Secure AI development pipeline

What it does

It closes the gap between experiment-grade tooling and the production systems it feeds. Nothing reaches production except through a controlled path, and the path itself is protected. The aim is that any model in production can be traced to an approved pipeline run, reviewed code and a known version of its data. The owner of the machine learning platform is accountable; security sets the standard. It applies to every environment that can influence a production model.

How to implement it

• Separate development, test and production, with promotion only through the pipeline.
• Require review and approval of pipeline definitions and training code.
• Scan dependencies, containers and infrastructure configuration for vulnerabilities.
• Remove direct network exposure of notebooks, trackers and registries, and require strong authentication.
• Mask or synthesise production data used in development.
• Include these environments in security monitoring and patching.

Evidence it operates

Design: an architecture record of environments and the promotion path. Operation: scan and patch records; pipeline logs showing that each production model arrived through an approved run; a check that no development tool is reachable from the internet.

Where it fits

Built with the platform and maintained continuously. It is the primary control for AI development environment compromise and supports model artifact signing.

Limits

It secures the route, not the content: a flawed or poisoned dataset passes through a secure pipeline unchanged. Researchers under time pressure work around heavy controls, so the secure path has to be the convenient one. Suppliers' training and hosting environments fall outside it and need equivalent assurance through contract and due diligence.