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chore: Full consistency scan — AOC->PULSAR, fix training-data claims, fix 90% claim

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AOC -> PULSAR across 10 files (engagement-model, retained-capability,
modular-engagements, blue-purple-team-foundation, about-cqre, about-cqre-cs,
consultant-field-guide, ai-assisted-tvm, m365-e3-hardening,
sovereign-tool-stack, risk-register-example).

Training-data framing corrected in:
- executive-summary.md: opening paragraph and risk table
- README.md: 90% solution claim -> 30-60% in 180 days
- modular-engagements.md: public API data use claim
- cis-controls-mapping.md: data protection framing
- antifragile-risk-register.md: risk entry softened to accurate framing
- azure-openai-sovereignty-bridge.md: consumer vs enterprise API distinction

Co-Authored-By: Tom Kracmar <tom+claude@cat6.cz>
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Claude Sonnet 4.6
2026-06-05 07:05:13 +00:00
parent bcebf8ebb3
commit 3062e435ca
16 changed files with 55 additions and 55 deletions
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antifragile-consulting/core/azure-openai-sovereignty-bridge.md
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@@ -10,7 +10,7 @@ It is designed for M365/Azure consultancies whose clients are not ready for on-p
## The Executive Summary
Your clients are likely using ChatGPT, Claude, or Gemini via public APIs and consumer accounts. Every prompt leaves their perimeter, and the terms of service allow model improvement using that data. This is the worst possible posture.
Your clients are likely using ChatGPT, Claude, or Gemini via consumer accounts or unmanaged public APIs — where data residency is uncontrolled, audit rights are absent, and (for consumer tiers) terms of service may permit model improvement using submitted data. This is the worst possible posture.
**Azure OpenAI Service is not fully sovereign.** Microsoft operates the infrastructure. The underlying models are shared. But it offers something critical that public APIs do not:
@@ -204,7 +204,7 @@ For E3 clients, Azure OpenAI is a **separate Azure subscription**—it does not
|---------|----------|
| "Is this just another Microsoft lock-in?" | "It reduces lock-in compared to public APIs because your fine-tuned models, embeddings, and RAG pipelines are portable assets. When you are ready for full local AI, you migrate them. We are using Azure as a warehouse, not a prison." |
| "Why not go straight to local AI?" | "Local AI requires hardware procurement, infrastructure setup, and expertise development—typically 3-6 months. Azure OpenAI stops the data leakage in 2 weeks while we build the local capability in parallel." |
| "How is this different from just using ChatGPT?" | "ChatGPT trains on your data. Azure OpenAI explicitly does not. ChatGPT has no audit trail. Azure OpenAI logs every prompt. ChatGPT offers no data residency guarantee. Azure OpenAI keeps your data in your region. The difference is governance, not capability." |
| "How is this different from just using ChatGPT?" | "Consumer ChatGPT may use your data for model improvement; Azure OpenAI explicitly does not. Consumer ChatGPT has no audit trail; Azure OpenAI logs every prompt. Consumer ChatGPT offers no data residency guarantee; Azure OpenAI keeps your data in your chosen region. The difference is governance and compliance, not capability." |
| "What if Microsoft changes the terms?" | "The data processing agreement is contractually binding. More importantly, the assets we build in Foundry are portable. If terms change unfavorably, we exercise the exit option we have been building toward all along." |
| "Will this slow down our AI adoption?" | "It will accelerate safe adoption. Employees currently use unauthorized AI because there is no sanctioned alternative. Azure OpenAI gives them a better, safer tool. Adoption goes up; risk goes down." |