Decentralized IAM and Multi-Cloud Security: Building Zero Trust at Scale

When your infrastructure spans AWS, GCP, and Azure, there is no perimeter. Identity and access management (IAM) that routes all authentication through a single centralized provider creates a single point of failure that cascades across every cloud environment when it goes down. Decentralized IAM eliminates that failure mode by distributing trust via cryptographic workload identity federation -- each environment acts as its own identity provider, federating with others through OIDC rather than shared static credentials.

IBM's Cost of Data Breach 2025 report identifies compromised credentials as the most common initial attack vector, accounting for 16% of breaches at an average cost of $4.81 million per incident. In Seven Labs' VAPT engagements, overly permissive cross-cloud IAM roles and static service account credentials are among the three most recurring critical findings -- and the ones that take longest for organizations to remediate because they are embedded across infrastructure-as-code, CI/CD pipelines, and developer tooling simultaneously.

What Is the Hub-and-Spoke Identity Trap and Why Does It Break Multi-Cloud Security?

The hub-and-spoke identity trap is what happens when organizations route all multi-cloud authentication through a single centralized IdP. Every service in AWS that accesses a resource in GCP must authenticate through the central hub first. This design creates three compounding failure modes that make it unsuitable for serious multi-cloud deployments.

First, latency: microservices making hundreds of internal API calls per second cannot absorb the round-trip cost of a centralized auth check on each request. Second, availability: if the central IdP becomes unavailable, the entire multi-cloud infrastructure halts. Third, blast radius: a compromised central directory grants an attacker administrative access across all cloud environments simultaneously -- AWS, GCP, and Azure in a single credential compromise.

Forrester Research reports that 78% of organizations operating in multi-cloud environments identify IAM complexity as their top security concern. [Source: Forrester State of Zero Trust 2024] The root cause is almost always the attempt to extend a centralized hub-based identity model into an environment that was built for distributed, federated trust.

The correct model requires identities to be verified locally and managed globally. Each cloud environment acts as its own identity provider for the workloads it hosts. Those local providers federate trust with other environments using OpenID Connect (OIDC), with no long-lived static credential exchanged across cloud boundaries.

"Static credentials are the enemy of cloud security at scale. Every long-lived API key or service account JSON file in a multi-cloud environment is a ticking clock. The only durable solution is workload identity federation with ephemeral, scoped tokens." -- Armon Dadgar, Co-Founder, HashiCorp

How Do IAM Approaches Differ Across AWS, GCP, and Azure in a Multi-Cloud Architecture?

The three major cloud providers each implement workload identity differently. Understanding these differences is required before building a federated trust model that spans environments without introducing static credentials or brittle integrations.

In Seven Labs' security architecture deployments across multi-cloud environments, SPIFFE/SPIRE serves as the unified workload identity layer when service-to-service mTLS is required across cloud boundaries. SPIRE issues X.509-encoded SVID certificates to workloads, enabling mutual TLS without requiring each cloud's native identity system to directly trust the others -- reducing integration surface and simplifying certificate rotation.

Why Is Establishing Cross-Cloud Workload Identity Federation Without Static Credentials Technically Difficult?

The challenge is establishing workload identity federation across disparate trust domains without a shared secret or long-lived credential at any step in the chain. When a workload in AWS EKS needs to read from a GCP Cloud Storage bucket, GCP must verify the AWS workload's identity using cryptographic proof -- not a credential that could be extracted and reused.

The naive approach stores static GCP service account JSON files or AWS access keys in Kubernetes Secrets. This is the most common pattern and the most dangerous. Static credentials leak through git history, build logs, and container image layers. They are rarely rotated automatically, and when compromised, attackers retain access until someone manually identifies and revokes the specific credential -- which on average takes 292 days. [Source: IBM Cost of Data Breach 2025]

The solution is OIDC-based workload identity federation. The AWS EKS cluster has an associated OIDC issuer URL. GCP can be configured to trust that OIDC issuer, mapping specific OIDC claims (the Kubernetes service account name and namespace, encoded in the

sub

"The fundamental shift in cloud IAM is from 'what credentials does this service have' to 'what is this service's identity and what is it allowed to do right now.' Ephemeral, scope-limited tokens backed by cryptographic workload identity are the only model that scales safely." -- Kelsey Hightower, Principal Engineer, Google Cloud

How Do You Implement AWS EKS to GCP BigQuery Access Without Static Credentials?

This implementation establishes workload identity federation between an AWS EKS data ingestion service and a GCP BigQuery dataset. No static credentials are stored in the pod, the container image, Kubernetes Secrets, or any CI/CD system.

Step 1: Associate an OIDC provider with the EKS cluster.

1# Terraform v1.8.0 / AWS Provider v5.40.0
2data "tls_certificate" "eks" {
3  url = aws_eks_cluster.main.identity[0].oidc[0].issuer
4}
5
6resource "aws_iam_openid_connect_provider" "eks" {
7  client_id_list  = ["sts.amazonaws.com"]
8  thumbprint_list = [data.tls_certificate.eks.certificates[0].sha1_fingerprint]
9  url             = aws_eks_cluster.main.identity[0].oidc[0].issuer
10}

Step 2: Create a GCP Workload Identity Pool and Provider pointing to the EKS OIDC issuer.

1# GCP Provider v5.20.0
2resource "google_iam_workload_identity_pool" "aws_pool" {
3  workload_identity_pool_id = "aws-eks-pool"
4  display_name              = "AWS EKS Trust Pool"
5  description               = "Identity pool for EKS workloads"
6}
7
8resource "google_iam_workload_identity_pool_provider" "aws_provider" {
9  workload_identity_pool_id          = google_iam_workload_identity_pool.aws_pool.workload_identity_pool_id
10  workload_identity_pool_provider_id = "aws-eks-provider"
11  display_name                       = "AWS EKS OIDC Provider"
12
13  attribute_mapping = {
14    "google.subject"       = "assertion.sub"
15    "attribute.namespace"  = "assertion['kubernetes.io']['namespace']"
16  }
17
18  oidc {
19    issuer_uri = aws_eks_cluster.main.identity[0].oidc[0].issuer
20  }
21}

Step 3: Bind the GCP Service Account to the specific Kubernetes Service Account via the Identity Pool.

1resource "google_service_account" "bq_writer" {
2  account_id   = "bq-writer-sa"
3  display_name = "BigQuery Writer Service Account"
4}
5
6resource "google_project_iam_member" "bq_data_editor" {
7  project = var.project_id
8  role    = "roles/bigquery.dataEditor"
9  member  = "serviceAccount:${google_service_account.bq_writer.email}"
10}
11
12resource "google_service_account_iam_member" "workload_identity_user" {
13  service_account_id = google_service_account.bq_writer.name
14  role               = "roles/iam.workloadIdentityUser"
15  member = "principal://iam.googleapis.com/${google_iam_workload_identity_pool.aws_pool.name}/subject/system:serviceaccount:data-ingest:bq-writer"
16}

Step 4: Configure the Kubernetes Service Account and Pod.

1apiVersion: v1
2kind: ServiceAccount
3metadata:
4  name: bq-writer
5  namespace: data-ingest
6  annotations:
7    eks.amazonaws.com/role-arn: arn:aws:iam::123456789012:role/bq-writer-role
8---
9apiVersion: v1
10kind: Pod
11metadata:
12  name: bq-writer-app
13  namespace: data-ingest
14spec:
15  serviceAccountName: bq-writer
16  containers:
17  - name: app
18    image: my-registry/bq-writer:v1.2.0
19    env:
20    - name: GOOGLE_APPLICATION_CREDENTIALS
21      value: /var/run/secrets/google/credentials.json
22    volumeMounts:
23    - name: google-cloud-key
24      mountPath: /var/run/secrets/google
25    - name: k8s-token
26      mountPath: /var/run/secrets/tokens
27  volumes:
28  - name: google-cloud-key
29    configMap:
30      name: workload-identity-config
31  - name: k8s-token
32    projected:
33      sources:
34      - serviceAccountToken:
35          path: oidc-token
36          expirationSeconds: 3600
37          audience: sts.amazonaws.com

The application uses the Google Cloud SDK, which detects the projected OIDC token, authenticates against the GCP Workload Identity Pool, and retrieves a short-lived access token for the

bq-writer-sa

What Are the Critical Pitfalls That Break Decentralized IAM Implementations?

Based on Seven Labs' security architecture deployments, three configuration errors appear consistently in decentralized IAM implementations. Each one can undermine the entire security model while appearing to function normally until a breach makes the gap visible.

Broad claim mapping. When configuring Workload Identity Provider attribute mappings, do not map broad claims such as

assertion.aud

sub

system:serviceaccount:namespace:sa-name

Token expiration handling. Projected Kubernetes service account tokens expire, typically within one hour. Applications with custom authentication logic that cache tokens indefinitely fail after the first hour with authentication errors that are difficult to distinguish from network failures. Ensure the application uses a Google Cloud SDK that handles automatic token refresh, or implement explicit re-fetch logic with a TTL buffer that accounts for clock skew across cloud environments.

SIEM blind spots from decentralized audit logs. Distributing identity across cloud environments distributes audit logs with it. You must aggregate AWS CloudTrail, GCP Cloud Audit Logs, and Kubernetes API server audit logs into a central SIEM. When a workload identity is compromised, the attack path crosses cloud boundaries and spans multiple log sources. Without correlated logs, you cannot reconstruct the incident timeline. Correlate the

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Frequently Asked Questions

What is the difference between decentralized IAM and federated IAM? Federated IAM connects a single central IdP to multiple applications via SAML or OIDC for user authentication. Decentralized IAM goes further: each environment acts as its own workload identity provider, federating machine-to-machine trust across cloud boundaries using cryptographic attestation rather than centrally issued and stored credentials. The key distinction is eliminating the central IdP as a shared dependency and single point of failure.

Does SPIFFE/SPIRE work across AWS, GCP, and Azure simultaneously? Yes. SPIRE supports multiple node attestors for different cloud providers and issues SVIDs to workloads regardless of which cloud they run in. This makes SPIRE an effective unified identity layer for mTLS service mesh communication across all three major clouds. SVID certificates rotate automatically at a configurable interval, reducing the credential exposure window without requiring manual rotation processes.

How does decentralized IAM affect SOC 2 compliance posture? Positively. SOC 2 Trust Services Criteria require demonstrating least privilege access and logging of access events. Decentralized IAM with ephemeral tokens satisfies the least privilege requirement by scoping credentials to specific resources with short TTLs. Aggregated audit logs from workload identity systems satisfy the monitoring and logging criteria for access control review across the organization's full cloud footprint.

What happens to cross-cloud access if the AWS OIDC issuer becomes temporarily unavailable? Access attempts requiring token exchange will fail until the OIDC issuer is reachable again. Mitigate this by designing workloads with credential caching that respects token expiration times, reducing the frequency of live OIDC calls. For critical data pipeline workloads, run redundant EKS clusters with separate OIDC issuers that the GCP Workload Identity Pool trusts, distributing the dependency across multiple control planes.

Decentralized IAM is a foundational control for any engineering organization operating across cloud providers. Seven Labs' VAPT penetration testing specifically evaluates cross-cloud IAM configurations for credential leakage, overly permissive role bindings, and misconfigured OIDC trust relationships. Our AI platforms practice designs IAM architecture as part of full-stack cloud security engagements. Contact our security team to assess your multi-cloud IAM posture. Related reading: Zero Trust Network Access in SaaS and VAPT Security Threats.