All news with #data governance tag

🔒 Google Cloud announced completion of a Dutch data protection impact assessment (DPIA) by SLM Rijk, confirming there are no known high data protection risks when recommended measures are applied. The outcome enables the Dutch central public sector to adopt Google Cloud from a privacy-assessment perspective and builds on earlier DPIA work for Google Workspace. Google emphasizes continued investment in privacy-enhancing technologies and support resources for customers.

🍕Papa John’s partnered with NBCUniversal, Instacart, and media agency Carat to target consumers when they’re likely low on groceries by analyzing Instacart purchase patterns. The campaign creates custom audiences based on purchases of staples like eggs, milk, and produce, then serves tailored creatives on NBCU streaming with prompts such as “Light on groceries?” and QR codes. Carat framed the approach as learning what’s in consumers’ fridges without being “too creepy.” The author notes historical parallels and ethical concerns about such predictive advertising.

🧩 AWS Clean Rooms now supports writing SQL query results to intermediate tables within a collaboration, enabling multi-step analytical workflows between partners. These intermediate tables allow reuse of complex joins and creation of shared ID mapping tables for downstream analyses, all within the collaboration’s privacy boundary. The feature helps reduce costs and improve performance for subsequent analyses such as reach, frequency, and attribution.

🔒 Google announced new privacy controls that separate saved history and personalization for Search services and Google Play, rolling out in users' Google Accounts in the coming days. The update creates distinct Search Services History and Personalized Recommendations settings, and similarly splits Play History and Personalization in Play. If Web & App Activity is on, the new Search Services History and its Save Media subsetting will be enabled after transition, but users can disable or delete saved media later.

🔍 Today AWS announced a preview of business context and semantic search for AWS Glue Data Catalog, enabling discovery of data by semantic meaning. You can enrich catalog tables with glossary terms, custom metadata fields, and add skills that provide agents with additional context. The new Glue Search API lets you find tables by both structure and attached business meaning, and MCP-compatible agents can use the aws-data-analytics plugin to integrate with minimal setup.

📌 This blog explains a structured approach to enterprise storage migration using Microsoft tools. It emphasizes planning, assessment, and choosing the right migration path based on data volume, connectivity, and downtime tolerance. Key solutions covered include Azure Migrate, Azure Storage Mover, Azure Data Box, and a preview Azure Copilot Migration Agent. The post illustrates phased strategies, real customer examples, and guidance for regulated and AI use cases.

🔒 Meta announced it will repurpose information businesses share about users' activity off its platforms to personalize Feed content and AI chatbot responses, expanding beyond targeted ads. The company said no new data collection is involved and that users can control this through an updated "Activity from other businesses" setting, replacing "Your activity off Meta technologies." The change will roll out next month in the U.S. and several other countries.

🧭 Amazon SageMaker Data Agent now integrates with SageMaker Catalog business context and metadata, letting data practitioners discover datasets and generate more accurate SQL and Python code using business terminology rather than cryptic table names. The agent leverages curated catalog content, including metadata synced from Collibra, Atlan, and Alation, to identify tables and columns, plan multi-step workflows, and respect governance by checking subscription status and providing access request links. This feature is available in SageMaker Unified Studio notebooks and the Query Editor in regions where Unified Studio is offered.

🛠️ Amazon SageMaker Unified Studio now adds business context, metadata, and data governance features for IAM-based domains. Customers can annotate AWS Glue Data Catalog tables with business names, descriptions, and README documentation, and use AI-generated metadata to automate cataloging. Teams can build business glossaries, define metadata form templates, and capture structured attributes like classification, retention, and ownership. These capabilities enable search, filtering by glossary or metadata fields, and access requests with automated Lake Formation permission grants, and are available in all regions where SageMaker Unified Studio is supported.

🛠️ Amazon SageMaker Unified Studio now integrates data quality rule authoring and evaluation powered by AWS Glue Data Quality. Data engineers, analysts, and data scientists can define rules, run evaluations, and view results for both data at rest and data in transit. The feature supports catalog table checks and Visual ETL job evaluations to detect issues before they impact analytics or ML workloads.

🔒 Amazon SageMaker Feature Store now supports the SageMaker Python SDK v3, providing modular APIs to manage feature groups with less boilerplate. Data scientists can enable Lake Formation access controls to enforce column- and row-level permissions on offline store data at feature group creation. The SDK also exposes Apache Iceberg table properties for configuring compaction and snapshot expiration to optimize storage and queries. Available in all AWS Regions where Feature Store is offered; install v3.8.0 or later to begin.

🔒 The Federal Trade Commission will ban data broker Kochava and its subsidiary Collective Data Solutions (CDS) from selling precise geolocation data without consumers' affirmative express consent as part of a settlement stemming from an August 2022 suit. The FTC alleged Kochava supplied paid clients — via an AWS Marketplace feed — with high-volume raw latitude/longitude transactions that enabled tracking to sensitive sites. Under the proposed court order, sales or transfers of precise location data are prohibited unless consumers directly request a service and explicitly consent; the companies must also implement a sensitive location program, supplier assessments, consent withdrawal and disclosure mechanisms, incident reporting to the FTC, and retention/deletion schedules.

🔎 Data security posture management (DSPM) is less about buying a single product and more about adopting a mindset: identify where sensitive data lives, quantify its value-at-risk, and use that information to prioritize remediation and investments. Full DSPM platforms can demand one to three dedicated FTEs to maintain, so many organizations should start with manual inventories, lightweight scanners or existing DLP outputs. The piece highlights practical scenarios—patch prioritization, M&A integrations, and IAM reviews—and warns that rising agentic AI and vendor access requirements make timely, measurable data discovery increasingly urgent in 2026.

🔧SDMA on AWS now supports custom transformation connectors and a unified desktop installer. Custom connectors enable submission of compute‑intensive jobs—such as format conversion, 3D rendering, image tiling, and metadata extraction—to AWS Deadline Cloud using Open Job Description templates, and can extend SDMA's built-in content analysis with bespoke verification or transformation logic. Connectors run in isolated compute environments and automatically ingest declared outputs back into SDMA's governed asset repository, allowing automated, chained processing across spatial data pipelines. The SDMA desktop application now offers a standalone installer that bundles required dependencies, removing the need to install the CLI or other components separately.

🤖 UKG built People Fabric to unify its legacy HCM and WFM systems into a single, real-time data and intelligence platform powered by AlloyDB for PostgreSQL and Google's Agentic Data Cloud. The platform establishes a canonical data model, ingests change streams via a custom CDC pipeline and Dataflow, and serves operational queries from AlloyDB while routing analytics workloads to BigQuery and tenancy metadata to Cloud SQL. The outcome is millisecond read-after-write behavior, native vector support for AI agents, and faster developer velocity across 126 application teams and thousands of database instances.

📜 The House Republican proposals — the SECURE Data Act and the GUARD Financial Data Act — would establish federal privacy standards that broadly preempt stronger state laws while limiting private lawsuits and centralizing enforcement with the FTC and state attorneys general. The bills emphasize data minimization, controller-processor obligations, a federal data broker registry, and new limits on automated profiling and teen data. Critics warn the measures could weaken existing protections, impose heavy operational burdens on CIOs and CISOs, and force vendors and legal teams to rework procurement, retention, and AI training practices.

🤖 Google Cloud introduces the Agentic Data Cloud, an AI-native architecture that converts enterprise data platforms into a dynamic System of Action for autonomous agents. It pairs a universal Knowledge Catalog, agentic-first practitioner tools, and a cross-cloud lakehouse to deliver trusted context, secure orchestration, and borderless data access. Early customers report substantial time and cost savings from agent-driven automation.

🚀 At Google Cloud Next '26, Looker was updated to enable Agentic BI through deeper integration with Gemini and BigQuery, introducing conversational agents that can trigger downstream business actions. New agents include upgraded Conversational Agents, Dashboard Agents, embedded conversational experiences, and Agentic Workflows. The release also modernizes the UI with AI-powered self-service tools like Visualization, Expression, and Insight Assistants. Emphasis is on governed semantic layers, open protocols, and developer tooling to reduce hallucinations and accelerate model-driven analytics.

🤖 Google Cloud introduced a next-generation cross-cloud Lakehouse engineered for the agentic AI era. It combines fully managed Apache Iceberg storage with read/write interoperability, a high-performance Managed Service for Apache Spark, and BigQuery integration to run multimodal workloads in real time. The service adds cross-cloud interconnect and caching to access AWS and Azure data with low latency, and unified governance via Knowledge Catalog to secure and profile data. Customers like Spotify and partners such as Accenture are already testing the platform.

🔁 The AWS Professional Services team provides a configuration-driven ETL accelerator that converts custom security logs into OCSF v1.1 and writes OCSF-compliant Parquet files partitioned for use with Amazon Security Lake or other data lakes. The serverless-first solution uses S3, Lambda, DynamoDB, Step Functions and either AWS Glue or EMR Serverless, and ingests mapping and metadata CSVs to drive transformations. An open-source GitHub repository includes deployment artifacts, example mappings, and instructions to validate outputs and run historical loads.