All news with #data governance tag

🔒 Microsoft-owned LinkedIn will begin using profile details, public posts and feed activity from users in the UK, EU, Switzerland, Canada and Hong Kong to train generative AI models and to support personalised ads across Microsoft starting 3 November 2025. Private messages are excluded. Users can opt out via Settings & Privacy > Data Privacy and toggle Data for Generative AI Improvement to Off. Organisations should update social media policies and remind staff to review their advertising and data-sharing settings.

🔒 Incogni’s Social Media Privacy Ranking 2025 evaluates 15 major platforms across data collection, resale, AI training, privacy settings, and regulatory fines. The analysis identifies Pinterest and Quora as the most privacy-conscious, while TikTok and Facebook rank lowest, driven by extensive data use and historical penalties. The report highlights practical differences in opt-outs, data-sharing, and default settings and recommends users review privacy controls and use Kaspersky’s Privacy Checker.

🔒 DeepSeek collects extensive interaction data — chats, images and videos — plus account details, IP address and device/browser information, and retains it for an unspecified period under a vague “retain as long as needed” policy. The service operates under Chinese jurisdiction, so stored chats may be accessible to local authorities and have been observed on China Mobile servers. Users can disable model training in web and mobile Data settings, export or delete chats (export is web-only), or run the open-source model locally to avoid server-side retention, but local deployment and deletion have trade-offs and require device protections.

🔍 Dataplex Universal Catalog now captures column-level lineage for BigQuery, extending object-level tracing to granular column transformations at no extra cost. The update provides interactive visual lineage graphs so users can inspect upstream and downstream flows for individual columns, trace origins, and assess downstream impact of modifications. This granularity helps validate authoritative sources for AI/ML features, enforce column-level governance, and improve compliance. It also surfaces freshness and usage metadata to support context-aware agents.

🔁 Amazon Aurora PostgreSQL-Compatible Edition now offers zero-ETL integration with Amazon SageMaker, enabling near-real-time replication of PostgreSQL tables into a lakehouse. The synced data conforms to Apache Iceberg open standards and is immediately accessible to SQL, Apache Spark, BI, and ML tools via a simple no-code interface without impacting production workloads. Comprehensive, fine-grained access controls are enforced across analytics engines, and the capability is available in multiple AWS Regions.

🔒 BigQuery data clean room query templates are now available in preview, enabling clean room owners to publish fixed, reusable TVF-based queries that accept table or field inputs and return only aggregated rows. Templates reduce data exfiltration risk, simplify onboarding for non-SQL users, and enforce consistent analytical and privacy controls via aggregation thresholds and approval workflows. They support single-direction and multi-party collaboration while keeping query logic hidden from subscribers.

🌐 AWS Clean Rooms now supports cross-region collaboration, letting organizations analyze partner data stored in different AWS and Snowflake Regions without copying or sharing underlying datasets. Collaboration creators can specify allowed result regions to help meet data residency and sovereignty requirements. This reduces integration work—no new pipelines or replication—and enables faster, secure joint analyses across advertising, investment, and R&D use cases.

🔐 Amazon SageMaker Unified Studio now supports corporate identities for interactive Apache Spark sessions using AWS Identity Center trusted identity propagation. Data engineers and scientists can sign on to JupyterLab Spark sessions with organizational credentials while administrators apply fine-grained access controls and maintain end-to-end data access traceability. The integration leverages AWS Lake Formation, Amazon S3 Access Grants, and Amazon Redshift Data APIs, and includes comprehensive AWS CloudTrail logging for interactive and background sessions to streamline compliance.

🔍 AWS has added transformation status reporting to the AWS B2B Data Interchange console, enabling operators to view processing outcomes and validation results for EDI transformations in a single interface. The console presents timelines and statuses for up to 10,000 recent input–output pairs per partnership and highlights errors and validation details to speed troubleshooting. This capability covers ANSI X12 and other EDI flows to and from JSON and XML and is available in all Regions where the service runs; AWS provides a user guide and a self-paced workshop to help teams get started.

🧭 Google outlines an AI-native stack to transform data scientists into agentic architects, unifying development, real-time data access, and production-grade agent deployment. Enhancements to Colab Enterprise notebooks add native SQL cells, editable visualizations, and an interactive Data Science Agent that can orchestrate BigQuery ML, DataFrames, and Spark workflows. The Lightning Engine is now generally available to accelerate Spark, while previews for stateful BigQuery continuous queries and autonomous embedding generation bring real-time streaming and vector search into analytics. A 'Build-Deploy-Connect' toolkit, including the Agent Development Kit, MCP Toolbox, and Gemini CLI extensions, helps move notebook prototypes into secure, scalable agent fleets.

🛡️ Cloudflare introduced the Content Signals Policy, an extension to robots.txt that lets site operators express how crawlers may use content after it has been accessed. The policy defines three machine-readable signals — search, ai-input, and ai-train — each set to yes/no or left unset. Cloudflare will add a default signal set (search=yes, ai-train=no) to managed robots.txt for ~3.8M domains, serve commented guidance for free zones, and publish the spec under CC0. Cloudflare emphasizes signals are preferences, not technical enforcement, and recommends pairing them with WAF and Bot Management.

🔒 Amazon Connect Contact Lens now provides automatic sensitive data redaction for voice and chat conversational analytics in French (France, Canada), Portuguese (Portugal, Brazil), Italian, German, and Spanish (Spain). You can remove PII, financial account numbers and PINs, and Internet access details from transcripts and audio files, choosing to redact selected entities or all detected sensitive data. Redacted values can be replaced with a generic placeholder (e.g., [PII]) or an entity-specific placeholder (e.g., [NAME]). Sensitive data redaction is available in all AWS Regions where Amazon Connect is offered.

🔍 ODOT teamed with Google Cloud and North Highland to centralize decades of bridge inspection, location, and maintenance data into BigQuery and govern it with Dataplex, creating a single trusted source for analysis. Non-technical and technical staff can query complex datasets conversationally through Gemini in Looker, while BigQuery ML powers predictive models to flag at-risk bridges ahead of failures. Secure sharing via Analytics Hub and unified governance enables better resource allocation, improved safety, and faster, data-driven decisions across the agency.

🔒 AWS Clean Rooms ML collaborators can now configure a privacy control to send redacted error log summaries to selected collaboration members. Summaries include exception type, error message, and the line in the code where the error occurred. When associating a model with a collaboration, parties decide which members receive summaries and whether detectable PII, numbers, or custom strings will be redacted. This helps teams debug models while protecting sensitive data and intellectual property.

🔒The rapid adoption of generative AI is prompting significant privacy and security concerns as vendors revise terms to use user data for model training. High-profile pushback — exemplified by WeTransfer’s reversal — revealed how unclear terms and live experimentation can expose corporate and personal information. Employees using consumer tools like ChatGPT for work tasks risk leaking secrets, and platforms such as Slack are explicitly reserving rights to leverage customer data. CISOs must balance strategic AI adoption with heightened compliance, governance and operational risk.

🔒 Amazon SageMaker Catalog now supports governed classification using Restricted Classification Terms, enabling catalog administrators to mark sensitive glossary terms so only authorized users or projects can apply them to assets. Administrators grant usage through explicit policies and group membership, allowing centralized governance teams to control labels like Seller-MCF or PII. The capability is available in all regions that support SageMaker Unified Studio; consult the user guide to get started.

🔒 EuroDaT, a state-owned data trustee, built safeAML with major German banks to enable controlled, pseudonymous transaction matching while preserving GDPR compliance. The cloud-native service runs on Google Cloud and Google Kubernetes Engine, using infrastructure-as-code, isolated VPCs and auditable processing so EuroDaT never accesses personal-data content. By letting banks request targeted supplementary information, safeAML accelerates suspicious-activity checks, reduces false positives and lays groundwork for wider use in ESG and health data sharing.

🔐 EuroDaT describes how its safeAML platform, built on Google Cloud and Google Kubernetes Engine, enables controlled, pseudonymous exchange of sensitive transaction data between banks. Acting as a neutral data trustee, EuroDaT never accesses personal content while automating secure, auditable workflows that replace error-prone phone calls. Pilots with German banks show faster, more accurate suspicion assessments and lower false positives.

🛡️ Data brokers compile extensive personal dossiers and sell them without consent. This guide explains the challenges of locating and removing your information, outlines typical data collected, and describes practical steps to submit opt-out or deletion requests. It recommends tracking requests in a spreadsheet, citing laws like CCPA or GDPR, and repeating removals every 3–6 months or using paid services.

📄 Amazon Web Services' Bedrock Data Automation now supports five additional document languages — Portuguese, French, Italian, Spanish, and German — expanding multilingual document processing beyond English. Customers can build blueprints, prompts, and instructions in these languages using BDA Custom Output, while BDA Standard Output will produce summaries and figure captions in the detected document language. This update is generally available across multiple AWS commercial and GovCloud regions and aims to accelerate multilingual document workflows for intelligent document processing and multimodal automation.