Ensure that vTPM feature is enabled for your Vertex AI notebook instances

Using Console

Below are the console steps to ensure vTPM is enabled for Vertex AI notebook instances. vTPM is a Shielded VM feature; for many notebook types this is only settable at creation time, so you may need to recreate the instance.

1. Check whether vTPM is already enabled

Go to Vertex AI Workbench
- In the Google Cloud console, open:
  Navigation menu → Vertex AI → Workbench
Identify your notebook:
- You’ll see User-managed notebooks and/or Managed notebooks tabs.
Open the instance details:
- Click the notebook name → in the details pane, look for Shielded VM or Security info.
- If the instance indicates vTPM (or “Shielded VM with vTPM / integrity monitoring”) is enabled, no further action is needed.
- If not visible or disabled, plan to recreate the notebook with vTPM on.

For existing Workbench instances, Shielded VM/vTPM settings generally cannot be changed in place. You must create a new notebook instance with vTPM enabled.

2. Create a new Vertex AI notebook with vTPM enabled

A. Managed notebooks

In the console, go to
Navigation menu → Vertex AI → Workbench → Managed notebooks.
Click New notebook (or Create notebook) and choose the image/type you need.
In the creation page, expand Environment, then Advanced options or Security (the naming can differ slightly):
- Look for Shielded VM options.
Enable:
- Turn on vTPM (or Virtual Trusted Platform Module (vTPM)).
- Optionally also enable Integrity monitoring (recommended).
Configure other settings (machine type, region, disk, etc.).
Click Create.

Once created, the managed notebook VM will have vTPM enabled.

B. User-managed notebooks

In the console, go to
Navigation menu → Vertex AI → Workbench → User-managed notebooks.
Click New notebook → select the image/framework and region/zone.
On the creation page, expand Machine configuration or Security / Shielded VM section:
- Check the box for Enable Shielded VM options if present.
- Enable vTPM (sometimes presented as “Turn on vTPM”).
- Optionally enable Integrity monitoring.
Finish configuring instance details and click Create.

3. Migrate data / notebooks from old instance (if needed)

Start both the old and new notebook instances.
From JupyterLab (or SSH), copy:
- Notebook files (.ipynb), scripts, and data from the old instance to:
  - A Cloud Storage bucket, or
  - Directly to the new instance using gsutil, scp, or Jupyter file upload.
Once migration is confirmed, you can delete the old notebook instance.

These steps ensure that your Vertex AI Workbench notebook instances are created with vTPM enabled via the GCP console.

Using CLI

In Vertex AI Workbench, vTPM is controlled by the Shielded VM settings of the notebook instance. You cannot toggle vTPM on/off for an existing notebook; you must create (or recreate) the instance with vTPM enabled.Below are CLI steps for both User‑managed and Managed notebooks.

1. Identify notebook instances missing vTPM

List all Workbench instances in a region:

PROJECT_ID=your-project-id
REGION=us-central1

gcloud config set project $PROJECT_ID

gcloud notebooks instances list \
  --location=$REGION

To inspect a specific instance’s Shielded VM config:

INSTANCE_ID=your-notebook-id

gcloud notebooks instances describe $INSTANCE_ID \
  --location=$REGION \
  --format="yaml|shieldedInstanceConfig"

If enableVtpm: true is not present, vTPM is not enabled.

2. Recreate a User‑managed notebook with vTPM enabled

Stop and delete the old instance (do not delete attached disks you need):

INSTANCE_ID=old-notebook
REGION=us-central1

gcloud notebooks instances stop $INSTANCE_ID --location=$REGION

gcloud notebooks instances delete $INSTANCE_ID \
  --location=$REGION \
  --quiet

Recreate a new instance with Shielded VM + vTPM:

NEW_INSTANCE_ID=new-notebook
MACHINE_TYPE=e2-standard-4
VM_IMAGE_PROJECT=deeplearning-platform-release
VM_IMAGE_FAMILY=common-cpu-notebooks

gcloud notebooks instances create $NEW_INSTANCE_ID \
  --location=$REGION \
  --machine-type=$MACHINE_TYPE \
  --vm-image-project=$VM_IMAGE_PROJECT \
  --vm-image-family=$VM_IMAGE_FAMILY \
  --shielded-secure-boot \
  --shielded-vtpm \
  --shielded-integrity-monitoring

Key flag: --shielded-vtpm (enables vTPM).If you have an existing persistent disk you want to attach, use the relevant --boot-disk-* flags when creating the instance (or recreate using the same config as the previous instance).

3. Recreate a Managed notebook with vTPM enabled

Managed notebooks are also backed by Shielded VMs; you must create them with vTPM:

NEW_INSTANCE_ID=new-managed-notebook
REGION=us-central1
MACHINE_TYPE=e2-standard-4

gcloud notebooks instances create $NEW_INSTANCE_ID \
  --location=$REGION \
  --vm-image-project=deeplearning-platform-release \
  --vm-image-family=common-cpu-notebooks \
  --machine-type=$MACHINE_TYPE \
  --instance-owners=$(gcloud auth list --filter=status:ACTIVE --format="value(account)") \
  --shielded-secure-boot \
  --shielded-vtpm \
  --shielded-integrity-monitoring

Migrate your notebooks/code from the old managed instance to the new one, then delete the old instance.

If you share the output of gcloud notebooks instances describe for one of your current instances, I can give an exact gcloud notebooks instances create command matching its configuration but with vTPM enabled.

Using Python

Below are concrete steps and a Python example to enable vTPM (Shielded VM) on Vertex AI Workbench (Vertex AI Notebooks) instances.Assumptions:

You’re using Vertex AI Workbench user-managed or managed notebooks (Notebooks API).
You have roles/notebooks.admin (or equivalent) and appropriate project permissions.
gcloud auth application-default login has been run or you’re running in a GCP environment with a service account.

1. Install and import the Notebooks client library

pip install google-cloud-notebooks

from google.cloud import notebooks_v1
from google.api_core.operation import Operation

2. Helper: wait for long‑running operations

def wait_for_op(op: Operation, desc: str = ""):
    print(f"Waiting for operation: {desc} ...")
    result = op.result()  # blocks until done
    print(f"Operation finished: {desc}")
    return result

3. Stop the instance (must be TERMINATED to change Shielded settings)

def stop_notebook_instance(project_id: str, location: str, instance_id: str):
    client = notebooks_v1.NotebookServiceClient()
    name = client.instance_path(project_id, location, instance_id)
    op = client.stop_instance(name=name)
    wait_for_op(op, f"Stop instance {instance_id}")

4. Patch the instance to enable vTPM

For Vertex AI Workbench (Notebooks API), the Shielded VM config is under gce_setup.shielded_instance_config for Workbench instances, or vm_image/container_image for older ones. The common field in the Notebooks API is:

gce_setup.shielded_instance_config.enable_vtpm

def enable_vtpm(project_id: str, location: str, instance_id: str):
    client = notebooks_v1.NotebookServiceClient()
    name = client.instance_path(project_id, location, instance_id)

    # Build the partial instance with only the field we change
    instance = notebooks_v1.Instance(
        name=name,
        gce_setup=notebooks_v1.GceSetup(
            shielded_instance_config=notebooks_v1.Instance.ShieldedInstanceConfig(
                enable_vtpm=True
            )
        ),
    )

    update_mask = {"paths": ["gce_setup.shielded_instance_config.enable_vtpm"]}

    op = client.patch_instance(
        name=name,
        instance=instance,
        update_mask=update_mask,
    )
    wait_for_op(op, f"Enable vTPM on {instance_id}")

If you get an error that gce_setup doesn’t exist (older notebook type), the path may instead be:

update_mask = {"paths": ["shielded_instance_config.enable_vtpm"]}

and you construct:

instance = notebooks_v1.Instance(
    name=name,
    shielded_instance_config=notebooks_v1.Instance.ShieldedInstanceConfig(
        enable_vtpm=True
    ),
)

Use the one that matches your instance type (see the error message / object fields from client.get_instance).

5. Start the instance again

def start_notebook_instance(project_id: str, location: str, instance_id: str):
    client = notebooks_v1.NotebookServiceClient()
    name = client.instance_path(project_id, location, instance_id)
    op = client.start_instance(name=name)
    wait_for_op(op, f"Start instance {instance_id}")

6. Full remediation script (single instance)

from google.cloud import notebooks_v1

def wait_for_op(op, desc=""):
    print(f"Waiting for operation: {desc} ...")
    result = op.result()
    print(f"Operation finished: {desc}")
    return result

def remediate_vtpm_vertex_notebook(project_id: str, location: str, instance_id: str):
    client = notebooks_v1.NotebookServiceClient()
    name = client.instance_path(project_id, location, instance_id)

    # 1) Stop instance
    op = client.stop_instance(name=name)
    wait_for_op(op, f"Stop instance {instance_id}")

    # 2) Enable vTPM (adjust fields/mask if your instance uses a different schema)
    instance = notebooks_v1.Instance(
        name=name,
        gce_setup=notebooks_v1.GceSetup(
            shielded_instance_config=notebooks_v1.Instance.ShieldedInstanceConfig(
                enable_vtpm=True
            )
        ),
    )
    update_mask = {"paths": ["gce_setup.shielded_instance_config.enable_vtpm"]}
    op = client.patch_instance(name=name, instance=instance, update_mask=update_mask)
    wait_for_op(op, f"Enable vTPM on {instance_id}")

    # 3) Start instance
    op = client.start_instance(name=name)
    wait_for_op(op, f"Start instance {instance_id}")

if __name__ == "__main__":
    PROJECT_ID = "your-project-id"
    LOCATION = "us-central1-b"  # region or zone used by your Workbench instance
    INSTANCE_ID = "your-notebook-instance-name"

    remediate_vtpm_vertex_notebook(PROJECT_ID, LOCATION, INSTANCE_ID)

7. Verification (optional, via Python)

def verify_vtpm_enabled(project_id: str, location: str, instance_id: str):
    client = notebooks_v1.NotebookServiceClient()
    name = client.instance_path(project_id, location, instance_id)
    inst = client.get_instance(name=name)
    cfg = inst.gce_setup.shielded_instance_config
    print("vTPM enabled:", cfg.enable_vtpm)

# verify_vtpm_enabled(PROJECT_ID, LOCATION, INSTANCE_ID)

This pattern can be wrapped in a loop to remediate multiple instances.

Using Terraform

resource "google_workbench_instance" "vertex_ai_notebook" {
  name     = "VERTEX_NOTEBOOK_INSTANCE_NAME"  # e.g. "notebook-1"
  location = "REGION"                         # e.g. "us-central1"
  project  = "PROJECT_ID"

  gce_setup {
    machine_type = "MACHINE_TYPE"             # e.g. "e2-standard-4"

    boot_disk {
      disk_type   = "BOOT_DISK_TYPE"          # e.g. "pd-balanced"
      disk_size_gb = BOOT_DISK_SIZE_GB        # e.g. 100
    }

    network_interfaces {
      network    = "NETWORK_SELF_LINK_OR_NAME"
      subnetwork = "SUBNETWORK_SELF_LINK_OR_NAME"
    }

    service_accounts {
      email = "SERVICE_ACCOUNT_EMAIL"
    }

    shielded_instance_config {
      enable_integrity_monitoring = true
      enable_secure_boot          = true
      enable_vtpm                 = true
    }
  }
}

Substitute:

VERTEX_NOTEBOOK_INSTANCE_NAME with your Vertex AI Workbench instance name.
REGION, PROJECT_ID, MACHINE_TYPE, BOOT_DISK_TYPE, BOOT_DISK_SIZE_GB, NETWORK_SELF_LINK_OR_NAME, SUBNETWORK_SELF_LINK_OR_NAME, and SERVICE_ACCOUNT_EMAIL with your values.

Changing shielded_instance_config.enable_vtpm from false to true may force replacement of the underlying VM, causing a brief outage when Terraform recreates the instance.For verification, terraform plan should show enable_vtpm changing from false (or being added) to true in the shielded_instance_config of google_workbench_instance.vertex_ai_notebook (and, if the provider marks it ForceNew, a planned -/+ replacement of the resource).

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GCP Threats

Ensure that vTPM feature is enabled for your Vertex AI notebook instances

More Info:

Risk Level

Address

Compliance Standards

Triage and Remediation

Remediation

1. Check whether vTPM is already enabled

2. Create a new Vertex AI notebook with vTPM enabled

A. Managed notebooks

B. User-managed notebooks

3. Migrate data / notebooks from old instance (if needed)

1. Identify notebook instances missing vTPM

2. Recreate a User‑managed notebook with vTPM enabled

3. Recreate a Managed notebook with vTPM enabled

1. Install and import the Notebooks client library

2. Helper: wait for long‑running operations

3. Stop the instance (must be TERMINATED to change Shielded settings)

4. Patch the instance to enable vTPM

5. Start the instance again

6. Full remediation script (single instance)

7. Verification (optional, via Python)

​More Info:

​Risk Level

​Address

​Compliance Standards

​Triage and Remediation

​Remediation

​1. Check whether vTPM is already enabled

​2. Create a new Vertex AI notebook with vTPM enabled

​A. Managed notebooks

​B. User-managed notebooks

​3. Migrate data / notebooks from old instance (if needed)

​1. Identify notebook instances missing vTPM

​2. Recreate a User‑managed notebook with vTPM enabled

​3. Recreate a Managed notebook with vTPM enabled

​1. Install and import the Notebooks client library

​2. Helper: wait for long‑running operations

​3. Stop the instance (must be TERMINATED to change Shielded settings)

​4. Patch the instance to enable vTPM

​5. Start the instance again

​6. Full remediation script (single instance)

​7. Verification (optional, via Python)

More Info:

Risk Level

Address

Compliance Standards

Triage and Remediation

Remediation

1. Check whether vTPM is already enabled

2. Create a new Vertex AI notebook with vTPM enabled

A. Managed notebooks

B. User-managed notebooks

3. Migrate data / notebooks from old instance (if needed)

1. Identify notebook instances missing vTPM

2. Recreate a User‑managed notebook with vTPM enabled

3. Recreate a Managed notebook with vTPM enabled

1. Install and import the Notebooks client library

2. Helper: wait for long‑running operations

3. Stop the instance (must be TERMINATED to change Shielded settings)

4. Patch the instance to enable vTPM

5. Start the instance again

6. Full remediation script (single instance)

7. Verification (optional, via Python)