gcp-auth

What this skill does

# GCP Authentication & Authorization

## Overview

Authentication answers "who are you?" — authorization (IAM) answers "what can you do?" Google Cloud uses three concepts together: **Principals** (user accounts, service accounts, federated identities), short-lived **tokens** issued by metadata servers or the Service Account Credentials API, and **IAM Allow Policies** that bind principals to predefined or custom roles on resources.

The right pattern depends on where the code runs and who is calling.

## Instructions

### Decision Matrix

| Scenario | Use |
|---|---|
| Local development, calling Google APIs | `gcloud auth application-default login` (ADC) |
| Local dev, but you need to act as a service account | Service account impersonation (NOT keys) |
| Code on Compute Engine / Cloud Run / Cloud Functions | Attached service account + metadata server |
| Code on GKE | Workload Identity Federation for GKE |
| Code on AWS / Azure / on-prem | Workload Identity Federation (with external IdP) |
| Calling a private Cloud Run / Cloud Functions service | OIDC ID token in `Authorization: Bearer` |
| Public / quota-limited APIs (Maps, Vertex AI Express) | API key with restrictions |

If a downloadable service account key (JSON) appears in the answer for any of these, the answer is wrong.

### Local Development with Application Default Credentials

```bash
# One-time setup: log in as your human account
gcloud auth login

# Create local ADC (this is what client libraries use)
gcloud auth application-default login

# Set the project for downstream commands
gcloud config set project my-project
gcloud auth application-default set-quota-project my-project
```

```python
# No credentials in code — ADC is picked up automatically
from google.cloud import storage

client = storage.Client()  # uses ~/.config/gcloud/application_default_credentials.json
for blob in client.list_blobs("my-bucket"):
    print(blob.name)
```

ADC search order:
1. `GOOGLE_APPLICATION_CREDENTIALS` env var (path to JSON)
2. `gcloud auth application-default login` file
3. Attached service account on the runtime (GCE, Cloud Run, GKE, etc.)

### Service Account Impersonation (Local Dev, Production-Like)

Never download service account keys for local testing. Impersonate instead.

```bash
# Grant your user the Token Creator role on the target service account
gcloud iam service-accounts add-iam-policy-binding \
  [email protected] \
  --member="user:[email protected]" \
  --role="roles/iam.serviceAccountTokenCreator"

# Use impersonation for one command
gcloud storage buckets list \
  --impersonate-service-account=prod-deploy@my-project.iam.gserviceaccount.com

# Or set ADC to use impersonation persistently
gcloud auth application-default login \
  --impersonate-service-account=prod-deploy@my-project.iam.gserviceaccount.com
```

### Production: Attached Service Accounts

```bash
# Cloud Run
gcloud run deploy api \
  --image=us-central1-docker.pkg.dev/my-project/repo/api:v1 \
  --region=us-central1 \
  [email protected]

# Compute Engine
gcloud compute instances create web-1 \
  --zone=us-central1-a \
  [email protected] \
  --scopes=cloud-platform

# Cloud Functions (2nd gen)
gcloud functions deploy process-events \
  --gen2 --region=us-central1 \
  --runtime=python311 \
  --trigger-topic=events \
  --service-account=events-runtime@my-project.iam.gserviceaccount.com
```

The runtime fetches a short-lived OAuth token from the local metadata server — application code calls `google.auth.default()` and just works.

### Workload Identity Federation for GKE

```bash
# Enable on the cluster
gcloud container clusters update prod-cluster \
  --region=us-central1 \
  --workload-pool=my-project.svc.id.goog

# Bind a Kubernetes ServiceAccount to a Google Service Account
gcloud iam service-accounts add-iam-policy-binding \
  [email protected] \
  --role="roles/iam.workloadIdentityUser" \
  --member="serviceAccount:my-project.svc.id.goog[default/orders-api]"
```

```yaml
apiVersion: v1
kind: ServiceAccount
metadata:
  name: orders-api
  namespace: default
  annotations:
    iam.gke.io/gcp-service-account: [email protected]
```

### Workload Identity Federation for AWS / Azure / On-Prem

```bash
# Create a workload identity pool
gcloud iam workload-identity-pools create aws-pool \
  --location=global --display-name="AWS Pool"

# Add an AWS provider
gcloud iam workload-identity-pools providers create-aws aws \
  --location=global \
  --workload-identity-pool=aws-pool \
  --account-id=123456789012

# Bind an AWS IAM role to a GSA
gcloud iam service-accounts add-iam-policy-binding \
  [email protected] \
  --role="roles/iam.workloadIdentityUser" \
  --member="principalSet://iam.googleapis.com/projects/PROJECT_NUMBER/locations/global/workloadIdentityPools/aws-pool/attribute.aws_role/arn:aws:sts::123456789012:assumed-role/AppRole"
```

### OIDC ID Tokens for Service-to-Service Calls

```python
# Service A (Cloud Run) calling Service B (private Cloud Run)
import google.auth.transport.requests
import google.oauth2.id_token
import requests

audience = "https://orders-api-xyz-uc.a.run.app"
auth_req = google.auth.transport.requests.Request()
id_token = google.oauth2.id_token.fetch_id_token(auth_req, audience)

response = requests.get(
    f"{audience}/orders/123",
    headers={"Authorization": f"Bearer {id_token}"},
)
print(response.json())
```

```bash
# Grant Cloud Run invoker on Service B to Service A's service account
gcloud run services add-iam-policy-binding orders-api \
  --region=us-central1 \
  --member="serviceAccount:[email protected]" \
  --role="roles/run.invoker"
```

### API Keys (Limited Use Cases)

```bash
# Create an API key restricted to a specific API and HTTP referrer
gcloud alpha services api-keys create \
  --display-name="maps-frontend" \
  --api-target=service=maps-backend.googleapis.com \
  --allowed-referrers="https://app.example.com/*"
```

Store the key in Secret Manager, never commit to git, and always restrict it.
## Examples

### Example 1 — Get a developer set up to call BigQuery

User clones the repo and needs to run integration tests locally. Walk them through `gcloud auth application-default login`, `gcloud config set project my-project`, and `gcloud auth application-default set-quota-project my-project`. Confirm the Python BigQuery client picks up ADC with no code changes. If they need to act as a service account, set up impersonation rather than handing them a JSON key.

### Example 2 — Migrate a GKE workload off a JSON key Secret

User's deployment mounts a service account JSON key as a Kubernetes Secret. Enable Workload Identity on the cluster, create a Kubernetes ServiceAccount with the `iam.gke.io/gcp-service-account` annotation, bind it to the existing GSA via `roles/iam.workloadIdentityUser`, redeploy without the Secret mount, then delete both the Secret and the underlying GSA key.

## Guidelines

- **Never download service account keys** — use impersonation, attached SAs, or Workload Identity Federation
- **Never use the Compute Engine default service account** in production — create a dedicated, least-privilege SA per workload
- For local dev, **ADC + impersonation** is the only acceptable pattern
- Use **Workload Identity for GKE** — JSON keys mounted as Secrets are a security anti-pattern
- For service-to-service calls, use **OIDC ID tokens**, not API keys or shared secrets
- **Restrict API keys** by API, IP, and HTTP referrer; store them in Secret Manager
- Compute Engine VMs and GKE node pools have **OAuth access scopes** that override IAM — check these first if attached SA calls fail unexpectedly
- Prefer **predefined roles** over custom roles; use custom only when predefined are too broad
- Audit service account usage via Cloud A