How do you Update Gemini / AI Model Versions in Your SecOpsEnvironment?

Are you using prebuilt or custom models with the Vertex AI integration? I have not yet tested it, but wonder if aliases would work - https://cloud.google.com/blog/products/ai-machine-learning/vertex-ai-model-registry?e=48754805#:~:text=AI%20Model%20Registry%2C-,aliases,-are%20uniquely%20named

We’re using prebuilt models, but different use cases may use different models, e.g. flash vs pro. 

Your current method is sound for discovery, but I would automate it as a recurring control rather than repeat the export manually.

The main improvement would be:

Enumerate playbooks and blocks through the SecOps API.

Recursively inspect all nested steps and action parameters.

Detect Gemini/Vertex integrations and extract:

explicit model IDs

aliases

variables used as model parameters

integration-instance defaults

Resolve inherited/default values, not only literal IDs stored in the playbook JSON.

Compare the resulting dependency inventory against a maintained deprecation list.

Alert or block promotion when a deprecated model is referenced.

One important edge case is that searching only for literal model IDs may miss playbooks that inherit the model from an integration instance, environment variable, alias, or nested block.

Aliases would help if every playbook references a stable alias and only the alias mapping changes. For prebuilt models or steps with model IDs embedded directly in their parameters, the dependency scan is still needed.

Ideally, Google could expose a first-class “model dependency inventory” showing which playbooks, blocks, and integration instances reference each model version.  🧠

​@whathehack81 thanks for the reply. Makes sense, our next step was going to be a job as you suggested which still mostly automates discovery. have you / your team managed to update the actions automatically using this method as well?