Crypto · SaaS
Credentials often start in an engineer's local dev environments, then get copied into unscoped CI/CD variables to keep pipelines running. Stored there as a raw value rather than a reference to a scoped secret, a signing key reaches every job in the pipeline — compromised or not.
Standing up a standalone vault narrows that exposure but requires mirroring every change to project membership, protected branches, and production environments. When the vault falls out of sync, a pipeline breaks or an agent’s service account keeps access it no longer needs.
GitLab Secrets Manager tightens credential security without a second permission model. It is now generally available in GitLab 19.5 for Premium and Ultimate on GitLab.com and GitLab Self-Managed, with GitLab Dedicated coming soon. Built on OpenBao, it stores build-time and runtime credentials under the same permissions and audit trail as your code. Platform teams can offer developers self-service secrets, and security teams get credentials scoped to the jobs that use them, plus the pipeline and job ID on every CI/CD read.
"GitLab Secrets Manager lets us centralize secrets across CI/CD, Kubernetes, and infrastructure as code without standing up multiple vaults or maintaining separate integration points. It also strengthens our supply chain security by tightening which users and pipelines can access a given credential." - Jeremy Nauta, Software Architect, OneTrust
How Secrets Manager scopes build-time secrets to each job
Agents can take actions no one anticipated, so security teams need to regard every job as if its agents will use any credential it can reach. Secrets Manager narrows what agents can reach with the context GitLab already holds about each job: its project, the branch it runs on, whether that branch is protected, and the environment it targets.
A developer declares the secret a job needs with the secrets: keyword in .gitlab-ci.yml, and GitLab injects the value only into jobs that declare it and fall within its scope. Scope conditions accept wildcards on branch and environment, and they combine, so one rule can require a protected branch and a production/* environment before a job receives the credential.
Inside the job, GitLab writes the value to a temporary file and exposes only its path, which limits exposure in subprocesses, crash dumps, and telemetry. GitLab masks the value in job logs and discards it when the job ends. Unlike a CI/CD variable, a secret is never readable in your project configuration.
OpenBao encrypts secrets at rest with AES-256-GCM, so a copy of the secrets database holds only ciphertext. Jobs fetch values straight from OpenBao with short-lived tokens bound to each job’s identity, which keeps plaintext secrets out of the GitLab application.
See how to get started with Secrets Manager:
Revoke and trace an exposed credential
When the axios npm package was compromised, a SANS advisory warned teams to "assume all environment variables on affected systems were exfiltrated" and extend investigations to CI/CD runners and build infrastructure. Responders had to work out which credentials each affected job could read, how to cut off access, and which runs read them.
With Secrets Manager, when a job runs a poisoned dependency, the exposure stops at the secrets that job declared and was authorized to read. To clean up, revoke and replace the leaked credential, update the value in Secrets Manager, and audit that one job while other pipelines keep running. Secrets Manager prompts you to set a rotation reminder when you create a secret. Dynamic credentials are coming soon and will provide ephemeral access without manual rotation.
GitLab streams every secret create, read, update, and delete event to your audit event destinations and records changes in the audit trail. Each pipeline read carries the pipeline and job IDs, which tie to the commit, the triggering user, and the merge request, when there is one. Incident responders and auditors can trace a read to the change behind it without correlating logs from a CI system, a vault, and an identity provider. Audit events never contain secret values.
Govern secrets with the permissions that govern your code
As agents take on more work, platform teams field more requests for pipelines and service accounts that need credentials. When those requests queue, teams fall back on broad grants and shared secrets.
Secrets Manager uses the GitLab namespace as its isolation boundary. Projects inherit secrets defined in the groups above them, so you define a shared credential once. Owners grant read, write, and delete permissions to specific users or default roles, using the same membership model that governs the code. Remove a person or an agent's service account from a project, and that identity's access to the project's secrets ends.
Developers reference secrets through the project access they already hold. When a secret reference breaks, they fix it in the same merge request as the code, with no second repository, approver, or access ticket. With a governed credential one line away, fewer secrets end up in over-scoped CI/CD variables, local files, or the repository.
One store for pipelines and runtime workloads
Kubernetes, Terraform, and scripts often get separate secret stores, each with its own access model and logs. Secrets Manager serves runtime tools from the same store as pipelines, under the same permissions and audit trail:
Kubernetes, through External Secrets Operator. ESO re-fetches the secret on a set interval and updates the Kubernetes Secret in place, so a rotated value reaches the cluster without a pipeline run.
Terraform and OpenTofu. They read a secret as a data source at plan or apply time and keep credentials out of .tfvars files and CI/CD variables.
OpenBao and Vault CLIs, through the Vault-compatible API. Teams keep their existing scripts and point them at Secrets Manager.
Other runtime tools, through the Secrets Manager API.
Each runtime tool authenticates with a service account or an access token, and that identity needs project membership and explicit read permission for the secret. GitLab then issues a separate token, valid for five minutes, that the tool uses to fetch it.
How Secrets Manager fits with GitLab's supply chain controls
GitLab’s governed software factory connects code, pipelines, packages, and secrets on one platform, with one identity model and one record of how a change reached production. Dependency Firewall decides which packages enter a pipeline, Secrets Manager limits which credentials the pipeline can reach, and Artifact Central governs what you ship.
Secrets Manager supports two of the five stages in the GitLab Security Standard, GitLab's framework for letting agents earn autonomy: For Authorize, it binds each job to the secrets its identity and scope allow, and for Respond, it streams every CRUD event.
Secret push protection blocks commits containing credentials before they reach the repository, and Secret detection finds the ones already in your code and history. GitLab Duo Agent Platform triages what those scans find and flags likely false positives, so your team revokes, rotates, and audits only real leaks.
GitLab's existing integrations for HashiCorp Vault, AWS Secrets Manager, Azure Key Vault, and Google Cloud Secret Manager keep working alongside Secrets Manager, so you can migrate at your own pace.
Get started
GitLab Secrets Manager is a paid add-on for Premium and Ultimate, billed through GitLab Credits: 1 credit per secret stored per month, and 1 credit per 2,500 read operations. An enterprise license agreement is available for air-gapped environments.
On GitLab.com, top-level group Owners can try GitLab Secrets Manager free for 30 days or 500 evaluation credits, whichever ends first. Go to your top-level group, select Secure > Secrets Manager, then Start 30-day trial.
On GitLab Self-Managed, your administrator must install and enable Secrets Manager, following the documented sizing and high-availability guidance.
GitLab Dedicated support is coming soon.
To find the first credentials to move, run the secret audit tool for CI/CD variables, which flags variables likely to hold credentials. A migration workflow for CI/CD variables is in development.
Have suggestions or feedback? Share them in the Secrets Manager feedback issue.
Oct 15 · 00:00·GitLab
Cloud & data centers
When strikes hit Yandex data centers, many assumed Russian bot operations went dark. Cloudflare data reveals that while search crawling stopped, malicious bots simply migrated to other networks.
Oct 14 · 20:24·Cloudflare
SaaS · Cybersecurity
New Open Compute Project (OCP) working group: Companies form a new working group to standardize on a silicon-based and attested secure integrated hardware security module (HSM) solution for cloud and hyperscale data center infrastructure. Strengthening cloud security: Secure key caching and attestation cryptographic key lifecycle management at the server level will reduce latency, improve scalability and strengthen t
Oct 13 · 13:00·Marvell Technology
AI · SaaS
AI agents are fundamentally changing the economics of software development. As the cost of producing code falls, the volume of changes moving through the software factory is accelerating beyond what infrastructure designed for human developers was built to handle.
GitLab anticipated this shift and began developing next-generation source code management (SCM) to support thousands of concurrent agents working against large repositories. In June, we announced next-gen SCM at GitLab Transcend, joined on stage by Nick G. from Anthropic's source control team. We've continued collaborating on the technical challenges of agent-scale development, and in August we shared more of our architecture and early performance results.
This week, Anthropic provided a compelling demonstration of why this work is so important. As daily merges to its monorepo grew from approximately 2,500 in May to more than 30,000 in September, its engineers built Scion, a specialized source control system designed for their environment.
Anthropic's experience reinforces the architectural direction we've been pursuing. Scion is purpose-built for enormous monorepos where nearly all clients are agents that are always online. Anthropic acknowledges that its approach isn't a general-purpose replacement for Git and requires its own code review, search, and CI infrastructure.
GitLab is building for the broader enterprise market: delivering agent-scale performance while preserving Git compatibility, infrastructure choice, and integration with the software development lifecycle.
"Scaling our monorepo for agentic development has changed how we think about Git server architecture. We see room to expand what Git servers can do for these workloads without departing from the overall Git model."
— Nick G., Source Control, Anthropic
Built on Git. Designed for agents.
Traditional Git workflows require clients to download substantial repository data, even when an agent needs only a small portion. A single agent invocation can involve 5–10 GB of data transfer and more than 30 seconds of setup, just to answer one question. Multiply that overhead across thousands of agents and source control becomes a bottleneck.
Most enterprises can't move source control onto new infrastructure just to scale it. Next-gen SCM preserves the Git protocol while modernizing the architecture underneath. Stateless compute scales horizontally against shared object storage, while agent-native interfaces let agents request the files and history they need without repeatedly cloning entire repositories.
In early internal tests, workloads completed up to 50x faster, used up to 2x fewer tokens, and generated up to 1,000x less network traffic compared with conventional workflows. These are peak improvements under specific test conditions, and we're continuing to validate performance under sustained, concurrent load.
The same architecture is designed to run on GitLab.com and on S3-compatible storage that customers control, including in air-gapped data centers. Repository data stays within the customer's infrastructure, preserving data sovereignty and existing investments while delivering the scalability agentic development demands.
For a closer look at the architecture and what we're building, watch Mark and Kranthi's latest next-gen SCM update:
Source control is only the first bottleneck
Speeding up source control alone moves the bottleneck downstream. An agent that writes code in seconds creates little value if the resulting changes spend hours waiting for validation, security review, or deployment.
Traditional merge queues and CI workflows face growing pressure when agents generate changes faster than validation can complete. We're seeing this pressure across GitLab.com. Over the past year, customers created 40% more CI/CD pipelines, while code pushes increased 50%. Agent-scale CI will need to validate more changes with less compute per change while preserving evidence of what was tested and verified.
This is why next-gen SCM is part of GitLab's broader vision for a governed software factory. As agents accelerate software production, source control, context, orchestration, CI, security, and governance must evolve together.
We're designing next-gen SCM to let thousands of agents work in parallel in isolated sessions, where unsuccessful experiments can be discarded without disrupting shared code. Successful changes can flow into GitLab's existing merge requests, pipelines, security checks, and governance controls on their path to production.
GitLab Orbit is being developed to provide a context graph of the entire software lifecycle, while GitLab Duo Agent Platform orchestrates agent work across stages. We're also advancing capabilities to make agents accountable for their work, including attribution and authorization of agent actions, preserving the instructions and decisions behind code changes, and giving reviewers the context needed to focus human judgment where it matters.
The goal is to reduce the time and total cost of moving software from intent to production, even as the number of agents and changes grows dramatically.
We believe a governed software factory offers the best economics for agentic development: more software delivered at lower cost per change, with the security and control enterprises require.
Advancing Git through open collaboration
Git's open ecosystem has been foundational to modern software development, and we believe it will remain essential in the agentic era. GitLab has long been an active contributor to Git, and we'll continue working with the community to advance the standard that millions of developers and tools depend on.
Our next-gen SCM architecture builds on that foundation, combining Git compatibility with a redesigned backend and agent-native interfaces designed to deliver the performance and scalability these new workloads demand.
This approach preserves the investments customers have made in Git while allowing GitLab to innovate rapidly across the broader software lifecycle. Our differentiation is the governed software factory: bringing source control, context, orchestration, security, and governance together on one platform.
What's next
Next-gen SCM is in private beta, and we're working with customers to validate performance against their own repositories and workloads.
As agents dramatically increase software production, the entire software factory must operate at machine scale. GitLab is building that foundation.
Request early access to next-gen SCM and evaluate it against your own repositories.
This blog post contains "forward-looking statements" within the meaning of Section 27A of the Securities Act of 1933, as amended, and Section 21E of the Securities Exchange Act of 1934. Although we believe that the expectations reflected in these statements are reasonable, they are subject to known and unknown risks, uncertainties, assumptions and other factors that may cause actual results or outcomes to differ materially. Further information on these risks and other factors is included under the caption "Risk Factors" in our filings with the SEC. We do not undertake any obligation to update or revise these statements after the date of this blog post, except as required by law.
Oct 13 · 00:00·GitLab
AI · SaaS
Why future AI data centers need to be open and heterogeneous AI is evolving to be more than just a workload. As initiatives transition from training to inferencing, from generative AI to agentic AI, AI will increasingly operate as a service and run across distributed environments, like the edge, cloud, and sovereign systems. Future AI systems will also be heterogenous. Instead of being dominated by one architecture or platform, different workloads with different needs will require a diverse mix of accelerators, processors, and infrastructure. Yet with the next era of AI comes a new set of I...
Oct 12 · 13:15·Hewlett Packard Enterprise
Finance · AI
Marvell Powers Next-generation AI Data Center Infrastructure with Switching, Interconnect, Memory and Storage Solutions Across Every Distance SANTA CLARA, Calif.--Marvell Technology, Inc. (NASDAQ: MRVL), a leader in data infrastructure semiconductor solutions, today announced it will showcase its comprehensive portfolio of AI data center infrastructure solutions at OCP Global Summit 2026, October 12
Oct 8 · 13:00·Marvell Technology
Finance · AI
Tokyo, Japan – October 6, 2026 – HPE (NYSE: HPE) and Mizuho Securities today announced that Mizuho Securities will build a new direct liquid cooling ready data center and deploy HPE Cray XD2000. The new infrastructure will support Mizuho Securities in advancing its global business strategy and delivering higher-value financial products and services to a wider customer base. Installation of the liquid cooling equipment, system buildout, and operational validation will begin this fall, with the system scheduled to go into production in mid-2027. Mizuho Securities’ Global Markets department pr...
Oct 6 · 13:15·Hewlett Packard Enterprise
Crypto · AI
At Transcend, we made over a dozen announcements across all four layers of our architecture for agentic software engineering: agent orchestration, data and context, DevOps workflows, and governance and security. Watch the replay, or read on for what shipped and how to turn on the new capabilities.
At a glance
What we launchedRelease statusWhat it does
Goal-driven flows (/goal)GA this monthCarry a change through review, tests, scans, and approvals without waiting between stages of the software lifecycle.
Custom FlowsGAChain agents together to match the process your teams already follow for software delivery.
GitLab for SlackGA this monthStart an agentic flow from the channels where your teams already work.
MCP ServerGA this monthSecurely connect AI tools and applications into GitLab workflows under the same policy.
Open weight modelsGAGet comparable performance on lower-cost workloads with GitLab-hosted GLM 5.3, Kimi K3, and MiniMax 3 models.
GitLab OrbitGA next monthGive your agents the right knowledge requiring 45x fewer retries with a context graph of your software lifecycle.
Duo Agent Platform Impact AnalyticsEarly accessMeasure the cost and impact of your AI investment, by team, task, and model.
Credit and usage controlsGASet caps at the subscription, group, and user level and get alerts on AI spend.
GitLab Artifact CentralBetaGive every package and container one governed home, next to source and CI.
GitLab Dependency FirewallEarly accessSet rules that block, quarantine, or warn on risky packages before they reach your build.
GitLab Secrets ManagerGA this monthScope build-time secrets to the job and revoke them with one click.
GitLab Security StandardAvailable todayHarden your software factory in five stages and let your agents earn autonomy.
New flows with Claude Mythos 5 and 5.1GA next monthFind and remediate vulnerabilities on GitLab Duo Agent Platform with new security flows.
Agent orchestration across the software lifecycle
Today, each step in your agentic workflow still waits for a person to approve it before the next one starts.
Goal-driven flows take a stated objective and run it end to end, through review, tests, security scans, and approvals from the Duo CLI, headless mode, or Agentic Chat. Custom Flows let you chain agents together to match the process your team already follows. GitLab for Slack starts a flow from the channel where the conversation is already happening. The MCP Server enables external AI applications and coding agents to securely access your GitLab instance under the same policy your own agents follow.
GitLab-hosted open-weight models, including GLM 5.3, Kimi K3, and MiniMax 3, give you a lower-cost option when a workload doesn't need a frontier model. At similar task-completion rates, some open weight pairings deliver up to 8x more model calls per GitLab Credit than their frontier counterparts.
Data and context to power human and agent reasoning
Every agent task starts by re-deriving the same context, which drives up retries and token use. And without coherent visibility into AI costs and ROI, there's no way to tell whether your investment is paying off.
GitLab Orbit, the context graph of your entire software lifecycle increases agent accuracy with up to 45x fewer retries, effectively reducing cost for agentic workflows with up to 4.5x fewer token use. During its beta period, Orbit has been used by more than 3,500 organizations, running over 280,000 queries.
Duo Agent Platform Impact Analytics shows the cost and impact of your AI investment by team, task, and model. Credit and usage controls let you set caps at the subscription, group, or user level, with alerts at 50%, 80%, and 100% of spend, and an automatic pause once you hit the cap you set.
DevOps infrastructure at agent scale
Most of what ships isn't code your team wrote, it's assembled from open source packages, base images, and libraries pulled in from everywhere. When every team runs its own registry, there's no single place to confirm what you actually published or shipped.
Back in June, during Transcend London, we introduced our next-gen source code management solution helping you achieve machine scale with your coding agents. The next machine-scale problem goes to the step after code, when you’re creating a build.
GitLab Artifact Central replaces a separate registry per team with one place to publish and pull from, built next to your source code and CI pipelines. It publishes and pulls Maven, npm, and Docker, and OCI packages directly from CI pipelines, with PyPI and NuGet coming at GA. Artifact Central uses the same identity and permissions as your source code, with virtual repos, native CI/CD authentication, and full build provenance. Early results show roughly 50% lower total cost of ownership compared to alternative tooling.
Governance and security with centralized enforcement
Agents pull in package components to your build at machine speed, making it impossible to manually secure what you ship. When that gap exists, you risk shipping security vulnerabilities into production rather than catching them before they impact customers, and you have to answer for it.
GitLab Dependency Firewall, now in early access, sets rules on package age, vulnerability severity, malicious package detection, and license compliance, with actions to block, quarantine, or warn, enforced on the registries you already use. It works directly with GitLab Artifact Central, and is compatible with JFrog Artifactory and Sonatype Nexus Repository.
With a greater number of builds, another risk emerges: agents inheriting build-time secrets from local dev environments that nobody secured, and using them across CI pipelines. GitLab Secrets Manager gives each build-time secret one home, scoped to the job that needs it, with one-click revocation for a leaked credential and up to 50% savings compared to hosting a separate vault.
The same AI models that let agents write and ship code at machine speed also make existing weaknesses faster and cheaper to discover, connect, and exploit. The GitLab Security Standard lays out five stages for assessing and hardening your agentic software development: authorizing every actor's access, isolating workspaces and their inputs, verifying the checks that judge the work, releasing exactly what was verified, and responding fast when something looks wrong, all built on a foundation of policy, audit, and ownership. Read the full standard for the complete breakdown.
Coming soon, Anthropic’s Claude Mythos 5 and 5.1 will power new GitLab Duo Agent Platform security flows, helping organizations with approved environments find and fix vulnerabilities faster than attackers can discover and exploit them.
How to buy with GitLab Flex
Agentic software engineering makes headcount, credit burn, and new capabilities hard to predict six months out. GitLab Flex is one annual commitment that covers new GitLab capabilities as they ship, without re-procurement. It lets teams reshape seat and AI spend via GitLab Credits on a monthly basis as their needs change.
Where to start
Turn on GitLab Duo Agent Platform: Existing customers start today with credits already in their subscription, and every new GitLab trial includes 30-day access. To increase the efficiency of your agents, turn on GitLab Orbit beta for GitLab.com.
Adopt the GitLab Security Standard: GitLab Ultimate is available as a free trial. A Secrets Manager trial is available to Ultimate and Premium customers. GitLab Artifact Central is open in beta, and you can request early access to Dependency Firewall.
Get more from every dollar with GitLab Flex: One annual commitment for new GitLab innovations without re-procurement - with the ability to reshape your spend for seats and GitLab Credits month to month.
If you're migrating from GitHub, do it on your own terms with dedicated migration tools, custom migration services, and an exclusive offer to get one year of GitLab free when signing up for a three-year contract.
And if you're ready to start building, join "Life after code: the path to production at the speed of imagination," our three-week hackathon in partnership with Anthropic and Google Cloud.
Wherever you start, you're choosing the same destination: the foundation for a governed software factory where agents accelerate the work and every change stays trusted.
We can’t wait to see what you build next.
Oct 6 · 00:00·GitLab
Finance · AI
SUNNYVALE, Calif. – Sept. 30, 2026 – HPE (NYSE: HPE) today announced a $1.2 billion order from Vultr, the world’s largest privately-held cloud infrastructure company, to deploy AMD Helios™ AI Rack by HPE systems at Vultr’s cloud data center locations in the United States. It is HPE's first order for the new AMD Helios system, which features purpose-built HPE Networking scale-up switching and software. HPE and AMD will support Vultr’s effort to meet service provider and enterprise demand for model training and inferencing workloads in their AI data centers. “AI is driving the largest infrast...
Sep 30 · 13:15·Hewlett Packard Enterprise
Finance · AI
SUNNYVALE, Calif. – Sept. 30, 2026 – HPE (NYSE: HPE) today is hosting HPE Networking Investor Day, where Networking business leaders will outline a strategy designed to accelerate profitable growth and long-term shareholder value creation in the AI era. The company will share how HPE plans to capture expansive demand across Data Center Networking, Routing, Campus & Branch, and Security, positioning HPE Networking as a key driver of its growth. “AI is reshaping the technology stack, making the network more strategic and driving significant new demand from enterprises and service providers,” ...
Sep 30 · 13:15·Hewlett Packard Enterprise
AI · Cloud & data centers
First 2nm demos: Demonstrations of first 2nm 400G/lane optical PAM4, first 2nm 800G ZR/ZR+ pluggable with MACsec and first 2nm 1.6T ZR and O-band coherent-lite technologies. Enabling AI infrastructure scaling: Marvell advanced optical and silicon design leverages leading-edge process nodes to deliver the improvements in performance, bandwidth density and power critical to scaling AI infrastructure efficiently. I nnov
Sep 21 · 05:00·Marvell Technology
AI · SaaS
What does independent living mean for persons with disabilities, and how can technology help them achieve it? 52 student finalists, chosen...
Sep 18 · 11:42·Dell Technologies