Give every model a named owner and split the work into five jobs: intake, pinning, update review, incident response and retirement. Most model risk in practice comes from the gaps between teams, where the data scientist downloaded it, the platform team runs it, and nobody owns the answer when something changes.
Why models fall between chairs
A package lands in a lockfile that code review sees. A model often arrives through a notebook, a download script or a shared drive, and nothing in the normal process notices. Without an owner, nobody pins it, nobody watches upstream, and nobody removes it when the project ends.
The table
| Job | Owner | Output |
|---|---|---|
| Intake | Requesting engineer, approved by security or platform | Source, owner, commit hash, license, file formats, loader code reviewed |
| Pinning | Requesting engineer | Full commit hash in version controlled config |
| Storage | Platform team | Approved copy in an internal store, read only for serving |
| Update review | Requesting engineer, with security input for format or loader changes | New hash approved or declined |
| Upstream watch | Platform or security | Webhook or scheduled check on the source repo |
| Incident response | Security, with the model owner | Isolate, rotate, rebuild; see what to do after loading an untrusted pickle |
| Retirement | Model owner | Removed from stores, caches, images and inventory |
One accountable name per model
Shared ownership usually means no ownership. Pick one person or one team per model and record it in the inventory. When that person leaves, reassignment should be a checklist item in offboarding, not something found during an incident.
Decision rights worth writing down
- Who can approve a new model? A short list, not everyone.
- Who can promote a model to production? See who should be allowed to promote a model to production.
- Who can accept a format exception? For example a pickle based file from a source you cannot replace.
- Who can make an emergency change? Define it, then review it afterwards.
Keep the process small
A form with eight fields that people fill in is better than a twenty page policy they avoid. Keep the intake record to one page, as described in a one page model intake record.
Measure it lightly
Count three things monthly: models in use with a named owner, models pinned by full hash, and models with no activity for ninety days. The last list feeds retirement.
Try it yourself
Click through it at your own pace, or open it full screen. Notice how findings carry an owner and a status, which is the same habit models need.
Safeguard's asset discovery, policies and compliance evidence automation can help keep owners and records current for the software around your models. Try the free scan.
Quick check
Should the data science team own model risk alone?
Usually not. Intake and incidents need security and platform input. Name one accountable owner per model.
What is the smallest useful inventory?
Model name, source, commit hash, owner, file formats and where it runs.
Last reviewed: 19 October 2026.
