Tag: Data Security Solution
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Data Security in Apache Iceberg: An Enterprise-Ready Framework for Granular Protection
Discover why Iceberg needs enterprise-grade, granular security—and what “pervasive protection” means in distributed data environments.
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Securing Data Integrity in the Age of AI
I’ve spent my career in security, and I’ve never seen a moment like this. Every organization wants to use AI and machine learning to get better insights, modernize operations, and improve services. But the challenge is clear: how do you migrate sensitive data to the cloud, share it broadly, and still keep it secure and…
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Why Data Security in AI Isn’t an Add-On — It’s Built Into Every Component of Your Pipeline
When most organizations think about securing AI, they still picture a staged process borrowed from traditional software: design the system, build it, test it, deploy it — and then, at the very end, tack on a “security review.” That sequence has always been flawed, but in AI it becomes outright dangerous. AI orchestration pipelines extend…
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Can Your Current Architecture Handle Secure, High-Speed Analytics on Databricks?
As enterprises increasingly use Databricks for analytics and AI, protecting sensitive data on the platform is paramount. Protegrity’s data protection integration with Databricks ensures that even sensitive data (like PII) can be analyzed in the cloud safely and in compliance with regulations. Two recent advancements make this integration especially powerful: By combining Unity Catalog’s unified…
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When Data Security Becomes a Strategic Advantage
Originally presented at Gartner Data and Analytics 2025 in London At Protegrity, we focus on securing the most important asset of all, the data itself. As a Solution Architect, I have the unique privilege of working with leading finance and retail organizations in EMEA. I get to see what kinds of data architectures they’re building, how they are automating…
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Empowering Data Security in GenAI: Step-by-Step Guide to PII Safeguarding in Bedrock using Protegrity
Introduction Generative AI (GenAI) applications, especially through Retrieval-Augmented Generation (RAG) pipelines, are transforming business interactions with data. These pipelines leverage language models and extensive enterprise knowledge bases for real-time queries of large internal datasets. Robust data privacy and security solutions are essential. Amazon Bedrock’s native security guardrails address this need. Protegrity, known for protecting sensitive…