TL;DR An AI security assessment identifies where your organization’s AI systems expose data, produce unreliable outputs, or create attack vectors that traditional security frameworks do not address. The assessment should cover model access controls, training data provenance, prompt injection risk, output validation, and alignment with the EU AI Act and NIST AI RMF. Most enterprises discover their biggest gaps are
TL;DR: Engineering outsourcing companies take responsibility for a delivery outcome, while staff augmentation only rents capacity. EPAM, Globant, and the global giants lead at scale in 2026, and specialists such as Kanerika lead for data and AI engineering outsourcing where governed agents and platform modernization decide the result. Comparing engagement models from a different angle? See also Staff Augmentation vs
TL;DR Databricks materialized views cache pre-computed query results and refresh them incrementally, so BI dashboards and ETL pipelines read from stored data rather than recomputing from scratch on every query. Every materialized view is backed by a serverless Lakeflow pipeline and governed through Unity Catalog. In 2026, refresh policies and EXPLAIN CREATE MATERIALIZED VIEW reached GA, Delta Sharing for MVs
FoodPharma’s Reporting Transformation with Microsoft Fabric and Kanerika
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Building an AI-Powered Compliance Platform That Scales Across Jurisdictions
Data Accuracy in Premium Reconciliation with Automation for Fortegra | Kanerika's case study
Enabling Faster Insights with Informatica to Microsoft Fabric Migration
How Microsoft Fabric Improved Sales & Financial KPIs for a Leading Pharma Manufacturer
How Microsoft Fabric Helps Reduce Cost & Boost Data Performance: A Case Study
Enhancing Decision-Making and Reducing Costs with Power BI.
