jpantsjoha Azure Expert
Azure is policy-first: Entra ID and Azure Policy are the guardrails; the Foundry is the agent surface. If it is not enforced by policy, it is a wish.
This skill enforces the discipline that makes Azure workloads production-safe: identity, policy, data boundaries, cost controls, and residency. It is not an Azure feature tour — it is a checklist of the things that cause incidents and compliance failures when skipped.
When to use
- Designing any Azure infrastructure (new or modified)
- Before deploying agents or LLM workloads to Azure (AI Foundry, Azure OpenAI, Azure AI Agent Service)
- When reviewing a Bicep/Terraform plan or a landing-zone design
- When a system spans subscriptions, touches regulated data, or crosses geographies
Procedure
Identity and access — verify least-privilege for every identity:
- Managed identities over service principals with secrets; no client secrets in code or config.
- Entra ID RBAC scoped to the specific function; PIM for standing privileged access.
- Conditional Access policies for human principals on production subscriptions.
Policy and landing zones — confirm governance is mechanical:
- Azure Policy assignments enforce allowed locations, required encryption, and denied public endpoints.
- Workload sits inside a Cloud Adoption Framework landing zone (or an explicit, owned deviation).
Data boundaries — for every data store:
- Classification recorded (Purview where in scope); CMK where required.
- Private Endpoints on PaaS data services; public network access disabled by default.
- Cross-tenant or cross-subscription sharing explicit and documented.
Data residency — for each resource:
- Allowed-locations policy constrains deployment geography (e.g. EU Data Boundary where required).
- For Azure OpenAI / Foundry calls: regional deployments, not global, where residency matters.
Cost controls — for every LLM, compute, or storage resource:
- Cost Management budgets with alerts at 50%, 75%, 90%, 100%.
- Foundry model quotas and rate limits set; autoscale maximums bounded.
Network and egress — confirm:
- Private Link over public endpoints; NSGs default-deny.
- Egress costed for cross-region and internet-bound traffic.
Observability — confirm:
- Azure Monitor / Log Analytics dashboards and alerts on error rate, latency, and cost.
- Activity logs routed centrally for retention and audit — evidence before go-live.
Run the Adversarial Gate — common Azure failure modes: service principals with secrets, public PaaS endpoints, missing allowed-locations policy, uncapped Foundry spend, standing owner access without PIM.
Official sources — validate before you assert
- Documentation:
learn.microsoft.com/azure· Well-Architected:learn.microsoft.com/azure/well-architected - Live docs via MCP (verify currency before pinning): the official Azure MCP Server (
github.com/microsoft/azure-mcp) and the Microsoft Learn docs MCP endpoint — fetch current docs instead of relying on memory. - GitHub, foundations:
github.com/Azure/Enterprise-Scale(CAF landing zones) ·github.com/Azure/bicep·github.com/Azure-Samples - GitHub, agent examples: Azure AI Agent Service samples under
github.com/Azure-Samples· AutoGen (github.com/microsoft/autogen) · Semantic Kernel (github.com/microsoft/semantic-kernel) - Rule: every service/API claim cites an official doc. Quotas, prices, and model names are dated facts — stale until re-verified against the source.
Outputs
- Azure guardrail checklist (pass/fail per item)
- Identity matrix: principal | role | scope | justification
- Data classification and boundary map
- Budget alert confirmation
- Open findings for human review
Guardrails
- No client secrets in code or config. Managed identity or nothing.
- Policy, not promises. If a guardrail is not an Azure Policy assignment, it does not exist.
- Residency is a constraint, not a preference. Enforce with allowed-locations policy.
- Budget alerts are not optional. An unmonitored LLM workload will produce a surprise invoice.