Security requirements for AI models change across the development lifecycle. A control that is essential during training, such as environment isolation, serves a different purpose than one required in production, such as drift alerting. Applying the same generic controls at every stage creates opportunities for vulnerabilities to take root in the AI pipeline.
This checklist gives your team a concrete, stage-by-stage reference for securing AI models from first training run to long-term production. It covers the controls that matter most for development, deployment, and ongoing monitoring. We’ve organized the list so you can quickly identify gaps and mitigate them without overhauling your entire pipeline.
Download the checklist to:
- Catch dependency and access control gaps in your development environment before they reach production.
- Confirm your deployment configuration is complete before you ship, with API security, output filtering, and traffic monitoring included.
- When production alerts fire, respond faster and identify the cause: distributional shift, model poisoning, or something else entirely.
Fill out the form to download the checklist.
