[{"data":1,"prerenderedAt":828},["ShallowReactive",2],{"/en-us/blog/a-developers-guide-to-building-an-ai-security-governance-framework":3,"navigation-en-us":43,"banner-en-us":463,"footer-en-us":473,"blog-post-authors-en-us-Ayoub Fandi":713,"blog-related-posts-en-us-a-developers-guide-to-building-an-ai-security-governance-framework":728,"blog-promotions-en-us":767,"next-steps-en-us":818},{"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":41,"template":13,"updatedDate":27,"__hash__":42},"blogPosts/en-us/blog/a-developers-guide-to-building-an-ai-security-governance-framework.md","A developer's guide to building an AI security governance framework",[7],"ayoub-fandi",[9],"Ayoub Fandi","Artificial Intelligence (AI) has firmly established itself as a pillar of digital transformation, disrupting industries, increasing efficiency, and providing unmatched access to large data sets. AI also raises profound questions regarding security governance. How do I ensure I can leverage the best of what AI has to offer while mitigating its potential security risks? As [AI continues to advance](https://about.gitlab.com/topics/devops/the-role-of-ai-in-devops/), there is a growing need for strong oversight and accountability. This article delves into the complex landscape of AI security governance, exploring various frameworks, strategies, and practices that organizations like GitLab are adopting to ensure the responsible development of AI technologies and features.\n\n## Greater scrutiny on AI\n\n### AI: Single term, numerous realities\nAI isn't a monolithic entity - it encompasses a spectrum of technologies and applications. From machine learning algorithms that power recommendation systems to advanced natural language processing models like Anthropic’s Claude 3, each AI system brings its unique set of opportunities and challenges.\n\nAccording to [a 2023 MITRE report](https://www.mitre.org/sites/default/files/2023-06/PR-23-1943-A-Sensible-Regulatory-Framework-For-AI-Security_0.pdf), three main areas of AI currently exist:\n\n1. **AI as a subsystem**\n\n\u003Cp>\u003C/p>*\"AI is embedded in many software systems. Discrete AI models routinely perform machine perception and optimization functions, from face recognition in photos uploaded to the cloud, to dynamically allocating and optimizing network resources in 5G wireless networks. \u003Cp>\u003C/p> \"There are a wide range of vulnerabilities and threats against these types of AI subsystems – from data poisoning attacks to adversarial input attacks – that can be used to manipulate subsystems.\"*\u003Cp>\u003C/p>\n\n2. **AI as human augmentation**\n\u003Cp>\u003C/p>*\"Another application of AI is in augmenting human performance, allowing a person to operate with much larger scope and scale. This has wide-ranging implications for workforce planning as AI has the potential to increase productivity and shift the composition of labor markets, similar to the role of automation in the manufacturing industry. \u003Cp>\u003C/p> \"While sophisticated hackers and military information operations can already generate believable content today using techniques such as computer-generated imagery, LLMs will make that capability available to anyone, while increasing the scope and scale at which the professionals can operate.\"*\u003Cp>\u003C/p>\n\n3. **AI with agency**\n\u003Cp>\u003C/p>*\"A segment of the tech community is increasingly concerned about scenarios where sophisticated AI could operate as an independent, goal-seeking agent. While science fiction historically embodied this AI in anthropomorphic robots, the AI we have today is principally confined to digital and virtual domains. \u003Cp>\u003C/p> \"One scenario is an AI model given a specific adversarial agenda. Stuxnet is perhaps an early example of sophisticated, AI-fueled, goal-seeking malware with an arsenal of zero-day attacks that ended up escaping onto the internet.\"*\u003Cp>\u003C/p>\n\nYou can focus your efforts in terms of security governance based on which areas your company is looking to adopt and the expected business benefits.\u003Cp>\u003C/p>\n\n### Frameworks for AI security governance\nFor effective AI security governance, we must navigate the complex landscape of guidelines and principles developed by various organizations.\n\nGovernments, international organizations, and tech companies have all played their part in shaping AI security governance frameworks. You can review the frameworks below and choose those that are relevant and/or apply to your organization:\n\n- [NIST AI Risk Management Framework (AI RMF)](https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf)\n- [Google’s Security Artificial Intelligence Framework](https://services.google.com/fh/files/blogs/google_secure_ai_framework_approach.pdf)\n- [OWASP Top 10 for LLMs](https://owasp.org/www-project-top-10-for-large-language-model-applications/assets/PDF/OWASP-Top-10-for-LLMs-2023-v1_0.pdf)\n- [The UK’s NCSC Principles for the Security of Machine Learning](https://www.ncsc.gov.uk/files/Principles-for-the-security-of-machine-learning.pdf)\n\nWhile these frameworks provide valuable guidance, they also introduce complexity. Organizations must determine which apply to their AI usage and how they align to their practices. Moreover, the dynamic nature of AI requires continuous adaptation to stay secure.\n\nSomething to note is that if you read through these frameworks, you’ll notice that numerous controls overlap with standard security best practices. This isn’t a coincidence. A strong overall security program is a prerequisite for proper AI security governance.\n\n## How-to: AI security governance\n### The why and the what\nAI security governance starts with understanding what AI technologies your organization is using or developing, why you are using them, and where these technologies fit into your operations. It's essential to define clear objectives and identify potential security risks associated with AI deployment. This introspection lays the foundation for effective AI security governance.\n\n#### The why\n\nUnderstanding the \"why\" behind each AI application is pivotal to build effective security governance. Each AI system deployed has to serve a specific purpose. Is AI being utilized to enhance customer experiences, automate manual tasks, or support the decision-making process? \n\nBy uncovering the motivations driving AI initiatives, organizations can align these projects with their broader business objectives. This alignment ensures that AI investments are strategically focused, delivering value in line with organizational goals. It also aids in prioritizing AI systems that have a more significant impact on the core mission of the company.\n\n#### The what\nIn the realm of AI security governance, the foundational step is conducting a comprehensive inventory of all AI systems, algorithms, and data sources within your organization. This includes meticulously cataloging all AI technologies in use, ranging from machine learning models and natural language processing algorithms to computer vision systems. This would also involve identifying the data sources feeding these AI systems, and their origins (internal databases, customer interactions, or third-party data providers). Such an inventory provides three main benefits: \n- to gain a holistic understanding of the AI ecosystem within the organization \n- to establish a strong basis for monitoring, auditing, and managing these assets effectively\n- to focus security efforts on the high-risk/critical areas\n\n### How to develop a security risk management program\nA robust security risk management program is at the core of responsible AI security governance. The critical building blocks for this program are the what and the why we discussed earlier. \n\nSpecificities of AI make security risk management more complex. In the NIST AI RMF mentioned earlier, numerous challenges are highlighted, including:\n\n- Difficult to measure AI-related security risks\n    - Potential security risks could emerge from the AI model, the software on which you are training the model, or the data ingested by the model. Different stages of the AI lifecycle might also trigger specific security risks depending on which actors (producers, developers, or consumers) are leveraging the AI solution.\n- Risk tolerance threshold might be complex to determine \n    - As the potential security risks aren’t easily identifiable, determining the risk tolerance your organization can withstand regarding AI can be a very empirical exercise.\n- Not considering AI in isolation \n    - Security governance of AI systems should be part of your security risk management strategy. Different users might have different parts of the overall picture. Ensuring you have complete information and full visibility into the AI lifecycle is critical to making the best decisions.\n\nSecurity risk management should be an ongoing process, adapting to the quickly evolving AI landscape. Reassessing the program, reviewing assumptions regarding the environment and involving additional business stakeholders are activities that should be happening on a regular basis.\n\n## AI security governance and the GitLab DevSecOps platform\n### Using AI to power DevSecOps \nLet’s take [GitLab Duo](https://about.gitlab.com/gitlab-duo-agent-platform/), our suite of AI capabilities to help power DevSecOps workflows, as an example. [GitLab Duo Code Suggestions](https://about.gitlab.com/solutions/code-suggestions/) helps developers write code more efficiently by using generative AI to assist in software engineering tasks. It works either through code completion or through code generation using natural language code comment blocks.\n\nTo ensure it can be fully leveraged, security needs of potential users and customers have to be considered. As an example, data used to produce Code Suggestions is immediately discarded by the AI models. \n\nAll of GitLab’s AI providers are subject to contractual terms with GitLab that prohibit the use of customer content for the provider’s own purposes, except to perform their independent legal obligations. [GitLab’s own privacy policy](https://about.gitlab.com/privacy/) prevents us from using customer data to train models without customer consent. \n\nOf course, to fully benefit from Code Suggestions, you should:\n- understand and review all suggestions to see if they align with your development guidelines\n- limit providing sensitive information or proprietary code in prompts \nensure the suggestion follows the same secure coding guidelines your company has\n- review the code using automated scanning for vulnerable dependencies, input validation and output sanitization, as well as license checks\n\n### Securing AI\nManaging the output of AI systems is equally important as managing the input. Security scanning tools can help identify vulnerabilities and potential threats in AI-generated code. \n\nManaging AI output requires a systematic approach to code review and validation. Organizations should [integrate security scanning tools into their CI/CD pipelines](https://docs.gitlab.com/user/application_security/), ensuring that AI-generated code is checked for security vulnerabilities before deployment. Automated security checks can help detect vulnerabilities early in the development process, reducing the risk of potential vulnerable code stemming from suggested code blocks being merged.\n\nFor any GitLab Duo generated code, changes are managed via merge requests which trigger your CI pipeline (including any security and code quality scanning you have configured). This ensures any governance rules you have set up for your merge requests like required approvals are enforced.\n\nAI systems are systems. Existing security controls apply to AI systems the same way they would apply to the rest of your environment. Common security controls around application security still apply, including [security reviews](https://docs.gitlab.com/user/project/merge_requests/reviews/data_usage/), security scanning, [threat modeling](https://danielmiessler.com/p/athi-an-ai-threat-modeling-framework-for-policymakers), encryption, etc. The [Google Secure AI Framework](https://services.google.com/fh/files/blogs/google_secure_ai_framework_approach.pdf) highlights these six elements:\n- expand strong security foundations to the AI ecosystem\n- extend detection and response to bring AI into an organization’s threat universe\n- automate defenses to keep pace with existing and new threats\n- harmonize platform-level controls to ensure consistent security across the organization\n- adapt controls to adjust mitigations and create faster feedback loops for AI deployment\n- contextualize AI system risks in surrounding business processes\n\nIf you have a strong security program, managing AI will be an extension of your current program and account for specific risks and vulnerabilities.\n\n## How GitLab Duo is secured\nGitLab recognizes the significance of security in AI governance. Our very strong security program is focused on ensuring our customers can fully leverage [GitLab Duo](https://docs.gitlab.com/user/ai_features/) in a secure manner. This is how the security departments are collaborating to secure GitLab’s AI features GitLab:\n- **Security Assurance:** Seeks to address our compliance requirements regarding security, that AI security risks are identified and properly managed, and that our customers understand how we secure our application, infrastructure, and services.\n\n- **Security Operations:** Monitors our infrastructure and quickly responds to threats using a team of skilled engineers as well as automation capabilities, helping to ensure AI features aren’t abused or used in a malevolent manner.\n\n- **Product Security:** Helps the product and engineering teams by providing security expertise for our AI features and helping to secure the underlying infrastructure on which our product is hosted.\n\n- **Corporate Security and IT Operations:** Finds potential vulnerabilities in our product to proactively mitigate and support other departments by performing research on relevant security areas.\n\nOur Security team works closely with GitLab's Legal and Corporate Affairs team to ensure our framework for AI security governance is comprehensive. The recent launch of the [GitLab AI Transparency Center](https://about.gitlab.com/blog/introducing-the-gitlab-ai-transparency-center/) showcases our commitment to implementing a strong AI governance. We published our AI ethics principles as well as our AI continuity plan to demonstrate our AI resiliency.\n\n## Learn more\nAI security governance is a complex area, especially as the field is in a nascent form. As AI continues to support our workflows and accelerate our processes, responsible AI security governance becomes a key pillar of any security program. By understanding the nuances of AI, enhancing your risk management program, and using AI features that are developed responsibly, you can ensure that AI-powered workflows follow the principles of security, privacy, and trust. \n\n>  Learn more about [GitLab Duo AI features](https://about.gitlab.com/gitlab-duo-agent-platform/).","ai-ml",{"template":13,"slug":14,"featured":15},"BlogPost","a-developers-guide-to-building-an-ai-security-governance-framework",true,{"title":5,"description":17,"authors":18,"heroImage":19,"tags":20,"category":11,"date":25,"body":10},"Learn the strategies and practices to adopt for secure and responsible development and use of AI.",[9],"https://res.cloudinary.com/about-gitlab-com/image/upload/v1749664638/Blog/Hero%20Images/applicationsecurity.png",[21,22,23,24],"AI/ML","DevSecOps","security","public sector","2024-04-23","md",null,{},"/en-us/blog/a-developers-guide-to-building-an-ai-security-governance-framework","---\nseo:\n  title: A developer's guide to building an AI security governance framework\n  description: >-\n    Learn the strategies and practices to adopt for secure and responsible\n    development and use of AI.\n  ogTitle: A developer's guide to building an AI security governance framework\n  ogDescription: >-\n    Learn the strategies and practices to adopt for secure and responsible\n    development and use of AI.\n  noIndex: false\n  ogImage: >-\n    https://res.cloudinary.com/about-gitlab-com/image/upload/v1749664638/Blog/Hero%20Images/applicationsecurity.png\n  ogUrl: >-\n    https://about.gitlab.com/blog/a-developers-guide-to-building-an-ai-security-governance-framework\n  ogSiteName: https://about.gitlab.com\n  ogType: article\n  canonicalUrls: >-\n    https://about.gitlab.com/blog/a-developers-guide-to-building-an-ai-security-governance-framework\ntitle: A developer's guide to building an AI security governance framework\ndescription: Learn the strategies and practices to adopt for secure and responsible development and use of AI.\nauthors:\n  - Ayoub Fandi\nheroImage: https://res.cloudinary.com/about-gitlab-com/image/upload/v1749664638/Blog/Hero%20Images/applicationsecurity.png\ntags:\n  - AI/ML\n  - DevSecOps\n  - security\n  - public sector\ncategory: ai-ml\ndate: '2024-04-23'\nslug: a-developers-guide-to-building-an-ai-security-governance-framework\nfeatured: true\ntemplate: BlogPost\n---\n\nArtificial Intelligence (AI) has firmly established itself as a pillar of digital transformation, disrupting industries, increasing efficiency, and providing unmatched access to large data sets. AI also raises profound questions regarding security governance. How do I ensure I can leverage the best of what AI has to offer while mitigating its potential security risks? As [AI continues to advance](https://about.gitlab.com/topics/devops/the-role-of-ai-in-devops/), there is a growing need for strong oversight and accountability. This article delves into the complex landscape of AI security governance, exploring various frameworks, strategies, and practices that organizations like GitLab are adopting to ensure the responsible development of AI technologies and features.\n\n## Greater scrutiny on AI\n\n### AI: Single term, numerous realities\nAI isn't a monolithic entity - it encompasses a spectrum of technologies and applications. From machine learning algorithms that power recommendation systems to advanced natural language processing models like Anthropic’s Claude 3, each AI system brings its unique set of opportunities and challenges.\n\nAccording to [a 2023 MITRE report](https://www.mitre.org/sites/default/files/2023-06/PR-23-1943-A-Sensible-Regulatory-Framework-For-AI-Security_0.pdf), three main areas of AI currently exist:\n\n1. **AI as a subsystem**\n\n\u003Cp>\u003C/p>*\"AI is embedded in many software systems. Discrete AI models routinely perform machine perception and optimization functions, from face recognition in photos uploaded to the cloud, to dynamically allocating and optimizing network resources in 5G wireless networks. \u003Cp>\u003C/p> \"There are a wide range of vulnerabilities and threats against these types of AI subsystems – from data poisoning attacks to adversarial input attacks – that can be used to manipulate subsystems.\"*\u003Cp>\u003C/p>\n\n2. **AI as human augmentation**\n\u003Cp>\u003C/p>*\"Another application of AI is in augmenting human performance, allowing a person to operate with much larger scope and scale. This has wide-ranging implications for workforce planning as AI has the potential to increase productivity and shift the composition of labor markets, similar to the role of automation in the manufacturing industry. \u003Cp>\u003C/p> \"While sophisticated hackers and military information operations can already generate believable content today using techniques such as computer-generated imagery, LLMs will make that capability available to anyone, while increasing the scope and scale at which the professionals can operate.\"*\u003Cp>\u003C/p>\n\n3. **AI with agency**\n\u003Cp>\u003C/p>*\"A segment of the tech community is increasingly concerned about scenarios where sophisticated AI could operate as an independent, goal-seeking agent. While science fiction historically embodied this AI in anthropomorphic robots, the AI we have today is principally confined to digital and virtual domains. \u003Cp>\u003C/p> \"One scenario is an AI model given a specific adversarial agenda. Stuxnet is perhaps an early example of sophisticated, AI-fueled, goal-seeking malware with an arsenal of zero-day attacks that ended up escaping onto the internet.\"*\u003Cp>\u003C/p>\n\nYou can focus your efforts in terms of security governance based on which areas your company is looking to adopt and the expected business benefits.\u003Cp>\u003C/p>\n\n### Frameworks for AI security governance\nFor effective AI security governance, we must navigate the complex landscape of guidelines and principles developed by various organizations.\n\nGovernments, international organizations, and tech companies have all played their part in shaping AI security governance frameworks. You can review the frameworks below and choose those that are relevant and/or apply to your organization:\n\n- [NIST AI Risk Management Framework (AI RMF)](https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf)\n- [Google’s Security Artificial Intelligence Framework](https://services.google.com/fh/files/blogs/google_secure_ai_framework_approach.pdf)\n- [OWASP Top 10 for LLMs](https://owasp.org/www-project-top-10-for-large-language-model-applications/assets/PDF/OWASP-Top-10-for-LLMs-2023-v1_0.pdf)\n- [The UK’s NCSC Principles for the Security of Machine Learning](https://www.ncsc.gov.uk/files/Principles-for-the-security-of-machine-learning.pdf)\n\nWhile these frameworks provide valuable guidance, they also introduce complexity. Organizations must determine which apply to their AI usage and how they align to their practices. Moreover, the dynamic nature of AI requires continuous adaptation to stay secure.\n\nSomething to note is that if you read through these frameworks, you’ll notice that numerous controls overlap with standard security best practices. This isn’t a coincidence. A strong overall security program is a prerequisite for proper AI security governance.\n\n## How-to: AI security governance\n### The why and the what\nAI security governance starts with understanding what AI technologies your organization is using or developing, why you are using them, and where these technologies fit into your operations. It's essential to define clear objectives and identify potential security risks associated with AI deployment. This introspection lays the foundation for effective AI security governance.\n\n#### The why\n\nUnderstanding the \"why\" behind each AI application is pivotal to build effective security governance. Each AI system deployed has to serve a specific purpose. Is AI being utilized to enhance customer experiences, automate manual tasks, or support the decision-making process? \n\nBy uncovering the motivations driving AI initiatives, organizations can align these projects with their broader business objectives. This alignment ensures that AI investments are strategically focused, delivering value in line with organizational goals. It also aids in prioritizing AI systems that have a more significant impact on the core mission of the company.\n\n#### The what\nIn the realm of AI security governance, the foundational step is conducting a comprehensive inventory of all AI systems, algorithms, and data sources within your organization. This includes meticulously cataloging all AI technologies in use, ranging from machine learning models and natural language processing algorithms to computer vision systems. This would also involve identifying the data sources feeding these AI systems, and their origins (internal databases, customer interactions, or third-party data providers). Such an inventory provides three main benefits: \n- to gain a holistic understanding of the AI ecosystem within the organization \n- to establish a strong basis for monitoring, auditing, and managing these assets effectively\n- to focus security efforts on the high-risk/critical areas\n\n### How to develop a security risk management program\nA robust security risk management program is at the core of responsible AI security governance. The critical building blocks for this program are the what and the why we discussed earlier. \n\nSpecificities of AI make security risk management more complex. In the NIST AI RMF mentioned earlier, numerous challenges are highlighted, including:\n\n- Difficult to measure AI-related security risks\n    - Potential security risks could emerge from the AI model, the software on which you are training the model, or the data ingested by the model. Different stages of the AI lifecycle might also trigger specific security risks depending on which actors (producers, developers, or consumers) are leveraging the AI solution.\n- Risk tolerance threshold might be complex to determine \n    - As the potential security risks aren’t easily identifiable, determining the risk tolerance your organization can withstand regarding AI can be a very empirical exercise.\n- Not considering AI in isolation \n    - Security governance of AI systems should be part of your security risk management strategy. Different users might have different parts of the overall picture. Ensuring you have complete information and full visibility into the AI lifecycle is critical to making the best decisions.\n\nSecurity risk management should be an ongoing process, adapting to the quickly evolving AI landscape. Reassessing the program, reviewing assumptions regarding the environment and involving additional business stakeholders are activities that should be happening on a regular basis.\n\n## AI security governance and the GitLab DevSecOps platform\n### Using AI to power DevSecOps \nLet’s take [GitLab Duo](https://about.gitlab.com/gitlab-duo-agent-platform/), our suite of AI capabilities to help power DevSecOps workflows, as an example. [GitLab Duo Code Suggestions](https://about.gitlab.com/solutions/code-suggestions/) helps developers write code more efficiently by using generative AI to assist in software engineering tasks. It works either through code completion or through code generation using natural language code comment blocks.\n\nTo ensure it can be fully leveraged, security needs of potential users and customers have to be considered. As an example, data used to produce Code Suggestions is immediately discarded by the AI models. \n\nAll of GitLab’s AI providers are subject to contractual terms with GitLab that prohibit the use of customer content for the provider’s own purposes, except to perform their independent legal obligations. [GitLab’s own privacy policy](https://about.gitlab.com/privacy/) prevents us from using customer data to train models without customer consent. \n\nOf course, to fully benefit from Code Suggestions, you should:\n- understand and review all suggestions to see if they align with your development guidelines\n- limit providing sensitive information or proprietary code in prompts \nensure the suggestion follows the same secure coding guidelines your company has\n- review the code using automated scanning for vulnerable dependencies, input validation and output sanitization, as well as license checks\n\n### Securing AI\nManaging the output of AI systems is equally important as managing the input. Security scanning tools can help identify vulnerabilities and potential threats in AI-generated code. \n\nManaging AI output requires a systematic approach to code review and validation. Organizations should [integrate security scanning tools into their CI/CD pipelines](https://docs.gitlab.com/user/application_security/), ensuring that AI-generated code is checked for security vulnerabilities before deployment. Automated security checks can help detect vulnerabilities early in the development process, reducing the risk of potential vulnerable code stemming from suggested code blocks being merged.\n\nFor any GitLab Duo generated code, changes are managed via merge requests which trigger your CI pipeline (including any security and code quality scanning you have configured). This ensures any governance rules you have set up for your merge requests like required approvals are enforced.\n\nAI systems are systems. Existing security controls apply to AI systems the same way they would apply to the rest of your environment. Common security controls around application security still apply, including [security reviews](https://docs.gitlab.com/user/project/merge_requests/reviews/data_usage/), security scanning, [threat modeling](https://danielmiessler.com/p/athi-an-ai-threat-modeling-framework-for-policymakers), encryption, etc. The [Google Secure AI Framework](https://services.google.com/fh/files/blogs/google_secure_ai_framework_approach.pdf) highlights these six elements:\n- expand strong security foundations to the AI ecosystem\n- extend detection and response to bring AI into an organization’s threat universe\n- automate defenses to keep pace with existing and new threats\n- harmonize platform-level controls to ensure consistent security across the organization\n- adapt controls to adjust mitigations and create faster feedback loops for AI deployment\n- contextualize AI system risks in surrounding business processes\n\nIf you have a strong security program, managing AI will be an extension of your current program and account for specific risks and vulnerabilities.\n\n## How GitLab Duo is secured\nGitLab recognizes the significance of security in AI governance. Our very strong security program is focused on ensuring our customers can fully leverage [GitLab Duo](https://docs.gitlab.com/user/ai_features/) in a secure manner. This is how the security departments are collaborating to secure GitLab’s AI features GitLab:\n- **Security Assurance:** Seeks to address our compliance requirements regarding security, that AI security risks are identified and properly managed, and that our customers understand how we secure our application, infrastructure, and services.\n\n- **Security Operations:** Monitors our infrastructure and quickly responds to threats using a team of skilled engineers as well as automation capabilities, helping to ensure AI features aren’t abused or used in a malevolent manner.\n\n- **Product Security:** Helps the product and engineering teams by providing security expertise for our AI features and helping to secure the underlying infrastructure on which our product is hosted.\n\n- **Corporate Security and IT Operations:** Finds potential vulnerabilities in our product to proactively mitigate and support other departments by performing research on relevant security areas.\n\nOur Security team works closely with GitLab's Legal and Corporate Affairs team to ensure our framework for AI security governance is comprehensive. The recent launch of the [GitLab AI Transparency Center](https://about.gitlab.com/blog/introducing-the-gitlab-ai-transparency-center/) showcases our commitment to implementing a strong AI governance. We published our AI ethics principles as well as our AI continuity plan to demonstrate our AI resiliency.\n\n## Learn more\nAI security governance is a complex area, especially as the field is in a nascent form. As AI continues to support our workflows and accelerate our processes, responsible AI security governance becomes a key pillar of any security program. By understanding the nuances of AI, enhancing your risk management program, and using AI features that are developed responsibly, you can ensure that AI-powered workflows follow the principles of security, privacy, and trust. \n\n>  Learn more about [GitLab Duo AI 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will train on your data: Opt out with GitLab","Learn why Atlassian's latest move is a threat to data governance and how GitLab's approach helps ensure your customers' data stays private and protected.",[734],"Jessica Hurwitz","2026-05-04","https://res.cloudinary.com/about-gitlab-com/image/upload/v1773866173/vte9qh8rriznvyclhkes.png","Starting August 17, 2026, Atlassian will begin collecting customer metadata and in-app content from Jira, Confluence, and other cloud products to train its AI offerings, including Rovo and Rovo Dev. This announcement comes after [GitHub recently changed its Copilot data usage policy](https://about.gitlab.com/blog/github-copilots-new-policy-for-ai-training-is-a-governance-wake-up-call/). **Taken together, these changes suggest opt-out-by-default is becoming the industry norm. GitLab takes the opposite position: no data collection, no AI training on customer data, no matter what tier you're on.**\n\n[Atlassian's change](https://www.atlassian.com/trust/ai/data-contribution) is enabled by default for all cloud customers and affects roughly 300,000 organizations. For customers on the Free, Standard, and Premium tiers, metadata collection is mandatory and cannot be turned off. Only Enterprise-tier customers have the option to opt out. This policy change deserves a close read if your engineering, IT, and program management teams run on Atlassian because they are most exposed by this change — and least likely to have been consulted before it happened.\n\nAlthough the underlying governance questions are the same for both Atlassian and GitHub's changes, the data at risk is different. Where GitHub's change concerned source code and developer interactions, Atlassian's reaches into project plans, internal documentation, workflow configurations, and operational metadata across Jira, Confluence, and the broader Atlassian stack. **For organizations that rely on these tools as their system of record for how work gets planned and delivered, the implications run deep.**\n\n## What changed and what it means for your data\n\nAtlassian will collect two categories of information: \n\n- **Metadata:** de-identified operational signals like story points, sprint dates, and SLA values, including data from its Teamwork Graph and connected third-party apps  \n- **In-app content:** user-generated material such as Confluence page content, Jira issue titles, descriptions, and comments\n\nAtlassian says it will apply de-identification and aggregation before training. Collected data may be retained for up to seven years, with in-app data removed within 30 days of opt-out and models retrained within 90 days.\n\nThere are some exclusions: Customers using customer-managed encryption keys, Atlassian Government Cloud, Isolated Cloud, or those with HIPAA requirements are carved out from collection. But for the vast majority of Atlassian's cloud customer base, data collection will start unless you pay for the Enterprise tier and actively flip the switch.\n\nThis reverses Atlassian's prior stated position that customer data would not be used to train or improve AI services. Organizations that adopted Jira and Confluence to manage their most sensitive planning workflows, sprint boards, security tickets, incident postmortems, and internal documentation will soon be contributing that content to Atlassian's AI training pipeline, without ever being asked.\n\n## The governance gap in \"opt-out by default\"\n\nOpt-out-by-default data collection for AI training is an emerging pattern across the software industry. It raises the same set of questions every time: How does this interact with existing data processing agreements? Does the vendor's definition of \"metadata\" match what your legal and security teams would consider non-sensitive data?\n\n**For many organizations, the answer to these questions is \"we don't know.\"** \n\nWhen a vendor changes its data practices through a terms-of-service update, the burden falls on the customer to notice, evaluate the implications, and act within the window the vendor provides. \n\nThe mandatory nature of metadata collection on Free, Standard, and Premium tiers makes this more acute. The only exit is upgrading to Enterprise, which requires a minimum of 801 users and custom pricing that would represent a significant cost jump for teams that aren't there yet. Data protection, in other words, is now a purchasing decision.\n\nThe tiered structure also introduces a subtler problem. Metadata like story points, sprint velocity, SLA metrics, and task classifications may seem innocuous in isolation, but in aggregate they reveal project structure, team performance patterns, and delivery cadence. For organizations in competitive industries, that operational intelligence has real value, and \"de-identified\" does not necessarily mean \"non-sensitive\" once patterns are reconstructable at scale.\n\n## Why this matters more for Atlassian-stack organizations\n\nIn Atlassian-based organizations, Jira has been the center how teams plan, track, and deliver work. It’s the source of truth for sprint planning, bug tracking, release management, portfolio coordination, and cross-functional project execution. \n\nIn regulated industries like financial services, public sector and manufacturing, Jira and Confluence together hold sensitive operational data that may be subject to compliance requirements. The risk compounds for organizations that have expanded beyond Jira into the broader Atlassian ecosystem.\n\nWhen you run Jira, Confluence, Bitbucket, and Bamboo together, the surface area of data now feeding into AI training spans your project plans, internal documentation, source code metadata, and CI/CD configurations — each of which security and compliance teams would want to review before sharing with a vendor's training pipeline.\n\nAtlassian’s Teamwork Graph connectors add another dimension for customers who have integrated third-party tools, such as Slack, Figma, Google Drive, Salesforce, and ServiceNow, into their environment. Teamwork Graph connectors index relationship and activity signals from these connected apps, which means the metadata Atlassian collects will not be limited to what lives inside Atlassian products. For security and compliance teams accustomed to evaluating data flows on a per-vendor basis, this cross-platform reach complicates the assessment considerably.\n\nOrganizations that are already navigating [Atlassian's push from Data Center](https://about.gitlab.com/blog/atlassian-ending-data-center-as-gitlab-maintains-deployment-choice/) and Server editions to the cloud face a compounding challenge. Adding default AI data collection to that migration path raises the stakes further: **The question is no longer just \"do we move to Atlassian Cloud?\" but \"do we move to Atlassian Cloud knowing our data will feed AI training unless we're on the most expensive tier?\"**\n\n## What regulated industries should be evaluating now\n\nThe compliance implications vary by sector, but the obligation to reassess is consistent.\n\nIn financial services, frameworks like [SR 11-7](https://www.federalreserve.gov/supervisionreg/srletters/sr1107.htm) and [DORA](https://eur-lex.europa.eu/eli/reg/2022/2554/oj/eng) require documented, auditable oversight of third-party technology providers, including how those providers handle data. In the public sector, [NIST 800-53](https://csrc.nist.gov/publications/detail/sp/800-53/rev-5/final) and [FISMA](https://www.cisa.gov/topics/cyber-threats-and-advisories/federal-information-security-modernization-act) make controlling where sensitive data flows a foundational requirement. In healthcare, [HIPAA](https://www.hhs.gov/hipaa/index.html) governs how patient-adjacent data is handled by third parties. \n\nAcross the board, a material change in a vendor's data practices, such as Atlassian moving from \"we don't train on your data\" to \"we do, by default,\" triggers a documentation and risk reassessment obligation. \n\nInstitutions operating under the [EU AI Act](https://eur-lex.europa.eu/eli/reg/2024/1689/oj/eng) face an additional dimension: opt-out framing aligns with U.S. norms, while European regulators generally expect opt-in consent for data processing of this nature.\n\nIf your model risk or vendor management team documented Atlassian's data handling controls before this announcement, the question isn't whether this change triggers a reassessment obligation. It does. The question is whether your team can take action before August 17.\n\n## What to look for in your platform vendors\n\nCTOs and CISOs across regulated industries need to adopt AI in a way they can explain to regulators, boards, and customers. Because of this, GitLab operates within the following set of principles:\n\n**Unconditional data commitments, not tier-dependent protections.** Regulated organizations need to know, with specificity, what happens to their data. A commitment that varies by plan tier, or that requires action before a deadline, introduces exactly the kind of uncontrolled variable that keeps CISOs up at night.\n\n**Transparency and auditability.** Model risk management frameworks require organizations to understand the AI systems they deploy, including the training data and third parties involved. Vendors who cannot answer these questions clearly create documentation risk.\n\n**Separation between customer data and vendor AI training.** When a platform vendor trains models on customer usage data, workflows and operational patterns become inputs to a system that also serves competitors. For organizations where project structure or delivery cadence represents competitive advantage, that exposure matters.\n\n## How GitLab's approach differs\n\nGitLab doesn't train on customer data — at any tier, full stop. AI vendors powering GitLab Duo features are contractually prohibited from using customer inputs or outputs for their own purposes, [a commitment GitLab CEO Bill Staples](https://www.linkedin.com/posts/williamstaples_gitlab-1810-agentic-ai-now-open-to-even-activity-7443280763715985408-aHxf) has consistently reiterated.\n\n[GitLab's AI Transparency Center](https://about.gitlab.com/ai-transparency-center/) documents exactly which models power which features, how data is handled, and what vendor commitments are in place. [GitLab's AI Continuity Plan](https://handbook.gitlab.com/handbook/product/ai/continuity-plan/) documents how vendor changes are managed, including any material changes to how AI vendors treat customer data. For institutions managing third-party AI risk under DORA or similar frameworks, vendor continuity and concentration are active governance concerns, and having a documented plan for both is part of what responsible AI tooling looks like.\n\nFor organizations that require AI processing to stay within their own infrastructure, [GitLab Duo Agent Platform](https://about.gitlab.com/gitlab-duo/) is available with GitLab Self-Managed deployments, including support for integration with self-hosted AI models. This means prompts and code never leave the customer's environment. GitLab also provides IP indemnification for Duo-generated output, with no filters required and no activation steps needed. Where your data lives remains your choice, no matter your deployment model or subscription tier.\n\n> Whether your organization stays on Atlassian or begins evaluating alternatives, the conversation about who controls your data and how it gets used should be happening now. **The August 17 deadline is approaching, but you still have time to [try GitLab Ultimate with Duo Agent Platform for free today](https://gitlab.com/-/trials/new).**",[21,739],"product",{"featured":15,"template":13,"slug":741},"atlassian-will-train-on-your-data-opt-out-with-gitlab",{"content":743,"config":752},{"title":744,"description":745,"authors":746,"heroImage":748,"date":749,"body":750,"category":11,"tags":751},"GitLab and Anthropic: Governed AI for enterprise development","GitLab deepens its Anthropic Claude integration, bringing governed AI, access to new models, and cloud flexibility to enterprise software development.",[747],"Stuart Moncada","https://res.cloudinary.com/about-gitlab-com/image/upload/v1776457632/llddiylsgwuze0u1rjks.png","2026-04-28","For enterprise and public sector leaders, the tension is familiar: Software teams need to move faster with AI, while security, compliance, and regulatory expectations only get more stringent. GitLab deepens its Anthropic Claude integration so organizations get access to newly released Claude models inside GitLab’s intelligent orchestration platform where governance, compliance, and auditability already run.\n\nClaude powers capabilities across GitLab Duo Agent Platform as the default model out of the box, across a variety of use cases from code generation and review to agentic chat and vulnerability resolution. If you've used GitLab Duo, you've already experienced how Duo agents automate workflows across the entire software development lifecycle (SDLC).\n\nThis accelerates the integration of Claude’s capabilities into GitLab, broadens how enterprises can deploy them, and reinforces what makes GitLab fundamentally different as a platform for software development and engineering: governance, compliance, and auditability built into every AI interaction.\n\n> \"GitLab Duo has accelerated how our teams plan, build, and ship software. The combination of Anthropic's Claude and GitLab's platform means we're getting more capable AI without changing how we work or how it is governed.\"\n>\n> – Mans Booijink, Operations Manager, Cube\n\n## The real differentiator: Governed AI\n\nWith GitLab, governance controls and auditing are built into the SDLC. When Claude suggests a code change through the GitLab Duo Agent Platform, that suggestion flows through the same merge request process, the same approval rules, the same security scanning, and the same audit trail as every other change. AI doesn't get a shortcut around your controls. It operates within them.\n\nAs GitLab moves deeper into agentic software development, where AI autonomously handles well-defined tasks, the governance layer becomes more important. An AI agent that can open a merge request, help resolve a vulnerability, or refactor a service needs to be auditable, attributable, and subject to the same policy enforcement as a human developer. That requirement is an architectural decision GitLab made from the start, and one that grows more consequential as AI agents take on broader responsibilities.\n\n## Enterprise deployment flexibility\n\nThis also expands how organizations access the latest Claude models through GitLab. Claude is available within GitLab through Google Cloud's Vertex AI and Amazon Bedrock, which means enterprises can route AI workloads through the hyperscaler commitments and cloud governance frameworks they already have in place. No separate vendor contract. No new data residency questions. Your existing Google Cloud or AWS relationship is the on-ramp. \n\nGitLab is now also available in the [Claude Marketplace](https://claude.com/platform/marketplace), allowing customers to purchase GitLab Credits and apply them toward existing Anthropic spending commitments – consolidating AI spend and simplifying how teams discover and procure GitLab alongside their Anthropic investments.\n\n## Advancing an agentic future\n\nGitLab's vision for agentic software development, where AI handles defined tasks autonomously across planning, coding, testing, securing, and deploying, requires models with strong reasoning, reliability, and safety characteristics. It also requires a platform where those autonomous actions are fully governed.\n\nAgentic workflows demand models with strong reasoning, reliability, and safety characteristics, criteria that guide how GitLab selects and integrates AI model partners. And GitLab's governance framework helps ensure that as AI agents assume more advanced development work, enterprises maintain full visibility and control over what those agents do, when they do it, and how changes are tracked.\n\n## What this means for GitLab customers\n\nIf you're already using GitLab Duo Agent Platform, you'll get access to Claude models and deeper AI assistance across your software development lifecycle, all within the governance framework you already rely on.\n\nIf you're evaluating AI-powered software development platforms, you shouldn't have to choose between advanced AI capabilities and enterprise control. This strategic integration is built to deliver both.\n\n> Want to learn more about GitLab Duo Agent Platform? [Get a demo or start a free trial today](https://about.gitlab.com/gitlab-duo-agent-platform/).",[21,739,290],{"featured":15,"template":13,"slug":753},"gitlab-and-anthropic-governed-ai-for-enterprise-development",{"content":755,"config":765},{"title":756,"description":757,"authors":758,"heroImage":760,"date":761,"body":762,"category":11,"tags":763},"Give your AI agent direct, structured GitLab access with glab CLI","The GitLab CLI (glab) provides AI agents structured, reliable access to projects via the MCP, eliminating friction. This tutorial shows how you can speed up code review and issue triage.",[759],"Kai Armstrong","https://res.cloudinary.com/about-gitlab-com/image/upload/v1776347152/unw3mzatkd5xyfbzcnni.png","2026-04-27","\nWhen teams use GitLab Duo, Claude, Cursor, and other AI assistants, more of the development workflow runs through an AI agent acting on your behalf — reading issues, reviewing merge requests, running pipelines, and helping you ship faster. Most developers are already using the GitLab CLI (`glab`) from the terminal to interact with GitLab. Combining the two is a natural next step.\n\n\nThe problem is that without the right tools, AI agents are essentially guessing when it comes to your GitLab projects. They might hallucinate the details of an issue they've never seen, summarize a merge request based on stale training data rather than its actual state, or require you to manually copy context from a browser tab and paste it into a chat window just to get started. Every one of those workarounds is friction: it slows you down, introduces the possibility of error, and puts a hard ceiling on what your agent can actually do on your behalf. `glab` changes that by giving agents a direct, reliable interface to your projects.\n\n\nWith `glab`, your agent fetches what it needs directly from GitLab, acts on it, and reports back — so you spend less time relaying information and more time on the work that matters.\n\n\nIn this tutorial, you'll learn how to use `glab` to give AI agents structured, reliable access to your GitLab projects. You'll also discover how that unlocks a faster, more capable development workflow.\n\n\n## How to connect your AI agent to GitLab through MCP\n\n\nThe most direct way to supercharge your AI workflow is to give your AI agent native access to `glab` through Model Context Protocol ([MCP](https://about.gitlab.com/topics/ai/model-context-protocol/)).\n\n\n MCP is an open standard that lets AI tools discover and use external capabilities at runtime. Once connected, your AI assistant can read issues, comment on merge requests, check pipeline status, and write back to GitLab, all without copying anything from the UI or writing a single API call yourself.\n\n\n To get started, run:\n\n\n ```shell\n # Start the glab MCP server\n glab mcp serve\n ```\n\n\n Once your MCP client is configured, your AI can answer questions like *\"What's the status of my open MRs?\"* or *\"Are there any failing pipelines on main?\"* by querying GitLab directly, not scraping the web UI, not relying on stale training data. See the [full setup docs](https://docs.gitlab.com/cli/) for configuration steps for Claude Code, Cursor, and other editors.\n\n\n One detail worth knowing: `glab` automatically adds `--output json` when invoked through MCP, for any command that supports it. Your agent gets clean, structured data without you needing to think about output formats. And because `glab` uses the official MCP SDK, it stays compatible as the\n protocol evolves.\n\n\n We've also been deliberate about *which* commands are exposed through MCP. Commands that require interactive terminal input are intentionally\n excluded, so your agent never gets stuck waiting for input that will never come. What's exposed is what actually works reliably in an agent context.\n\n\n ## Let your AI participate in code review\n\n\n Most developers have a backlog of MRs waiting for review. It's one of the most time-consuming parts of the job and one of the best places to put\n AI to work. With `glab`, your agent doesn't just observe your review queue, it can work through it with you.\n\n\n ### See exactly what still needs addressing\n\n\n Start with this:\n\n\n ```shell\n glab mr view 2677 --comments --unresolved --output json\n ```\n\n\n This input returns the full MR: metadata, description, and every\n unresolved discussion, as a single structured JSON payload. Hand that to\n your AI and it has everything it needs: which threads are open, what the\n reviewer asked for, and in what context. No tab-switching, no copy-pasting\n individual comments.\n\n\n \n ```json\n {\n   \"id\": 2677,\n   \"title\": \"feat: add OAuth2 support\",\n   \"state\": \"opened\",\n   \"author\": { \"username\": \"jdwick\" },\n   \"labels\": [\"backend\", \"needs-review\"],\n   \"blocking_discussions_resolved\": false,\n   \"discussions\": [\n     {\n       \"id\": \"3107030349\",\n       \"resolved\": false,\n       \"notes\": [\n         {\n           \"author\": { \"username\": \"dmurphy\" },\n           \"body\": \"This error handling will swallow panics — consider wrapping with recover()\",\n           \"created_at\": \"2026-03-14T09:23:11.000Z\"\n         }\n       ]\n     },\n     {\n       \"id\": \"3107030412\",\n       \"resolved\": false,\n       \"notes\": [\n         {\n           \"author\": { \"username\": \"sreeves\" },\n           \"body\": \"Token refresh logic needs a test for the expired token case\",\n           \"created_at\": \"2026-03-14T10:05:44.000Z\"\n         }\n       ]\n     }\n   ]\n }\n ```\n\n\n Instead of reading through every thread yourself, you ask your agent  *\"what do I still need to fix in MR 2677?\"* and get back a prioritized summary with suggested changes. This all happens from a single command.\n\n\n ### Close the loop programmatically\n\n\n Once your AI has helped you address the feedback, it can resolve\n discussions:\n\n\n ```shell\n # List all discussions — structured, ready for the agent to process\n glab mr note list 456 --output json\n\n # Resolve a discussion once the feedback is addressed\n glab mr note resolve 456 3107030349\n\n # Reopen if something needs another look\n glab mr note reopen 456 3107030349\n ```\n\n\n\n ```json\n [\n   {\n     \"id\": 3107030349,\n     \"body\": \"This error handling will swallow panics — consider wrapping with recover()\",\n     \"author\": { \"username\": \"dmurphy\" },\n     \"resolved\": false,\n     \"resolvable\": true\n   },\n   {\n     \"id\": 3107030412,\n     \"body\": \"Token refresh logic needs a test for the expired token case\",\n     \"author\": { \"username\": \"sreeves\" },\n     \"resolved\": false,\n     \"resolvable\": true\n   }\n ]\n ```\n\n\n\n Note IDs are visible directly in the GitLab UI and API, no extra lookup needed. Your agent can work through the full list, verify each fix, and\n resolve as it goes.\n\n\n ## Talk to your AI about your code more effectively\n\n\n Even if you're not running an MCP server, there's a simpler shift that makes a huge difference: using `glab` to feed your AI better information.\n\n\n Think about the last time you asked an AI assistant to help triage issues or debug a failing pipeline. You probably copied some text from the GitLab UI and pasted it into the chat. Here's what your agent is actually\n working with when you do that:\n\n\n ```text\n open issues: 12 • milestone: 17.10 • label: bug, needs-triage ...\n ```\n\n\n Compare that to what it gets with `glab`:\n\n\n \n ```json\n [\n   {\n     \"iid\": 902,\n     \"title\": \"Pipeline fails on merge to main\",\n     \"labels\": [\"bug\", \"needs-triage\"],\n     \"milestone\": { \"title\": \"17.10\" },\n     \"assignees\": []\n   },\n   ...\n ]\n ```\n\n\n Structured, typed, complete; no ambiguity, no parsing guesswork. That's the difference between an agent that can act and one that has to ask\n follow-up questions.\n\n\n If you're using the MCP server, you get this automatically: `glab` adds `--output json` for any command that supports it. If you're working directly\n from the terminal, just add the flag yourself:\n\n\n ```shell\n # Pull open issues for triage\n glab issue list --label \"needs-triage\" --output json\n\n # Check pipeline status\n glab ci status --output json\n\n # Get full MR details\n glab mr view 456 --output json\n ```\n\n\n We've significantly expanded JSON output support in recent releases. It now covers CI status, milestones, labels, releases, schedules, cluster agents, work items, MR approvers, repo contributors, and more. If `glab` can\n retrieve it, your AI can consume it cleanly.\n\n\n ### A real workflow\n\n\n ```shell\n $ glab issue list --label \"needs-triage\" --milestone \"17.10\"\n --output json\n ```\n\n\n ```text\n Agent: I found 2 unassigned bugs in the 17.10 milestone that need triage:\n 1. #902 — Pipeline fails on merge to main (opened 5 days ago)\n 2. #903 — Auth token not refreshing on expiry (opened 4 days ago)\n Both are unassigned. Want me to draft triage notes and suggest assignees based on recent commit history?\n ```\n\n\n ## Your agent is never limited to built-in commands\n\n\n `glab`'s first-class commands cover the most common workflows, but your agent is never limited to them. Through `glab api`, it has authenticated access to the full GitLab REST and GraphQL API surface, using the same session, with no extra credentials or configuration required.\n\n\n This is a meaningful differentiator. Most CLI tools stop at what their commands expose. With `glab`, if GitLab's API supports it, your agent can do it. It's always working from a trusted, authenticated context.\n\n\n A practical example: fetching just the list of changed files in an MR before deciding which diffs to pull in full:\n\n\n ```shell\n # Get changed file paths — lightweight, no diff content yet\n glab api \"/projects/$CI_PROJECT_ID/merge_requests/$CI_MERGE_REQUEST_IID/diffs?per_page=100\" \\\n | jq '.[].new_path'\n\n# Then fetch only the specific file your agent needs\nglab api \"/projects/$CI_PROJECT_ID/merge_requests/$CI_MERGE_REQUEST_IID/diffs?per_page=100\" \\\n| jq '.[] | select(.new_path == \"path/to/file.go\")'\n ```\n\n\n ```text\n \"internal/auth/token.go\"\n \"internal/auth/token_test.go\"\n \"internal/oauth/refresh.go\"\n ```\n\n\n For anything the REST API doesn't cover (epics, certain work item queries, complex cross-project data),  `glab api graphql` gives you the full\n GraphQL interface:\n\n\n ```shell\n   glab api graphql -f query='\n {\n   project(fullPath: \"gitlab-org/gitlab\") {\n     mergeRequest(iid: \"12345\") {\n       title\n       reviewers { nodes { username } }\n     }\n   }\n }'\n ```\n\n ```json\n{\n   \"data\": {\n     \"project\": {\n       \"mergeRequest\": {\n         \"title\": \"feat: add OAuth2 support\",\n         \"reviewers\": {\n           \"nodes\": [\n             { \"username\": \"dmurphy\" },\n             { \"username\": \"sreeves\" }\n           ]\n         }\n       }\n     }\n   }\n }\n\n ```\n\n\n Your agent has a single, authenticated entry point to everything GitLab exposes without the token juggling, separate API clients, or configuration\n overhead.\n\n\n ## What's coming and your feedback\n\n\n Two improvements we're actively working on will make `glab` even more useful for agent workflows:\n\n\n **Agent-aware help text.** Today, `--help` output is written for humansvat a terminal. We're updating it to surface the non-interactive alternative\n for every interactive command, flag which commands support `--output json`, and generally make help a useful resource for agents discovering\n capabilities at runtime — not just humans.\n\n\n **Better machine-readable errors.** When something goes wrong today, agents get the same human-readable error messages as terminal users. We're\n changing that so errors in JSON mode return structured output, giving your agent the information it needs to handle failures gracefully, retry intelligently, or surface the right context back to you.\n\n\n Both of these are in active development. If you're already using `glab` with an AI tool, you're exactly the audience we want feedback from.\n\n\n * **What friction are you hitting?** Commands that don't behave well in agent contexts, error messages that aren't actionable, gaps in JSON output\n coverage. We want to know.\n\n * **What workflows have you unlocked?** Real usage patterns help us prioritize what to build next.\n\n\n Join the discussion in [our feedback issue](https://gitlab.com/gitlab-org/cli/-/issues/8177) — that's where we're shaping the roadmap for agent-friendliness, and where your input will have the most direct impact. If you've found a specific gap, [open an issue](https://gitlab.com/gitlab-org/cli/-/issues/new). If you've got a fix in mind, contributions are welcome. Visit [CONTRIBUTING.md](https://gitlab.com/gitlab-org/cli/-/blob/main/CONTRIBUTING.md) to get started.\n\n\n The GitLab CLI has always been about giving developers more control over their workflow. As AI becomes a bigger part of how we all work, that means making `glab` the best possible interface between your AI tools and your GitLab projects. We're just getting started and we'd love to build the next part with you.\n",[21,739,764],"tutorial",{"featured":15,"template":13,"slug":766},"give-your-ai-agent-direct-structured-gitlab-access-with-glab-cli",{"promotions":768},[769,782,793,804],{"id":770,"categories":771,"header":772,"text":773,"button":774,"image":779},"ai-modernization",[11],"Is AI achieving its promise at scale?","Quiz will take 5 minutes or less",{"text":775,"config":776},"Get your AI maturity score",{"href":777,"dataGaName":778,"dataGaLocation":253},"/assessments/ai-modernization-assessment/","modernization assessment",{"config":780},{"src":781},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/qix0m7kwnd8x2fh1zq49.png",{"id":783,"categories":784,"header":785,"text":773,"button":786,"image":790},"devops-modernization",[739,39],"Are you just managing tools or shipping innovation?",{"text":787,"config":788},"Get your DevOps maturity score",{"href":789,"dataGaName":778,"dataGaLocation":253},"/assessments/devops-modernization-assessment/",{"config":791},{"src":792},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138785/eg818fmakweyuznttgid.png",{"id":794,"categories":795,"header":796,"text":773,"button":797,"image":801},"security-modernization",[23],"Are you trading speed for security?",{"text":798,"config":799},"Get your security maturity score",{"href":800,"dataGaName":778,"dataGaLocation":253},"/assessments/security-modernization-assessment/",{"config":802},{"src":803},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/p4pbqd9nnjejg5ds6mdk.png",{"id":805,"paths":806,"header":809,"text":810,"button":811,"image":816},"github-azure-migration",[807,808],"migration-from-azure-devops-to-gitlab","integrating-azure-devops-scm-and-gitlab","Is your team ready for GitHub's Azure move?","GitHub is already rebuilding around Azure. Find out what it means for you.",{"text":812,"config":813},"See how GitLab compares to GitHub",{"href":814,"dataGaName":815,"dataGaLocation":253},"/compare/gitlab-vs-github/github-azure-migration/","github azure migration",{"config":817},{"src":792},{"header":819,"blurb":820,"button":821,"secondaryButton":826},"Start building faster today","See what your team can do with the intelligent orchestration platform for DevSecOps.\n",{"text":822,"config":823},"Get your free trial",{"href":824,"dataGaName":53,"dataGaLocation":825},"https://gitlab.com/-/trial_registrations/new?glm_content=default-saas-trial&glm_source=about.gitlab.com/","feature",{"text":519,"config":827},{"href":57,"dataGaName":58,"dataGaLocation":825},1777934800337]