Active Portfolio Build
Secure AI Governance on Azure
A staged cloud security governance implementation focused on identity controls, policy guardrails, data protection, controlled AI access, and audit-ready evidence for secure AI adoption.
Executive Summary
A controlled implementation pattern for secure AI adoption.
This case study documents a staged Azure security governance implementation focused on secure AI adoption. The build demonstrates how identity controls, policy guardrails, data protection, controlled AI access, and audit-ready evidence can be combined into a defensible cloud security governance pattern.
The scenario assumes an enterprise environment preparing to adopt AI-enabled services while managing risks related to excessive privilege, data leakage, unmanaged model access, weak monitoring, and incomplete governance evidence.
Governance Objectives
The build is organized around security outcomes, not tool deployment alone.
Establish least-privilege access for AI-supporting cloud services.
Apply Azure governance guardrails before sensitive workloads are deployed.
Protect secrets, keys, and classified data paths used by AI-enabled services.
Control AI service access through identity, policy, network, and gateway layers.
Capture audit-ready evidence that maps technical implementation to governance outcomes.
Planned Architecture
Layered controls for identity, guardrails, data, AI access, and evidence.
The architecture is intentionally governance-first: each technical layer must produce implementation evidence that supports security review and risk decisions.
Identity Control Plane
Microsoft Entra ID, RBAC, Conditional Access, PIM, Managed Identities
Define who can administer, access, and operate cloud and AI resources under least-privilege conditions.
Governance Guardrails
Management Groups, Azure Policy, Defender for Cloud
Apply policy-based controls, posture visibility, and documented exception handling across the environment.
Data Protection
Key Vault, Encryption, Microsoft Purview, Sensitivity Labels, DLP
Protect secrets, classify sensitive data, and demonstrate defensible data handling for AI-adjacent workflows.
Controlled AI Access
Azure AI / Azure OpenAI, Private Networking, APIM / AI Gateway, Content Safety
Restrict and monitor model access using identity-aware, policy-driven, and observable access patterns.
Monitoring & Evidence
Log Analytics, Microsoft Sentinel, Policy Exports, Risk Register, Control Matrix
Collect validation evidence that supports auditability, incident review, and governance reporting.
Implementation Roadmap
AIFest2026 training converted into a portfolio production track.
Week 0
Lab Readiness
Rebuild a clean Azure lab scope, establish management structure, enable core logging, and prepare evidence folders.
Weeks 1–2
Identity & Access
Create RBAC model, Conditional Access baseline, privileged access workflow, and managed identity pattern.
Weeks 3–4
Governance & Posture
Assign Azure Policy initiatives, review Defender for Cloud posture, and document compliance deltas.
Weeks 5–6
Data Protection
Harden Key Vault, validate secret access, configure classification labels, and capture DLP evidence.
Weeks 7–8
Secure AI Access
Design controlled AI access using private networking, managed identity, gateway controls, and safety evaluation.
Weeks 9–10
Monitoring & Audit Evidence
Configure detection logic, incident workflow, compliance exports, and evidence packages.
Weeks 11–12
Case Study Publication
Consolidate diagrams, control matrix, risk register, outcomes, and final BuiltByPCB narrative.
Evidence Artifacts
What this build will produce.
Secure AI governance architecture diagram
Entra RBAC model and role map
Conditional Access baseline
PIM workflow or privileged access model
Azure Policy initiative and assignment evidence
Defender for Cloud before/after posture evidence
Key Vault private endpoint and firewall configuration evidence
Purview sensitivity label and DLP evidence
APIM / AI Gateway policy examples
Content Safety or evaluation results
Sentinel analytics and incident lifecycle evidence
Cloud Security Governance control matrix
Risk register and governance decision log
Target Outcomes
What “done” means.
Identity
Least-privilege role model, privileged access workflow, and managed identity usage documented.
Governance
Policy-based guardrails assigned and exceptions documented with owner, reason, and review path.
Posture
Defender for Cloud baseline captured and improvement evidence documented.
Data
Secrets, keys, labels, and DLP controls demonstrated with sanitized implementation evidence.
AI Access
AI service access controlled through identity, network, gateway, and safety controls.
Auditability
Evidence mapped to a control matrix, risk register, and case study narrative.
Current Status
Active portfolio build with staged evidence publication.
This page currently serves as the public shell for the Secure AI Governance case study. Evidence will be added as each control domain is implemented, validated, and documented. No production claims are made until the supporting artifacts are available.