Securing a CI/CD pipeline for ML models needs everything a regular software pipeline needs (provenance, signing, reproducibility) plus model-specific extensions, since a model artifact carries risk a regular binary doesn't: it can be poisoned during training, and its trustworthiness needs to be verifiable again at inference time, not just at deploy time.
From pull request to production promotion
A PR touching training code or data triggers automated checks: dependency scanning on the training environment's own libraries (a compromised or malicious version of a widely-used ML library is a real, documented supply-chain risk), and model-behavior tests that validate the retrained model against a held-out evaluation set, checking not just that it trains successfully but that its output distribution and key metrics fall within an expected range, catching a training pipeline that silently produced a degenerate or backdoored model. Once these pass, the pipeline builds a reproducible model container (pinning the exact training code, data version, and library versions used), generates an SBOM for that container the same way it would for any application image, and signs both the container and the model artifact itself, separately, since a model file can be swapped out from underneath a correctly-signed container image if only the container is signed and not the model artifact specifically.
Artifact storage
Both the signed container and the signed model artifact are pushed to a dedicated, access-controlled model registry, distinct from a general-purpose object store, with immutable versioning so a specific promoted model version can never be silently overwritten by a later training run reusing the same tag. Access to write into the registry is restricted to the pipeline's own service identity, not to individual engineers, so a promotion can only originate from a run that went through the required checks; read access for the serving infrastructure is scoped to pulling by exact version or digest, never a mutable "latest" pointer, closing the same moving-tag risk that applies to container images elsewhere in this topic. Retention policy keeps every promoted version (not just the current one) for a defined period, since a rollback or an audit may need to reference a model version that's no longer the one currently serving traffic.
Provenance and reproducibility for models specifically
Model provenance needs to capture, in addition to the usual build inputs, the exact training data version or hash and the training configuration (hyperparameters, random seed) used, since 'which model architecture and code' alone doesn't fully describe what produced a specific trained model's weights the way source code alone fully describes a compiled binary.
Preventing deployment of untrusted code and verifying provenance at inference time
The serving infrastructure should verify the model artifact's signature and provenance at load time, refusing to serve any model whose signature doesn't match an expected, approved training pipeline's identity, exactly the same admission-time verification pattern used for container images, just applied to the model artifact specifically rather than only the container that wraps it. For architectures where a sidecar or inference-serving process fetches secrets (API keys, database credentials the serving process needs), the same short-lived, scoped credential-injection pattern used elsewhere in this topic applies unchanged.
Audit metadata and security approvals
Every promotion from staging to production should capture which specific training run produced this model, what evaluation metrics it achieved, which registry version it was stored as, and who approved the promotion, giving the same kind of auditable trail a regulated financial or healthcare deployment would need to demonstrate later.
Trade-offs
The model-behavior tests add real computational cost (evaluating a retrained model against a held-out set on every relevant PR) compared to a typical unit-test suite's near-instant runtime; that cost is justified because a model's correctness genuinely cannot be verified by reading its code the way a typical function's correctness can, so this evaluation step is doing real, otherwise-unavailable verification work, not just adding process overhead.
