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

# 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

# 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.

# Grant your user the Token Creator role on the target service account
gcloud iam service-accounts add-iam-policy-binding \
  prod-deploy@my-project.iam.gserviceaccount.com \
  --member="user:alice@example.com" \
  --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

# Cloud Run
gcloud run deploy api \
  --image=us-central1-docker.pkg.dev/my-project/repo/api:v1 \
  --region=us-central1 \
  --service-account=api-runtime@my-project.iam.gserviceaccount.com

# Compute Engine
gcloud compute instances create web-1 \
  --zone=us-central1-a \
  --service-account=web-runtime@my-project.iam.gserviceaccount.com \
  --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

# 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 \
  orders-api@my-project.iam.gserviceaccount.com \
  --role="roles/iam.workloadIdentityUser" \
  --member="serviceAccount:my-project.svc.id.goog[default/orders-api]"

apiVersion: v1
kind: ServiceAccount
metadata:
  name: orders-api
  namespace: default
  annotations:
    iam.gke.io/gcp-service-account: orders-api@my-project.iam.gserviceaccount.com

Workload Identity Federation for AWS / Azure / On-Prem

# 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 \
  cross-cloud-reader@my-project.iam.gserviceaccount.com \
  --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

# 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())

# 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:caller-runtime@my-project.iam.gserviceaccount.com" \
  --role="roles/run.invoker"

API Keys (Limited Use Cases)

# 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 Audit Logs and the Recommender's "unused service accounts" insight