27 Jul, 2026
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Shadow AI Doesn’t Knock First

Shadow AI Doesn’t Knock First

Tools Automate. Platforms Compound.

Tools Automate. Platforms Compound.

A Thousand Tables Deep

A Thousand Tables Deep

Signal Over Noise: How AI Finds the Drugs Hiding in Plain Sight

Signal Over Noise: How AI Finds the Drugs Hiding in Plain Sight

The Last Mile of the Lakehouse

The Last Mile of the Lakehouse

Switch To Production: Building An AI-Ready Data Foundation

Switch To Production: Building An AI-Ready Data Foundation

Reimagining the Enterprise in the Age of AI

As we enter a new year, I’ve been reflecting on a question nearly every CEO, CIO, and CTO is grappling with today: Over the past two years, enterprises have invested…
Sai Gundavelli

Data Masking Capability: Risk Reduction Without Analytical Collapse

Barry Kunst0

Executive Summary (TL;DR) Data masking is a risk transformation control, not a confidentiality boundary like encryption. The primary failure mode is analytical distortion caused by unrealistic masked values. Deterministic masking preserves joins and model behavior but increases correlation risk. Dynamic masking protects runtime access paths but introduces latency and policy complexity. Masking succeeds only when […]

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8 mins read

The Architecture of Trust: Why Healthcare AI Needs Governance at Its Core

Mark Lee0

Earlier this week, I had the privilege of speaking at TAL Healthfest 2026 in Hyderabad’s T-Hub—one of the world’s largest innovation campuses—under the banner of the Touch-A-Life Foundation. The audience was a cross-section of healthcare leaders, technologists, and policymakers, all grappling with the same question: how do we move at the speed of AI while […]

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9 mins read

Why Data Lakes Fail the Trust Test and How to Build an AI-Ready Data Layer

Barry Kunst0

TL;DR Data lakes fail on trust: not storage, not compute, not formats. AI raises the stakes: ambiguity becomes action risk for LLMs and agents. Fix the fundamentals: authority, lineage, semantics, and policy-aware access controls. Make answers reproducible: definitions plus lineage plus quality checks for each KPI. Connect to compliance: retention, access evidence, and defensible deletion. […]

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8 mins read

RHITL: Why the Right Human in the Loop Actually Matters

Stephen Tallant0

Blog Commentary Look, everyone’s talking about “human in the loop” these days. It’s become one of those phrases that gets thrown around in every AI discussion, right up there with “ethical AI” and “guardrails.” But here’s the thing: just putting *a* human in the loop doesn’t cut it anymore. You need the *right* human in […]

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5 mins read

AS/400 (IBM i) in 2026: Modernize Without Breaking Audit, Revenue, or History

Barry Kunst0

TL;DR AS/400 (IBM i) persists because it runs mission-critical, regulator-visible workloads reliably. The biggest risk is not age. It is institutional opacity, lost lineage, and compliance blind spots. Modernization succeeds when you control data first, then retire applications with audit-ready proof. IBM i data can be high value for analytics and AI, but only with […]

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6 mins read