Zero-Trust RAG Architecture for Regulated Enterprises
Challenge: A regulated enterprise client required a Generative AI solution to democratize access to internal knowledge bases. However, the initiative was previously blocked by the CISO due to critical "Shadow AI" risks.
0%
Network Isolation
0%
Response Determinism
0%
Audit Capability
RAG combines the power of large language models with enterprise data retrieval. Instead of relying solely on the model's training data, RAG retrieves relevant context from your documents and feeds it to the LLM, ensuring responses are grounded in your actual data.
Why RAG over Fine-Tuning? RAG allows you to update knowledge without retraining the model. It's cost-effective, maintains data sovereignty, and provides explainability through source citations.
The AI workload resides in a dedicated Spoke VNet, peered with the corporate Hub (Azure Firewall). All PaaS services are accessed via Private Endpoints.
All communication between Web App, Azure OpenAI, and AI Search occurs via Private Endpoints. No data traverses the public internet.
All Azure OpenAI and AI Search traffic via Private Endpoints
No PaaS services exposed to the public internet
Satisfies "Private Networking with Azure OpenAI" requirement
Azure AI Prompt Flow provides a visual development environment to build, test, and deploy RAG workflows. It enables LLMOps best practices with automated evaluation and monitoring.
Version-controlled flows with CI/CD integration for continuous deployment and A/B testing.
Optimize user queries for better retrieval accuracy
Retrieve top-k relevant documents from Azure AI Search
Inject retrieved context into the LLM prompt
GPT-4o generates grounded response with citations
Azure AI Search provides hybrid search (keyword + vector) with semantic ranking to ensure the most relevant context is retrieved for the LLM.
Hybrid search retrieves relevant docs 95% of the time
Significantly reduced hallucinations by prioritizing context
Optimized chunk size (500 tokens) with 50-token overlap
Hallucination Control
Semantic Ranking ensures the LLM receives the most relevant context
Implemented automated testing pipelines to continuously measure response quality across three critical dimensions:
Factual accuracy - does the response cite actual document content?
92%
Grounded Score
Context quality - is the retrieved information relevant to the query?
89%
Relevance Score
Response quality - is the answer well-structured and readable?
94%
Coherence Score
Automated evaluation runs on every Prompt Flow deployment to ensure quality regressions are caught before production.
Enforced Microsoft Entra ID for both control plane (deployments) and data plane (chat access). All authentication is identity-based with zero secrets in code.
Managed Identities eliminate the need for credential rotation and secret management.
App Service MI granted read access to Azure OpenAI
App Service MI granted query access to AI Search
Full audit trail of who accessed what data and when
0 Secrets
in Code, Config, or Environment Variables
Configured Azure AI Content Safety filters to block jailbreak attempts and harmful content, ensuring the AI adheres to Responsible AI principles.
All safety filters configured to meet Microsoft's Responsible AI Standard v2 requirements.
100% detection rate for known jailbreak patterns
Severity thresholds set to "Medium" for all categories
Passed Responsible AI Standard v2 audit
Responsible AI
Compliant with Microsoft Standard v2
Mitigated high inference costs by implementing a "Smart Chunking" strategy during document ingestion. Optimized chunk size balances context quality with token efficiency.
Implemented semantic caching via Azure API Management to serve frequent queries from cache, reducing backend model calls.
Reduced avg tokens per query by 20%
40% cache hit rate → 40% fewer model calls
~30% reduction in total inference costs
30%
Reduction in Backend Model Calls
"By implementing a Zero-Trust RAG architecture with Azure AI Foundry, we transformed a CISO-blocked initiative into a production-ready, compliant GenAI platform that democratizes knowledge access while maintaining enterprise security standards."
Network Isolation
Cost Reduction
Groundedness Score
Responsible AI v2