Secure GenAI Deployment Infrastructure Checklist

A secure GenAI deployment infrastructure checklist covers four controls — model integrity, access control during rollout, environment isolation, and audit — securing the path from artifact to production. For the model deployment security framework, see model deployment security checklist. For the full infrastructure security, see building secure AI infrastructure.

The Four Controls

Model integrity: hash-verify the model artifact before deployment to prevent tampered models from reaching production. Access control: only authorized identities trigger or approve deployment, with the approval recorded. Environment isolation: the deployment pipeline runs isolated from the serving runtime; a compromise of one does not compromise the other. Audit: every deployment action logged — who deployed what, when, with what approval — for post-incident investigation and compliance review. For post-deployment verification, see how AI model deployment works.

FAQ

What security does GenAI deployment infrastructure need?

Model integrity verification, access-controlled rollout, environment isolation, and audit logging. The deployment pipeline is an attack surface; securing it is as important as securing production. See the four controls above.

Summary

Secure GenAI deployment requires integrity, access control, isolation, and audit. For the full framework, see model deployment security checklist.