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Is your Azure estate ready for AI?

A 10-question self-assessment for IT and data leaders. Each gap maps to what CloudOI builds — no pitch required to see your score.

10 questions · yes or no

Honest answers give a clearer gap map. Takes about three minutes.

  1. 1. Are critical Azure data and AI services reachable only via private endpoints (not public by default)?

  2. 2. Do you have a governed data lake (or equivalent) with clear zones for raw, cleansed, and curated data?

  3. 3. Is data catalogued with lineage and sensitivity labels (e.g. Microsoft Purview or equivalent)?

  4. 4. Is elevated admin access just-in-time (no standing global admin for day-to-day work)?

  5. 5. Do production workloads have matching Dev and QA environments (true parity, not “dev shares prod”)?

  6. 6. If you use Azure OpenAI or similar, is it private-network integrated with audit logging?

  7. 7. Are budgets, tags, and policies in place so AI/ML compute cannot run unchecked?

  8. 8. Is centralised logging and alerting covering identity, network, and data platform health?

  9. 9. Is MFA enforced for all users, with Conditional Access protecting admin and Azure management?

  10. 10. Can ML training/inference run in isolated networks with managed identities (no secrets in code)?