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Layered approval workflows, protected container repositories, and real-time SAST in VS Code strengthen enterprise security controls.
Sophisticated governance and shift-left security
GitLab 17.8 enables more granular approval requirements in security policies and protected container repositories for enhanced supply chain security. Real-time SAST scanning in VS Code and ML model experiment tracking reaches general availability (GA), demonstrating GitLab's focus on both shift-left security and MLOps workflow maturity.
Enforce layered security approvals from varied roles, individual approvers, or separate groups with up to five approval rules per policy. Organizations can configure:
Distinct role approvals requiring one approval from a Developer role and another from a Maintainer role.
Role and group approvals requiring one approval from Developer or Maintainer and separate approval from Security Group members.
Distinct group approvals requiring approvals from different specialized groups like Python Experts and Security teams.
Complex compliance workflows to ensure the right people review every change.
Address security and control challenges in managing container images with protected container repositories. This solution provides:
Enhanced security through strict access controls for sensitive container repositories.
Granular permissions for push, pull, and management operations.
Seamless integration with GitLab CI/CD pipelines so there’s no workflow disruption.
Protection from unauthorized access and accidental modifications to critical container assets.
SAST scanning in VS Code
Code
Secure
Scan project files directly in VS Code before committing or pushing them to find and fix security vulnerabilities faster. Developers can:
Identify security issues immediately without waiting for pipeline results.
View scan results in a dedicated side panel that updates as code changes.
Hover over vulnerability results for detailed descriptions or open in a separate editor window.
Fix vulnerabilities before they enter the codebase.ML model experiment tracking (now GA)
Track machine learning experiments with parameters, metrics, and artifacts logged directly into GitLab. This GA release enables teams to:
Log experimental metadata so data scientists can replicate experiments later.
Keep all experimental data within your GitLab environment for centralized management.
Access enhanced data displays, deeper GitLab integration, and improved permissions.
Collaborate on ML experiments alongside code and CI/CD workflows.