Skip to main content

Posts

Showing posts from January, 2026

Microsoft to acquire Osmos to bolster Fabric platform

Microsoft has announced it is acquiring the agentic AI data engineering platform, Osmos . It plans to incorporate the new technology into its data platform Microsoft Fabric. According to Microsoft, Osmos’ AI agents turn raw data into analytics and AI-ready assets that can be used by OneLake, which is Fabric’s unified data lake. Microsoft says that this acquisition is part of the company’s overall mission of enabling its customers to unify their data and analytics into a single platform. “By bringing Osmos’s technology and team into Microsoft, we have the opportunity to accelerate what we’ve been building and deliver it to a far broader audience—directly where customers already operate their data platforms,” said Kirat Pandya, CEO of Osmos. Bogdan Crivat,  corporate vice president for Azure Data Analytics, added: “Today’s announcement reinforces Microsoft’s focus to help every organization unlock more value from their data faster and with greater simplicity. The Osmos team will ...

From SBOM to AI BOM: Rethinking supply chain security for AI native software

Most supply chain practitioners already understand the value of a Software Bill of Materials. SBOMs give you visibility into the libraries, frameworks, and dependencies that shape modern software, allowing you to respond quickly when vulnerabilities emerge. But as AI native systems become foundational to products and operations, the traditional SBOM model no longer captures the full scope of supply chain risk. Models, datasets, embeddings, orchestration layers, and third-party AI services now influence application behavior as much as source code. Treating these elements as out of scope creates blind spots that organizations can no longer afford. This shift is why the concept of an AI Bill of Materials is starting to matter. An AI BOM extends the logic of an SBOM to reflect how AI systems are actually built and operated. Instead of cataloging only software components, it records models and their versions, training and fine-tuning datasets, data sources and licenses, evaluation artifact...