AI Artifacts
Build AI From Artifacts You Can Trust
Secure your AI supply chain with open source python packages and models built from source, vetted, and documented before your teams choose them.
Every AI Project is a Supply Chain You Didn’t Approve
Coding assistants pull dependencies into your team’s builds faster than anyone can review them. AI projects run on Python. AI projects depend on Python and open source packages that may never have passed your company’s security and policy checks.
You Can’t Prove What’s in the Stack
What is in our AI supply chain, and who is accountable for the quality? Today that answer takes weeks of hunting, time you do not have.
A Vulnerability is Disclosed and the Clock Starts
Are we affected, and where? Without a bill of materials already in hand, that is a long investigation, not a quick lookup.
Your Team Owns the Risk of Decisions You Did Not Make
Your teams choose the tools and dependencies. You are accountable for the security and compliance risks, often without knowing what was approved or why.
Your Teams Need Trusted Artifacts. You Need Confidence in What They Use.
You can’t sign off on what you can’t see. So we build packages from source. 50M+ users and 95% of the Fortune 500 build on Anaconda, from 20,000+ packages and models we build, benchmark, and verify.
Qualified at Intake, Not Inspected Afterwards
Vetted artifacts and controlled builds help your teams reduce supply chain risk before dependencies enter your environment, reducing downstream investigation and remediation.
Only the Highest Quality Artifacts are Available
- Packages built from source, not repackaged
- Open source models benchmarked with security scores
- Role-based governance
Packages Are Built From Source and Secure From the Start
- SBOM and AIBOM per artifact
- Source-hash verification
- Signed before publication
Reduce the Scope and Disruption of Patching
- Dynamic linking
- Pre-built cross-language dependency trees
- Coordinated releases
Three Steps to Trusted and Secure Artifacts
No migration and no new tools for your developers. What changes is where they resolve packages and models from.
Step 1
Bring us the environment you’re least comfortable signing off on. We’ll show you what’s actually in it.
Step 2
Connect your teams to the curated channel. They keep familiar conda and pip workflows and IDEs while accessing vetted artifacts.
Step 3
Apply your security and license policies centrally, with rules your teams inherit automatically.
Answer the Question Before it Becomes an Incident
Less time triaging and gathering evidence. More time on the risks that need your judgment.
A CVE Lands and You Already Have the Answer
Exposure is a lookup in a record you already hold. Scope stays narrow because you can see exactly which packages are affected and where they run.
Auditors Get Answers Before They Finish Asking
License data is already documented and policy is enforced automatically, so audit prep takes less manual work.
You Stop Being the Gate
Developers install what they need without a review queue. The package that should not be there was never available, so there is nothing to route around.
Before & After
From Public Repos to Trusted Packages
No one is accountable when a package breaks
Vendor accountability for every package
Anyone can build and publish a package
Every package built by Anaconda engineers
Build process varies from package to package
Built from source, every time
Binary dependencies hidden inside wheels
Binaries unbundled, with cross-language dependencies resolved
Vulnerability detection left to third-party scanners
CVE matching plus human review
SBOM and license data still emerging
SBOM and SPDX license data included
No testing of how an update affects downstream packages
Coordinated downstream testing before release
Fixing one library means rebuilding every package that bundles it
One update fixes a dependency everywhere
Runs Wherever Your Security Rules Require
Choose hosted SaaS, your own infrastructure, or a fully air-gapped network. You decide where your artifacts live.
SAAS
Onboard Teams in Hours, Not Months
Hosted by Anaconda. Policies apply automatically, vulnerability alerts include fix guidance, and SSO takes three clicks.
Your own infrastructure
Behind Your Firewall, or Inside Your VPC
- Full catalog, unlimited private artifact hosting, complete audit trails
- Mirror conda, PyPI, and CRAN on your schedule
Air-gapped networks
No Outbound Connection Required
- Signed bundles carried across on portable media
- Packages refresh monthly, vulnerability data daily
AI Supply Chain Risk Compounds Over Time
It compounds when a vulnerability lands on a Friday. It compounds when an audit fails because a license question has no documented answer. Neither is a failure of your team. It’s what happens when AI is assembled from artifacts nobody qualified.
Frequently Asked Questions About AI Artifacts
How is this different from scanning what we already pull from PyPI?
A scanner inspects a package after it is already a compiled binary in your environment and for conda packages almost always generates false positives and false negatives. Anaconda builds from source, so the evidence still exists when the work is done. Scanning is inspection. Anaconda is a qualification.
Python packages are free. Why pay for Anaconda?
Free packages give you the code. They don’t give you an accountable builder or the evidence your security and compliance obligations call for. Anaconda adds packages built from source, an SBOM for each package, CVE matching with human review, policy enforcement, audit trails, and enterprise support. Your teams keep the same open source packages they rely on today. Set Anaconda policy to restrict AI supply chain access based on corporate policy.
Does this require developers to change tools?
No. Teams keep conda and pip workflows and the IDEs, Notebooks, and other tools they already use. What changes is the channel they resolve from, and role-based governance determines what is available in it.
Can we run this with no internet connection at all?
Yes. Artifacts are available in air-gapped networks, meaning networks physically isolated from every other network. Anaconda publishes package and CVE data as signed bundles you download on a connected machine and carry across on portable media. Verify the SHA-256 checksums before you move them. Package bundles refresh monthly and CVE data daily, and your mirror picks them up on its next scheduled run, or whenever you force one.
How do self-hosted deployments stay current?
Your mirror syncs conda, PyPI, and CRAN on the schedule you set. Active mirroring pulls new packages as soon as they are published. Passive mirroring fetches a package the first time someone requests it. Schedule either with cron. Vulnerability data refreshes hourly.
What is the cool-off period, and does it slow my developers down?
New upstream versions are held for review before release, which stops a poisoned “latest” release from reaching your pipeline minutes after publication. Developers pull normally. The delay applies to a small number of brand-new releases to ensure they do not include malicious code, not to packages teams use every day.
How do you vet a model you did not build?
Differently from a package, and deliberately so. Open-weight models are curated from leading repositories and validated with industry-standard benchmarks, then documented with security scoring, licensing and performance metrics. You get an AI Bill of Materials describing provenance, licensing and composition.
Can we control which models our teams are allowed to use?
Yes, by policy rather than by review queue. Set rules based on security vulnerabilities, licensing or compute requirements, block access to unapproved models, and route approvals automatically. Models are stored in your own private cloud with role-based access, SSO and audit trails, so no data leaves your environment.
What exactly do we get with every artifact?
For packages, an SBOM listing every package and dependency you pulled, plus provenance and source-hash verification. For models, an AI Bill of Materials covering provenance, licensing and composition, alongside security scoring and benchmark results. In both cases the record arrives with the artifact rather than being assembled later.
Trusted AI Starts With What You Build On
AI Artifacts supply the packages and models behind every project on the Anaconda Platform.
AI Workspaces
Packages, models, and agentic development environments to build with.
AI Orchestration
Get more AI ideas out of experimentation and into production, faster.
AI Security & Guardrails
Gain control without becoming the gate. Know what’s entering AI development.