-devsecops-workflows.md","3 GitLab features to level up DevSecOps workflows",[7],"salman-ladha",[9],"Salman Ladha","Last month, we, along with the GitLab community, introduced more than 140 improvements to our AI-powered DevSecOps platform to help you build better and more secure software, faster. With that much product innovation, we know it can be difficult to keep track of the latest GitLab has to offer. So, each quarter, we’re spotlighting the most impactful capabilities to help you consolidate toolchains, boost development efficiency, and improve application security. Here are three new features [released in GitLab](/releases/whats-new/) over the past few months that make an immediate impact on your software development.\n\n > Learn why GitLab was named a Leader in the [2024 Gartner® Magic Quadrant™ for DevOps Platforms](https://about.gitlab.com/blog/gitlab-named-a-leader-in-the-2024-gartner-magic-quadrant-for-devops/) and the [2024 Gartner® Magic Quadrant™ for AI Code Assistants](https://about.gitlab.com/blog/gitlab-named-a-leader-in-2024-gartner-magic-quadrant-for-ai-code-assistants/).\n\n## Root Cause Analysis: Diagnose broken pipelines faster\n\n[Developers spend less than a quarter of their time on code creation](https://about.gitlab.com/developer-survey/), according to our 2024 Global DevSecOps Survey. The bulk of their time is consumed by administrative tasks, planning, and troubleshooting — many of which can be accelerated with AI.\n\nFor example, diagnosing broken pipelines is a frustrating task for developers, which requires them to tediously scour through dense log files to identify the cause of the error. This often leads to trial-and-error fixes, sleuthing for solutions on Google, or asking a peer for support. This is a practical scenario where [GitLab Duo Root Cause Analysis](https://about.gitlab.com/blog/developing-gitlab-duo-blending-ai-and-root-cause-analysis-to-fix-ci-cd/) can meaningfully help developers.\n\nRoot Cause Analysis analyzes log files to uncover the core issue behind an error message in a CI/CD pipeline. Not only does it provide teams with insight into what caused the issue, but it also suggests a fix to help resolve the issue faster.\n\nWith less time spent on troubleshooting, developers can focus on building differentiated products to help their organizations win.\n\nGitLab Duo Root Cause Analysis is available as a [GitLab Duo Enterprise add-on](https://about.gitlab.com/sales/?type=free-trial&toggle=gitlab-duo-pro).\n\n\u003C!-- blank line -->\n\u003Cfigure class=\"video_container\">\n  \u003Ciframe src=\"https://www.youtube.com/embed/sTpSLwX5DIs?si=JZSgd7GTTk4y6mre\" frameborder=\"0\" allowfullscreen=\"true\">\u003C/iframe>\n\u003C/figure>\n\u003C!-- blank line -->\n\n## Vulnerability Explanation: Quickly understand security risks\n\nWe know that developers are playing [an even greater role in the remediation of security vulnerabilities](https://about.gitlab.com/developer-survey/). However, not every developer is well-versed in cybersecurity or has a working knowledge of the tactics, techniques, and procedures a threat actor will use to exploit an application. This creates a knowledge gap, which is exposed when vulnerabilities are uncovered.\n\n[GitLab Duo Vulnerability Explanation](https://about.gitlab.com/the-source/ai/understand-and-resolve-vulnerabilities-with-ai-powered-gitlab-duo/) bridges the knowledge gap between security and development teams. It gives developers a detailed description of the vulnerability infecting their code, real-world examples of how attackers can exploit the vulnerable code, and practical suggestions for remediation.\n\nWith this feature, you can level up your security skills, resolve vulnerabilities faster, and help create a proactive security culture — all while lightening the load on your security teams.\nGitLab Duo Vulnerability Explanation is available as a [GitLab Duo Enterprise add-on](https://about.gitlab.com/sales/?type=free-trial&toggle=gitlab-duo-pro).\n\n\u003C!-- blank line -->\n\u003Cfigure class=\"video_container\">\n  \u003Ciframe src=\"https://www.youtube.com/embed/MMVFvGrmMzw?si=Zsx-91078XSNNUSm\" frameborder=\"0\" allowfullscreen=\"true\"> \u003C/iframe>\n\u003C/figure>\n\u003C!-- blank line -->\n\n## Advanced SAST: Filter out the noise\n\nFalse positives are a [top frustration](https://about.gitlab.com/developer-survey/2024/security-compliance/) for both security and development teams. Unfortunately, this is a common complaint of traditional Static Application Security Testing (SAST). While SAST is great at integrating security early in the software development lifecycle, its value diminishes when it produces inaccurate results. “Drowning in a backlog of vulnerabilities” is a reality for many security and development teams, often resulting in tension between them.\n\n[Advanced SAST](https://about.gitlab.com/blog/gitlab-advanced-sast-is-now-generally-available/), our newest security scanner, uses a proprietary detection engine with rules informed by in-house security research to identify exploitable vulnerabilities. It delivers more accurate results, so security and development teams don’t have to sort through the noise of false-positive results, shortening triage time, improving development velocity, and decreasing friction between teams.\n\nAdvanced SAST is available in the [GitLab Ultimate tier](https://about.gitlab.com/pricing/ultimate/).\n\n\u003C!-- blank line -->\n\u003Cfigure class=\"video_container\">\n  \u003Ciframe src=\"https://www.youtube.com/embed/xDa1MHOcyn8?si=Ff4HjNpvv5eXsSNH\" frameborder=\"0\" allowfullscreen=\"true\"> \u003C/iframe>\n\u003C/figure>\n\u003C!-- blank line -->\n\n## Put these features to work today\n\nAt GitLab, we’re committed to making it easier for teams to build software, faster. Capabilities like GitLab Duo Root Cause Analysis, GitLab Duo Vulnerability Explanation, and GitLab Advanced SAST are just a few of the recent innovations we’ve delivered to help developers and security teams level up their DevSecOps workflows. To learn more, check out our [releases page](/releases/whats-new/).\n\n> Get started with these new features today with [a free trial of GitLab Ultimate](https://gitlab.com/-/trials/new?glm_content=default-saas-trial&glm_source=about.gitlab.com%2F).","product",{"template":13,"slug":14,"featured":15},"BlogPost","3-gitlab-features-to-level-up-devsecops-workflows",true,{"title":5,"description":17,"authors":18,"heroImage":19,"tags":20,"category":11,"date":25,"body":10},"Fix broken pipelines faster, better understand security vulnerabilities, and filter out false positives with our latest platform improvements.",[9],"https://res.cloudinary.com/about-gitlab-com/image/upload/v1749665762/Blog/Hero%20Images/blog-gl17-release-hero-17-0-93-1800x945-fy25__1_.png",[11,21,22,23,24],"features","DevSecOps","security","CI/CD","2024-10-29","md",null,{},"/en-us/blog/3-gitlab-features-to-level-up-devsecops-workflows","---\nseo:\n  title: 3 GitLab features to level up DevSecOps workflows\n  description: >-\n    Fix broken pipelines faster, better understand security vulnerabilities, and\n    filter out false positives with our latest platform improvements.\n  ogTitle: 3 GitLab features to level up DevSecOps workflows\n  ogDescription: >-\n    Fix broken pipelines faster, better understand security vulnerabilities, and\n    filter out false positives with our latest platform improvements.\n  noIndex: false\n  ogImage: >-\n    https://res.cloudinary.com/about-gitlab-com/image/upload/v1749665762/Blog/Hero%20Images/blog-gl17-release-hero-17-0-93-1800x945-fy25__1_.png\n  ogUrl: >-\n    https://about.gitlab.com/blog/3-gitlab-features-to-level-up-devsecops-workflows\n  ogSiteName: https://about.gitlab.com\n  ogType: article\n  canonicalUrls: >-\n    https://about.gitlab.com/blog/3-gitlab-features-to-level-up-devsecops-workflows\ntitle: 3 GitLab features to level up DevSecOps workflows\ndescription: Fix broken pipelines faster, better understand security vulnerabilities, and filter out false positives with our latest platform improvements.\nauthors:\n  - Salman Ladha\nheroImage: https://res.cloudinary.com/about-gitlab-com/image/upload/v1749665762/Blog/Hero%20Images/blog-gl17-release-hero-17-0-93-1800x945-fy25__1_.png\ntags:\n  - product\n  - features\n  - DevSecOps\n  - security\n  - CI/CD\ncategory: product\ndate: '2024-10-29'\nslug: 3-gitlab-features-to-level-up-devsecops-workflows\nfeatured: true\ntemplate: BlogPost\n---\n\nLast month, we, along with the GitLab community, introduced more than 140 improvements to our AI-powered DevSecOps platform to help you build better and more secure software, faster. With that much product innovation, we know it can be difficult to keep track of the latest GitLab has to offer. So, each quarter, we’re spotlighting the most impactful capabilities to help you consolidate toolchains, boost development efficiency, and improve application security. Here are three new features [released in GitLab](/releases/whats-new/) over the past few months that make an immediate impact on your software development.\n\n > Learn why GitLab was named a Leader in the [2024 Gartner® Magic Quadrant™ for DevOps Platforms](https://about.gitlab.com/blog/gitlab-named-a-leader-in-the-2024-gartner-magic-quadrant-for-devops/) and the [2024 Gartner® Magic Quadrant™ for AI Code Assistants](https://about.gitlab.com/blog/gitlab-named-a-leader-in-2024-gartner-magic-quadrant-for-ai-code-assistants/).\n\n## Root Cause Analysis: Diagnose broken pipelines faster\n\n[Developers spend less than a quarter of their time on code creation](https://about.gitlab.com/developer-survey/), according to our 2024 Global DevSecOps Survey. The bulk of their time is consumed by administrative tasks, planning, and troubleshooting — many of which can be accelerated with AI.\n\nFor example, diagnosing broken pipelines is a frustrating task for developers, which requires them to tediously scour through dense log files to identify the cause of the error. This often leads to trial-and-error fixes, sleuthing for solutions on Google, or asking a peer for support. This is a practical scenario where [GitLab Duo Root Cause Analysis](https://about.gitlab.com/blog/developing-gitlab-duo-blending-ai-and-root-cause-analysis-to-fix-ci-cd/) can meaningfully help developers.\n\nRoot Cause Analysis analyzes log files to uncover the core issue behind an error message in a CI/CD pipeline. Not only does it provide teams with insight into what caused the issue, but it also suggests a fix to help resolve the issue faster.\n\nWith less time spent on troubleshooting, developers can focus on building differentiated products to help their organizations win.\n\nGitLab Duo Root Cause Analysis is available as a [GitLab Duo Enterprise add-on](https://about.gitlab.com/sales/?type=free-trial&toggle=gitlab-duo-pro).\n\n\u003C!-- blank line -->\n\u003Cfigure class=\"video_container\">\n  \u003Ciframe src=\"https://www.youtube.com/embed/sTpSLwX5DIs?si=JZSgd7GTTk4y6mre\" frameborder=\"0\" allowfullscreen=\"true\">\u003C/iframe>\n\u003C/figure>\n\u003C!-- blank line -->\n\n## Vulnerability Explanation: Quickly understand security risks\n\nWe know that developers are playing [an even greater role in the remediation of security vulnerabilities](https://about.gitlab.com/developer-survey/). However, not every developer is well-versed in cybersecurity or has a working knowledge of the tactics, techniques, and procedures a threat actor will use to exploit an application. This creates a knowledge gap, which is exposed when vulnerabilities are uncovered.\n\n[GitLab Duo Vulnerability Explanation](https://about.gitlab.com/the-source/ai/understand-and-resolve-vulnerabilities-with-ai-powered-gitlab-duo/) bridges the knowledge gap between security and development teams. It gives developers a detailed description of the vulnerability infecting their code, real-world examples of how attackers can exploit the vulnerable code, and practical suggestions for remediation.\n\nWith this feature, you can level up your security skills, resolve vulnerabilities faster, and help create a proactive security culture — all while lightening the load on your security teams.\nGitLab Duo Vulnerability Explanation is available as a [GitLab Duo Enterprise add-on](https://about.gitlab.com/sales/?type=free-trial&toggle=gitlab-duo-pro).\n\n\u003C!-- blank line -->\n\u003Cfigure class=\"video_container\">\n  \u003Ciframe src=\"https://www.youtube.com/embed/MMVFvGrmMzw?si=Zsx-91078XSNNUSm\" frameborder=\"0\" allowfullscreen=\"true\"> \u003C/iframe>\n\u003C/figure>\n\u003C!-- blank line -->\n\n## Advanced SAST: Filter out the noise\n\nFalse positives are a [top frustration](https://about.gitlab.com/developer-survey/2024/security-compliance/) for both security and development teams. Unfortunately, this is a common complaint of traditional Static Application Security Testing (SAST). While SAST is great at integrating security early in the software development lifecycle, its value diminishes when it produces inaccurate results. “Drowning in a backlog of vulnerabilities” is a reality for many security and development teams, often resulting in tension between them.\n\n[Advanced SAST](https://about.gitlab.com/blog/gitlab-advanced-sast-is-now-generally-available/), our newest security scanner, uses a proprietary detection engine with rules informed by in-house security research to identify exploitable vulnerabilities. It delivers more accurate results, so security and development teams don’t have to sort through the noise of false-positive results, shortening triage time, improving development velocity, and decreasing friction between teams.\n\nAdvanced SAST is available in the [GitLab Ultimate tier](https://about.gitlab.com/pricing/ultimate/).\n\n\u003C!-- blank line -->\n\u003Cfigure class=\"video_container\">\n  \u003Ciframe src=\"https://www.youtube.com/embed/xDa1MHOcyn8?si=Ff4HjNpvv5eXsSNH\" frameborder=\"0\" allowfullscreen=\"true\"> \u003C/iframe>\n\u003C/figure>\n\u003C!-- blank line -->\n\n## Put these features to work today\n\nAt GitLab, we’re committed to making it easier for teams to build software, faster. Capabilities like GitLab Duo Root Cause Analysis, GitLab Duo Vulnerability Explanation, and GitLab Advanced SAST are just a few of the recent innovations we’ve delivered to help developers and security teams level up their DevSecOps workflows. 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foundation","Pair GitLab Duo Agent Platform with Amazon Bedrock for agentic software development and orchestration.","If your team runs GitLab and has a strong AWS practice, a new combination of Duo Agent Platform and Amazon Bedrock is just for you. The model is simple: GitLab acts as your orchestration layer to help accelerate your entire software lifecycle with agentic AI, and Bedrock is designed to provide a secure, compliant foundation model layer with AI inference behind the scenes.\n\nGitLab Duo Agent Platform enables you to handle planning, merge pipelines, security scanning, vulnerability remediation, and more as part of your GitLab workflows, while the GitLab AI Gateway routes model calls to Bedrock (or GitLab-managed Bedrock-backed endpoints, depending on your setup). That means you can build on the identity and access management (IAM) policies, virtual private cloud (VPC) boundaries, regional controls, and cloud spend commitments you already have in AWS.\n\nIf you already use Amazon Bedrock and want AI to help inside the work you already do in GitLab, not in yet another standalone chat tool, this is the pairing for you.\n\n\nIn this article, we look at the real problem many teams face today: AI is fragmented, data paths are fuzzy, and Bedrock investment gets underused when AI sits outside the software development lifecycle. Then we break down your deployment options for GitLab Duo Agent Platform:\n\n* Integrated with self-hosted models on Amazon Bedrock for GitLab Self-Managed deployments and self-hosted AI gateway   \n* Integrated with GitLab-operated models on Amazon Bedrock (with GitLab-owned keys) for GitLab Self-Managed deployments and GitLab-hosted AI gateway  \n* Integrated with GitLab-operated models on Amazon Bedrock (with GitLab-owned keys) for GitLab.com instances and GitLab-hosted AI gateway\n\nWe wrap with a summary on how this approach helps avoid shadow AI and point-tool sprawl without creating a parallel tech stack for AI tooling.\n\n## AI everywhere, control nowhere\n\nSomewhere in your company right now, software teams might be using an AI tool that your security team hasn't approved. Prompt data might be leaving your environment through a path no one has fully mapped. And your organization’s Amazon Bedrock investment might be underused while individual teams expense separate AI tools, pulling workloads and cloud spend away from the platforms you’ve already committed to.\n\nInstead of being a people problem, this might be an architecture problem. And it surfaces the same three constraints in nearly every enterprise:\n\n**Operational fragmentation.** Each team, or sometimes even an individual developer, picks their own development toolset, including AI tooling and model selection. That fragmentation makes end-to-end governance within the software development lifecycle nearly impossible.\n\n**Security and sovereignty.** Where does prompt and code data actually flow? Who owns the logs?\n\n**Cloud spend optimization.** Commitments to key cloud providers like AWS are diluted as workloads and AI usage drift to point tools outside of customers’ existing agreements.\n\nGitLab Duo Agent Platform and Amazon Bedrock help solve this together. The division of labor is straightforward: Duo Agent Platform owns the workflow orchestration with agentic AI for software development, Bedrock owns the inference layer and hosts approved foundational models, and your organization has full control over the data and policy boundaries you already defined in AWS. Three jobs, three owners, no fragmentation.\n\n## GitLab Duo Agent Platform: The agentic control plane\n\nGitLab Duo Agent Platform is GitLab's agentic AI layer: a framework of specialized agents and flows that operate simultaneously and in-parallel, going beyond the traditional stage-based handoffs  and helping automate work across the entire software lifecycle. Rather than a single assistant responding to prompts, Duo Agent Platform enables teams to orchestrate many AI agents asynchronously using unified data and project context, including issues, merge requests, pipelines, and security findings. Linear workflows are turned into coordinated, continuous collaboration between software teams and their AI agents, at scale.\n\nWith that control plane in place, the natural next question is which AI foundation should power these agents. For customers who run GitLab Self-Managed on AWS and need inference traffic, prompt data, and logs to also stay within their AWS environment along with their software lifecycle data, Amazon Bedrock acting as the AI inference layer is the natural fit. \n\n## Amazon Bedrock: The trusted AI foundation\n\nAmazon Bedrock is a fully managed, serverless foundation model layer that runs entirely within your AWS environment. Customer data stays in the customer's AWS account: inputs and outputs are encrypted in transit and at rest, never shared with model providers, and never used to train base models. Bedrock carries compliance certifications across GDPR, HIPAA, and FedRAMP High, covering many regulated industry requirements out of the box. Teams can also bring fine-tuned models from elsewhere via Custom Model Import and deploy them alongside native Bedrock models through the same infrastructure, without managing separate deployment pipelines. Bedrock Guardrails adds configurable safeguards across all models for content filtering, hallucination detection, and sensitive data protection.\n\nTogether, GitLab Duo Agent Platform and Bedrock consolidate DevSecOps orchestration and AI model governance, helping eliminate the fragmentation that happens when teams roll out AI tools independently.\n\n## Choosing your deployment path\n\nThe integration delivers the same core GitLab Duo Agent Platform capabilities regardless of how it is deployed. What varies is who runs GitLab, who operates the AI Gateway, and whose Bedrock account the inference runs through. The right pattern depends on where your organization already operates.\n\nAt a high level, the integration has three main components:\n\n* **GitLab Duo Agent Platform:** agentic workflows embedded across the software development lifecycle  \n* **AI Gateway (GitLab-managed or self-hosted):** the abstraction layer between Duo Agent Platform and the foundational model backend   \n* **Amazon Bedrock:** the AI model and inference substrate\n\n![Deployment of GitLab and AWS Bedrock](https://res.cloudinary.com/about-gitlab-com/image/upload/v1776362365/udmvmv2efpmwtkxgydch.png)\n\nChoosing a deployment pattern is informed by where an organization wants to place the levers of control. The patterns below are designed to meet teams where they already are, whether that's SaaS-first, self-managed for compliance, or all-in on AWS with existing Bedrock investments.\n\n| Deployment Model | GitLab.com instance with GitLab-hosted AI Gateway with GitLab-operated Bedrock models   | GitLab Self-Managed with GitLab-hosted AI Gateway with GitLab-operated Bedrock models | GitLab Self-Managed  with self-hosted AI Gateway and customer-operated Bedrock models |\n| :---- | :---- | :---- | :---- |\n| **Ideal if you:** | Are primarily on GitLab.com and don’t want to self-host AI gateway and Bedrock models  | Need GitLab Self-Managed for compliance and operational reasons but don’t want to manage AI layer | Are AWS-centric with existing Bedrock usage and strict data/control needs  |\n| **Key Benefits** | Fastest, turnkey way to get Duo Agent Platform workflows: GitLab runs GitLab.com, the AI Gateway, integrated with Bedrock AI models. | Keep GitLab deployed in your own environment while consuming Bedrock models via a GitLab-managed AI Gateway, combining deployment control with simplified AI operations. | Run GitLab and AI Gateway in your AWS account, reuse existing IAM/VPC/regions, keep logs and data in your environment, and draw Bedrock usage from your existing AWS spend commitments. |\n\n## How customers use GitLab Duo Agent Platform with Amazon Bedrock\n\nPlatform teams can use GitLab Duo Agent Platform with Amazon Bedrock to standardize which models handle code suggestions, security analysis, and pipeline remediation. This helps enforce guardrails and logging centrally rather than letting individual teams adopt separate tools independently.\n\nSecurity workflows see particular benefit. GitLab Duo Agent Platform agents can propose and validate fixes for security findings within GitLab, helping reduce the manual triage work developers would otherwise handle outside the platform.\n\nFor enterprises already committed to AWS, routing AI workloads through Bedrock from within GitLab enables you to keep developer AI usage aligned with existing cloud agreements rather than generating separate, unplanned spend.\n\n## Closing the loop\n\nThe constraints that slow enterprise AI adoption are often not technical. They are organizational: fragmented tooling, ungoverned data flows, and cloud spend that never consolidates. Those are the problems that can stall AI programs even after the pilots succeed.\n\nGitLab Duo Agent Platform and Amazon Bedrock help address each one directly. Platform teams get consistent governance, auditability, and standardized paths for AI usage across the software development lifecycle. Development teams get streamlined, agentic workflows that feel native to GitLab. And AWS-centric organizations get to extend their existing Bedrock investment rather than build parallel AI infrastructure alongside it.\n\nThe result is an AI program that scales without fragmenting. Governance and velocity on the same stack, serving the same teams, under policies the organization already owns.\n\n\n> To explore which deployment pattern is right for your organization and how to align GitLab Duo Agent Platform and Amazon Bedrock with your existing AWS strategy, [contact the GitLab sales team](https://about.gitlab.com/sales/) and we’ll help you design and implement the best architecture for your environment. 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