An artifact registry is a centralized, managed repository that stores, versions, and secures software packages, container images, and machine learning models. It acts as a single source of truth for build dependencies and deployment artifacts, enforcing strict access control policies and preventing the use of unverified or tampered components within a DevSecOps pipeline.
Glossary
Artifact Registry

What is Artifact Registry?
A foundational control point for securing the machine learning software supply chain by centralizing the storage, vulnerability analysis, and access governance of immutable build artifacts and model dependencies.
Beyond storage, a modern registry automatically scans for known vulnerabilities (CVEs) and verifies cryptographic signatures to ensure artifact integrity. By integrating with SLSA Framework controls and Binary Authorization policies, it blocks the deployment of non-compliant images, directly mitigating dependency confusion attacks and ensuring only trusted code reaches production environments.
Key Features of an Artifact Registry
An artifact registry provides a centralized, governed hub for storing, versioning, and securing software packages and dependencies. The following capabilities define a production-grade, enterprise-ready implementation.
Vulnerability Scanning
Automated, continuous scanning of every artifact upon push and on a recurring schedule. The registry integrates with public vulnerability databases (CVEs) to flag known risks in operating system packages and language-level dependencies. Key aspects include:
- Layer-by-layer container image analysis
- Identification of high and critical severity CVEs
- Blocking of deployments based on policy violations
- Integration with Vulnerability Exploitability eXchange (VEX) to filter non-exploitable findings
Fine-Grained Access Control
Enforces strict, identity-based permissions for pushing, pulling, and deleting artifacts. Policies are managed as code using engines like Open Policy Agent (OPA). This ensures:
- Role-based access control (RBAC) for teams
- Repository-level isolation for different projects
- Temporary, scoped tokens for CI/CD pipelines
- Integration with Workload Identity for keyless authentication
Cryptographic Signing & Verification
Ensures artifact integrity and non-repudiation from build to deployment. The registry supports keyless signing via Sigstore and Cosign, which ties signatures to short-lived OIDC certificates. This enables:
- Verification that an artifact has not been tampered with
- Confirmation of the build system's identity
- Enforcement of Binary Authorization policies at deploy time
- Storage of attestations in a Transparency Log for auditing
Immutability & Versioning
Artifacts are immutable once published. Any change, no matter how small, generates a new, unique version identified by a content-addressable digest. This prevents:
- Overwriting of production dependencies (dependency confusion)
- Drift between development and production environments
- Unauthorized modification of released software
- Enables precise dependency pinning and reproducible builds
Dependency Graph & SBOM Generation
Automatically constructs a dependency graph mapping all transitive relationships between stored components. The registry can generate a Software Bill of Materials (SBOM) in standard formats like SPDX or CycloneDX on demand. This provides:
- Complete visibility into the software supply chain
- Rapid impact analysis when a new zero-day vulnerability is disclosed
- Streamlined license compliance auditing
Remote Proxy & Caching
Acts as a pull-through cache for upstream public registries (e.g., Docker Hub, Maven Central, PyPI). This centralizes egress traffic and insulates builds from external outages. Benefits include:
- Reduced bandwidth consumption and faster builds
- A single point of governance for all external dependencies
- The ability to quarantine and scan third-party libraries before internal use
- Protection against dependency confusion attacks by prioritizing private packages
Frequently Asked Questions
Clear, technical answers to the most common questions about securing, managing, and operationalizing artifact registries within a hardened AI supply chain.
An artifact registry is a centralized, managed repository for storing, versioning, and securing software packages, container images, and machine learning model binaries throughout the development lifecycle. It functions as a single source of truth, acting as a private package manager that proxies public registries while hosting proprietary artifacts. The system works by authenticating clients via OIDC-based workload identity, authorizing push/pull operations against granular policy-as-code rules, and automatically triggering vulnerability scans upon ingestion. When a CI/CD pipeline publishes a build, the registry assigns an immutable digest (e.g., sha256:...) to guarantee cryptographic integrity, ensuring that a deployment referencing model:v1.2 always pulls the exact, untampered bytes verified during the build phase.
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Related Terms
Explore the interconnected security and governance concepts that form the foundation of a trusted artifact registry, ensuring integrity and compliance across the software supply chain.
Dependency Confusion
A supply chain attack vector where a malicious package with a higher version number is uploaded to a public registry, tricking a build system into downloading it instead of the intended private dependency. Mitigations include:
- Scoping private packages with namespaces or scoped registries
- Configuring clients to prioritize the private artifact registry
- Using dependency pinning to exact versions and hashes
Container Scanning
The automated process of analyzing container images layer-by-layer to identify embedded OS packages, libraries, and known vulnerabilities before deployment. Registry integration features:
- Continuous scanning on push to the artifact registry
- CVE databases mapped to package manifests
- Policy-based blocking of images exceeding vulnerability thresholds
- Generates VEX statements to reduce false positives

About the author
Prasad Kumkar
CEO & MD, Inference Systems
Prasad Kumkar is the CEO & MD of Inference Systems and writes about AI systems architecture, LLM infrastructure, model serving, evaluation, and production deployment. Over 5+ years, he has worked across computer vision models, L5 autonomous vehicle systems, and LLM research, with a focus on taking complex AI ideas into real-world engineering systems.
His work and writing cover AI systems, large language models, AI agents, multimodal systems, autonomous systems, inference optimization, RAG, evaluation, and production AI engineering.
Partnered with leading AI, data, and software stack.
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