# The maturity gap in ML pipeline infrastructure

DevFeed: [The maturity gap in ML pipeline infrastructure](<https://devfeed.tech/articles/the-maturity-gap-in-ml-pipeline-infrastructure-13263.md>)

Original publisher: [Read original article](<https://www.chainguard.dev/unchained/the-maturity-gap-in-ml-pipeline-infrastructure>)

Published: 2026-01-26T00:00:00Z

Content type: opinion

Language: en

Sources: [Chainguard: Unchained](<https://devfeed.tech/sources/chainguard-unchained.md>)

Topics: [Security](<https://devfeed.tech/topics/security.md>), [Architecture & Design](<https://devfeed.tech/topics/architecture-design.md>), [Tooling](<https://devfeed.tech/topics/tooling.md>), [Machine learning](<https://devfeed.tech/topics/machine-learning.md>), [Software Engineering](<https://devfeed.tech/topics/software-engineering.md>), [PyTorch](<https://devfeed.tech/topics/pytorch.md>)

Tags: [ai-coding](<https://devfeed.tech/tags/ai-coding.md>), [attacks](<https://devfeed.tech/tags/attacks.md>), [chainguard-pytorch-image](<https://devfeed.tech/tags/chainguard-pytorch-image.md>), [infrastructure](<https://devfeed.tech/tags/infrastructure.md>), [machine-learning](<https://devfeed.tech/tags/machine-learning.md>), [machine-learning-pipelines](<https://devfeed.tech/tags/machine-learning-pipelines.md>), [ml](<https://devfeed.tech/tags/ml.md>), [ml-ops](<https://devfeed.tech/tags/ml-ops.md>), [pipeline](<https://devfeed.tech/tags/pipeline.md>), [python](<https://devfeed.tech/tags/python.md>), [pytorch](<https://devfeed.tech/tags/pytorch.md>), [secure-by-default](<https://devfeed.tech/tags/secure-by-default.md>), [security](<https://devfeed.tech/tags/security.md>), [security-best-practices](<https://devfeed.tech/tags/security-best-practices.md>), [serialization](<https://devfeed.tech/tags/serialization.md>), [serialization-format](<https://devfeed.tech/tags/serialization-format.md>), [tooling](<https://devfeed.tech/tags/tooling.md>)

## AI overview

This article argues that ML pipeline infrastructure in 2026 has a security maturity gap: common tooling does not yet provide the secure-by-default protections expected in software engineering. It examines risks including data poisoning, model laundering, and insecure model serialization, and discusses short-term mitigations and longer-term industry improvements.

## Source excerpt

ML pipelines in 2026 still lack secure-by-default tooling. Learn the key security gaps in ML Ops and how teams can reduce risk today.