Overly broad IAM grants
Principals with excessive permissions can access model artifacts, training data, or prediction endpoints.
AI Security
Google Cloud AI Security Services
Govern AI on Google Cloud with identity, network, and data controls that examiners can verify.
Why GCP AI workloads need security
Google Cloud gives you powerful AI services, from Vertex AI to Gemini, but the shared-responsibility model leaves identity, network boundaries, and data protection in your hands. Misconfigured IAM roles or public buckets are common entry points.
MHCIS maps GCP AI deployments to your compliance program so you can prove control over models, training data, and inference traffic.
Common risks
Public storage and datasets
Cloud Storage buckets or BigQuery datasets used for AI training can be accidentally exposed.
Unencrypted data flows
Data moving between Cloud Run, Vertex AI, and external APIs may lack encryption or proper key management.
Model governance gaps
Without versioning, approval workflows, and drift detection, models run without accountability.
Shadow AI
Teams spin up Gemini or Vertex AI projects outside central visibility and policy.
What MHCIS does
GCP IAM and zero-trust design
Principle-of-least-privilege roles, workload identity, and VPC Service Controls for AI resources.
Data protection
Encryption with Cloud KMS, bucket policies, and data loss prevention for AI datasets and outputs.
Vertex AI governance
Model registries, approval gates, drift detection, and audit logging for the full ML lifecycle.
Network segmentation
Private Service Connect, Cloud NAT, and firewall rules that keep AI traffic off the public internet.
Compliance automation
Continuous control validation and evidence generation for SOC 2, PCI-DSS, HIPAA, and GLBA.
Checklist
Google Cloud AI security essentials
- Audit IAM bindings on Vertex AI, Cloud Storage, and BigQuery resources.
- Enable VPC Service Controls around AI services and data stores.
- Encrypt training data and model artifacts with customer-managed keys.
- Use Private Service Connect for model inference traffic where possible.
- Enable Cloud Audit Logs for all AI admin, data, and activity events.
- Implement model versioning and approval workflows in Vertex AI.
- Scan for public buckets and datasets used in AI pipelines.
Common questions
Straight answers to what regulated buyers ask first.
Related compliance
AI security work maps to the same compliance program we document on these pages.