Lock Down Your AI — Without Locking Down the Business.
The Zero Trust & AI Data Governance Playbook helps enterprises design a security and governance fabric around data, models, and AI workloads — using Google Cloud, Vertex AI, and MBCC Vapor Cloud patterns.
What This Playbook Actually Delivers
This is a blueprint for building a Zero Trust and data governance layer that keeps up with AI velocity. It covers how identities, networks, data, and AI workloads should behave — and how to prove it to auditors.
Zero Trust Reference Model
An opinionated Zero Trust model for AI on Google Cloud: identity, networks, devices, workloads, and data flows — mapped to practical guardrails and controls.
Data Governance Patterns
How to classify data, segment datasets, and govern access to training corpora, feature stores, and model artifacts without slowing down product teams.
AI-Specific Controls
Controls for notebooks, pipelines, and endpoints: service accounts, VPC Service Controls, CMEK, artifact isolation, and audit logging for AI workloads.
Audit-Ready Story
A story you can tell to regulators, customers, and boards about how AI workloads are secured and governed — with diagrams, responsibilities, and evidence sources.
Who This Is For
The Zero Trust & AI Data Governance Playbook is designed for enterprises who already know AI is strategic — and now must make it safe, compliant, and sustainable.
- CIOs and CISOs responsible for enterprise cloud and data security.
- Chief Data Officers and Heads of AI who need a governance story.
- Platform and security architects designing AI platforms on Google Cloud.
- Risk, compliance, and legal teams who must certify AI-powered products.
What’s Inside the Playbook
The playbook is a combination of security architecture, governance patterns, and operating model guidance.
- Zero Trust principles mapped to AI workloads and data flows.
- Google Cloud control set: IAM, VPC Service Controls, CMEK, Secret Manager, logging, and monitoring.
- Patterns for securing Vertex AI: training pipelines, endpoints, model registry, and artifacts.
- Data governance building blocks: classification, lineage, catalogs, and approvals.
- Operating model: who owns which decisions, and how security reviews fit into AI delivery.
Engagement Options
You can use this as an internal framework or bring in a Vapor Cloud Digital Leader (VCDL) to move faster.
- Assessment & Blueprint: Rapid current-state review and target Zero Trust / governance design.
- Control Implementation: Landing Google Cloud controls and policies in your environment.
- Platform Hardening: Hardening your AI platforms, pipelines, and endpoints using the playbook.
- Audit & Customer Story: Packaging the security and governance story for external stakeholders.