Prompt Injection in Enterprise AI: Protect the Path from Retrieval to Action
Build a defensible boundary between retrieved content and agent authority, with practical tool controls, approval rules and adversarial release tests.
04 / INSIGHTS
Ideas for building
with intelligence.
Original analysis and practical guidance for leaders and builders working across modern data platforms, enterprise analytics and applied AI.
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Six connected disciplines.
One intelligent ecosystem.
01
AI Agents & LLMs
Enterprise agents, retrieval, evaluation, guardrails and useful automation.
Explore topic ↗02Data Engineering
Reliable pipelines, SQL, Python, cloud platforms, data quality and operations.
Explore topic ↗03Architecture & Cloud
Lakehouse, warehouse, Microsoft Fabric, Azure and scalable platform decisions.
Explore topic ↗04Analytics & Power BI
Semantic models, trusted KPIs, Power BI, reconciliation and decision-ready reporting.
Explore topic ↗05Governance & Reliability
Observability, lineage, ownership, testing, security and production controls.
Explore topic ↗06Industry Use Cases
Practical data and AI applications across complex, regulated industries.
Explore topic ↗LATEST PERSPECTIVES
Research, frameworks
and practical direction.
Slow Power BI Report? A Layer-by-Layer Troubleshooting Framework
Diagnose slow Power BI reports systematically by isolating the visual, DAX, semantic model, storage mode and source layer before changing anything.
AI Gateway Architecture: A Practical Enterprise Design Framework
An AI gateway can centralize model access, policy, routing and telemetry—but only when its responsibilities and failure boundaries are designed deliberately.
Enterprise AI in Banking and Energy: Where Agents Create Real Value
The most valuable early agents support evidence-heavy work with bounded authority—while high-impact decisions retain strong controls and human accountability.
From Chatbot to Dependable AI Agent: What Changes in Production?
An agent does more than generate language: it retrieves context, selects tools, changes systems and creates a new class of operational risk.
The Hidden Business Cost of Untrusted KPIs
When teams debate the number instead of the decision, the real defect is often in definitions, model design, ownership and refresh evidence.
Lakehouse, Data Warehouse or Both? A Practical Decision Framework
The right choice depends on workloads, data shapes, engineering skills, transactions and operating responsibility—not architecture fashion.
Why a Successful Data Pipeline Run Does Not Prove Data Completeness
A green status confirms that activities completed—not that the expected data arrived, remained complete or represented the correct business moment.
Why AI Readiness Starts With Data Reliability
The fastest route to useful enterprise AI is often better ownership, quality, semantics and operational evidence—not a larger model.
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