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Google Cloud Platform in Regulated Environments

GCP for regulated data and AI workloads

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Compliance Context

What Regulated Teams Get Wrong with Google Cloud Platform

Google Cloud Platform's compliance footprint is growing rapidly, but its regulatory coverage in healthcare and government is narrower than AWS or Azure for many workloads. GCP's HIPAA BAA covers core services including BigQuery, Cloud Storage, Compute Engine, and Cloud Run, but customers must explicitly accept the BAA and configure services to the required safeguards. BigQuery is a particular compliance surface in healthcare: it is extremely easy to accidentally expose a PHI-containing dataset to all users in a GCP organization through IAM policy inheritance, and BigQuery's column-level security — which would restrict PHI columns to authorized principals — is not enabled by default. GCP's Vertex AI platform is increasingly used for regulated ML workloads, but the data residency of training jobs and model artifacts must be explicitly configured — GCP may process ML workloads in any region by default. Under GDPR, GCP's data processing terms and regional configuration determine where EU personal data is processed, but services like Cloud CDN and Cloud Armor may process request data at global edge nodes. For FedRAMP Moderate and High authorizations, workloads must run on Google Cloud's FedRAMP-authorized infrastructure with specific service and configuration restrictions that differ from standard GCP.

Common Mistakes
Not accepting the GCP HIPAA BAA before processing PHI — the shared responsibility model requires explicit agreement
BigQuery IAM bindings at the dataset level without column-level security — PHI columns are accessible to anyone with dataset access
Vertex AI training jobs without explicit region configuration — Google may process ML jobs in any region
Cloud Storage buckets with fine-grained ACLs instead of uniform bucket-level access — per-object ACLs create inconsistent access control
VPC without Service Controls — lateral movement between projects through GCP APIs is possible without perimeter controls
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How We Use It

Google Cloud Platform in Our Regulated Engagements

We configure GCP environments for regulated workloads with organization-level policies that establish the compliance baseline before any project is created. Organization Policies are used to deny non-compliant configurations at the GCP resource hierarchy level: domain-restricted sharing prevents IAM bindings to external domains, uniform bucket-level access prevents per-object ACLs on Cloud Storage, and resource location restrictions enforce data residency at the organization level. BigQuery datasets that contain PHI or PII are provisioned with column-level security policies applied before any data is loaded, and all BigQuery audit logs are enabled and shipped to a dedicated compliance log project. VPC Service Controls are implemented for all PHI-handling projects to prevent data exfiltration through GCP APIs.

Cloud Infrastructure & Migration
Governance

Compliance Enforcement at the Code Level

GCP governance in our engagements is enforced through Organization Policies, VPC Service Controls, Security Command Center, and Terraform. Organization Policies with deny effects enforce the compliance baseline across all projects in the organization — individual project owners cannot override organization-level constraints. Security Command Center Standard or Premium is enabled to detect misconfigurations, vulnerabilities, and anomalous behavior. Terraform manages all GCP resource provisioning with compliance-validated modules that include the required IAM, encryption, and logging configuration. SentienGuard integrates with Cloud Logging and Security Command Center to generate continuous compliance evidence.

A
ALICE — Autonomous Compliance Engine

ALICE validates every commit against the applicable regulatory framework before it merges. Compliance violations are caught at the commit level — not in production, not in an audit finding.

Production Scenario

In Production

A pharma company's data science team engaged us to build a GCP-based genomics data platform under HIPAA and their IRB protocol. The platform ingests sequencing data from clinical sites, runs de-identification pipelines, and exposes analysis results through a researcher portal. We implemented BigQuery column-level security for all PHI fields, VPC Service Controls around the de-identification pipeline, and organization-level data residency restrictions. The platform passed both the IRB data security review and the company's HIPAA Privacy Officer review at first submission.

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We build Google Cloud Platform systems for healthcare, financial services, energy, and government. Compliance-native from architecture. Fixed-price delivery.

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Cloud Security & Compliance Architecture Guide

GCP Organization Policy configuration, VPC Service Controls, and BigQuery column-level security for regulated data workloads.

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