Arun Prasad

The Australian Prudential Regulation Authority (APRA) is set to modernise its data stack and governance architecture, underpinned by a new platform built with Databricks on Azure. Ry Crozier has the details for iTnews. https://lnkd.in/g8VVa5vE

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🚀 Introducing My Enterprise-Grade AI Data Copilot Lab A GitHub Simulator for Governed AI Data Platforms 🔗 https://lnkd.in/gdK4hC9R In the rush to adopt generative AI, many organisations jump straight to answers from LLMs — without asking: ⁉️ Is it governed? Traceable? Auditable? Enterprise-ready⁉️ To answer that, I built a Governance-First AI Data Copilot Lab that simulates a real-world, regulated AI stack by design — not by accident. 🏛️ What This Lab Really Is It’s not just another demo. It’s a sandboxed, side-by-side architectural framework that mirrors what enterprises must build if they want safe, trusted, and compliant AI: 💡 Governance + Semantic Certification + AI Reasoning + Validation Controls That’s the stack where trust is engineered — not hoped for. 🧠 Key Architectural Principles 1️⃣ Deterministic Controls First Before any LLM sees a prompt: Business terms ("glossary.yaml") are validated Datasets are registered and certified Only approved semantic views are allowed Disallowed intent is refused with structured JSON Governance gates enforce rules — always. 2️⃣ Semantic Logic Boundary The model never reasons over raw data: Only certified semantic views (e.g., vw_account_summary) KPI and business logic are formalised This mirrors how enterprises protect financial and regulatory logic. 3️⃣ Controlled RAG Retrieval is: Constrained to certified context Indexed via vector store Injected into the LLM as curated knowledge The model doesn’t wander — it reasoning with permission. 4️⃣ Post-Inference Validation Every response: Is validated against strict schemas Includes source lineage Rejects hallucination or speculation That makes outputs regulator-ready. 🧩 Why This Matters Enterprises don’t need more models. They need AI that fits into governed platforms like: ✔ Snowflake + Collibra + RAG ✔ Semantic layers with KPI enforcement ✔ Deterministic governance with probabilistic explainability ✔ Traceable, auditable, and compliant outputs This lab demonstrates how. 📊 Architecture Snapshot This isn’t a one-off script. It’s a layered architecture with: ➡ Data Foundation (raw sources) ➡ Governance & Metadata Layer ➡ Semantic Certified Views ➡ AI Runtime (controlled RAG + LLM) ➡ Governance Gates (pre & post inference) ➡ Auditable Output Layer All implemented in code — and all mapped to enterprise equivalents. 🧪 Try It Yourself 🔗 https://lnkd.in/gdK4hC9R Clone it. Run it. Explore the boundaries between: Deterministic Controls ↔ Probabilistic Reasoning If your organisation is serious about regulated AI — this is the stack they should be thinking about. 🧠 Final Thought Enterprise AI is not a prompt engineering problem. It’s an architecture problem — one of governance, traceability, and engineered trust. #EnterpriseAI #DataGovernance #RAG #SemanticLayer #Snowflake #AIArchitecture #GovernedAI #Copilot #AIPlatform

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