# Learning Palantir Foundry 🚀 "Sales reps see only the rows for their assigned customers" — achieved on a single dataset, without spawning a copy per department. That is what Restricted Views (row-level security) deliver. 📌 Title and Feature URL Title: 制限付きビュー(行レベルセキュリティ) URL: 📝 Overview Restricted Views implement granular row-level access control. A restricted view is built on top of a backing dataset and lets different users see different subsets of the same underlying data based on defined permissions. This removes the need to copy datasets per team: you keep one dataset as the source of truth shared across the company while splitting visibility at the row level. 🔧 How It Works Restricted views operate through policies containing rules that determine row visibility. - Policies evaluate the viewing user's attributes, column names from the backing dataset, and specific values (strings, Booleans, numbers, arrays) to decide which rows are shown. - When referencing users, groups, or organizations, you must use the unique identifier (UUID) in both the policy column and the policy definition — names alone will not work. - In marking-backed views, the upstream dataset holds a STRING ARRAY column of Marking IDs, and each row is visible only to users with the required markings. - A restricted view is built on top of a backing dataset and cannot be used as an input for transforms. - Experimental branching support allows adding and merging restricted view policy changes. 🛠 Practical Usage - Add a column to the backing dataset that drives row access (for example, assigned branch or organization ID). - Define a policy that matches that column against user attributes to build the row-level filter. - Save restricted views in a separate Project from the source datasets to keep access management clean. - For markings, attach an array of required Marking IDs per row to control visibility. 🎯 Use Cases - Limit sales representatives to viewing customers at their assigned branch. - Separate records on a shared table by department or organization. - Disclose differently classified records only to users holding the required markings. ⚠️ Caveats - Restricted views cannot serve as transform inputs, so they cannot be plugged directly into downstream pipeline processing. - Users, groups, and organizations must be referenced by UUID; name-based references do not work. - Merging policy changes via branching is experimental and may not be universally available. #PalantirFoundry# #DataGovernance#

Circle Foundation is supporting Pacific Community Ventures with a grant for the Radiant Data Hub, an AI-enabled platform bringing data governance, predictive modeling, and impact tools to community lenders.

Databricks Chief Information Officer Naveen Zutshi has been named to TIME's 2026 Executives of the Year: Tech & Data list! @TIME highlights Naveen's mission to make every Databricks employee a builder with Genie, while strengthening the data governance behind how Genie accesses and acts on company data. Congratulations to Naveen and all 50 leaders shaping the future of technology, AI, cybersecurity, and digital innovation. Explore the full list:

🚀 Alibaba Cloud is now in Brazil. We’ve launched our first cloud region in Brazil to accelerate cloud and AI transformation. With the new data centers, local infrastructure delivers secure, resilient and scalable cloud services with low latency and data governance for Brazilian businesses. And there’s more: we’re bringing enterprise-grade agentic AI services to help businesses build, deploy and operate AI agents at scale. 🌎 106 Availability Zones | 31 Regions worldwide Brazil’s cloud + AI transformation starts here. #AlibabaCloud# #CloudComputing# #AgenticAI# #AI# #Brazil#

Run inference over millions of records — free of SQL, and without your data ever leaving Snowflake. Here's distributed batch inference at scale ⚙️ Title: Batch Inference at Scale URL: ⚙️ Overview A capability that runs distributed inference workloads on Snowpark Container Services (SPCS) with Ray as the execution framework. Inference runs as a dedicated distributed workload, supporting both traditional models and LLMs, consolidating complex operations into a single API call. ❓ Challenges Solved Many customers, especially those migrating from non-SQL systems, need batch inference decoupled from SQL. ・This is especially true for files and unstructured data at large scale ・Rearchitecting workflows around SQL-first patterns is a heavy burden 💡 Methodology & How It Works ・The input DataFrame is materialized and written to a stage as Parquet files ・A job is provisioned on SPCS; the primary node initializes as the Ray head and replicas join as workers ・Each worker reads staged data, performs inference independently, and writes results to an output stage ・Unified API: a single run_batch() call handles both structured and unstructured data ・Multimodal support (images, audio, video); workers load weights once and reuse across batches; JobSpec controls workers and GPU allocation 🌍 Use Cases ・Nightly summarization of millions of support tickets ・Product catalog enrichment via image-to-text generation ・Information extraction from scanned PDFs, audio transcription and labeling, video classification and description BatchInferenceTask integrates with Snowflake Tasks for DAG automation, and all processing stays inside Snowflake — running large-scale inference while preserving data governance. #Snowflake# #BatchInference#

Two enterprise AI failure modes, two evenings, one fall. Cost (Sept 29) and hallucinations (Oct 8) are usually treated as separate problems. They're not — bad data governance drives both. NYC, under 100 seats each. Sept 29th: Oct 8th:

Ready to move your AI initiatives from experimentation to real-world impact? #SnowflakeWorldTour# is coming to 23 cities worldwide with practical strategies for building, deploying, and governing AI directly on your data. Here's what's in store: 🔹 Hands-on sessions on Snowflake Cortex 🔹 Generative AI for the enterprise 🔹 Data governance in the age of AI 🔹 Customer stories showing what's possible 🔹 Connections with local data leaders and experts Discover new ways to turn your data into intelligent applications! Register today for a city near you! 📍

The AI slowdown might actually be exactly what we need. 👀 One of my biggest Dreamforce takeaways: the conversation is shifting from how fast we can build AI to how we actually get PEOPLE to adopt it. And Salesforce’s strategy is getting clearer: Slack = where the work happens
Surfaces = how we interact with AI
Claude = another way into your Salesforce ecosystem
Salesforce = the trusted data + governance layer underneath it all The technology moved fast. Now we need to bring our people with us. And THAT might be the most important part of the next phase of AI. #Dreamforce# #DF26# #Salesforce# #Slack# #AI# #Claude# #Slackforce# #TechTok# @Benioff @rseaman2 @rbgavin @slack @salesforce

📢 𝗝𝗨𝗦𝗧 𝗜𝗡: $NVO Novo Nordisk and Anthropic Partner to Accelerate AI-Powered Drug Discovery 👉 𝗞𝗲𝘆 𝗛𝗶𝗴𝗵𝗹𝗶𝗴𝗵𝘁𝘀: ➤ 𝗡𝗼𝘃𝗼 𝗡𝗼𝗿𝗱𝗶𝘀𝗸 and 𝗔𝗻𝘁𝗵𝗿𝗼𝗽𝗶𝗰 announce an AI drug discovery collaboration. ➤ Novo will deploy Anthropic's 𝗳𝗿𝗼𝗻𝘁𝗶𝗲𝗿 𝗔𝗜 𝗺𝗼𝗱𝗲𝗹𝘀 across R&D. ➤ 𝗖𝗹𝗮𝘂𝗱𝗲 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 will support biological reasoning and scientific workflows. ➤ Companies will jointly tackle 𝗱𝗿𝘂𝗴 𝗱𝗶𝘀𝗰𝗼𝘃𝗲𝗿𝘆 challenges identified by researchers. ➤ Novo will also use Anthropic models for 𝗔𝗜-𝗱𝗿𝗶𝘃𝗲𝗻 𝘀𝗼𝗳𝘁𝘄𝗮𝗿𝗲 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴. ➤ Initial deployment will test 𝗖𝗹𝗮𝘂𝗱𝗲 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 on selected R&D workflows. ➤ Collaboration aims to shorten the path from 𝗿𝗲𝘀𝗲𝗮𝗿𝗰𝗵 𝘁𝗼 𝗺𝗮𝗿𝗸𝗲𝘁𝗲𝗱 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝘀. ➤ Agreement incorporates 𝗱𝗮𝘁𝗮 𝗴𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 protocols and human oversight. 👉 𝗪𝗵𝘆 𝗧𝗵𝗶𝘀 𝗠𝗮𝘁𝘁𝗲𝗿𝘀: ➤ Could accelerate complex 𝗯𝗶𝗼𝗹𝗼𝗴𝗶𝗰𝗮𝗹 𝗿𝗲𝗮𝘀𝗼𝗻𝗶𝗻𝗴 and drug development workflows. ➤ Expands frontier AI adoption inside a major global 𝗽𝗵𝗮𝗿𝗺𝗮𝗰𝗲𝘂𝘁𝗶𝗰𝗮𝗹 R&D organization. ➤ Gives Anthropic another major enterprise application for 𝗖𝗹𝗮𝘂𝗱𝗲 in life sciences. ➤ Demonstrates growing integration of 𝗴𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗔𝗜 into pharmaceutical research. 👉 𝗘𝘅𝗽𝗲𝗿𝘁 𝗦𝘁𝗮𝘁𝗲𝗺𝗲𝗻𝘁𝘀: 𝗠𝗶𝗸𝗲 𝗗𝗼𝘂𝘀𝘁𝗱𝗮𝗿, President and CEO of Novo Nordisk: "AI can help us increase productivity in R&D and compress the path from research to marketed product. But beyond this, AI tools can also offer completely new scientific opportunities and aid reasoning and understanding of human biology and drug mechanics." 𝗗𝗮𝗿𝗶𝗼 𝗔𝗺𝗼𝗱𝗲𝗶, Co-Founder and CEO of Anthropic: "By giving leading researchers access to safe, capable, and trusted frontier models, we can shorten research timelines, improve outcomes, and discover medicines and treatments that significantly improve human life."

Becoming AI-first is not just a technology strategy. It’s an organizational design problem. Last week at @Glean:GO, leaders from some of the world’s largest companies—like @GM, @Ericsson, @Cisco, @Deloitte, @Mastercard, @Dell, and @GeneralMills—showed that even organizations with long histories can become AI-first. But the transition requires redesigning how work gets done, not simply adding AI to the existing stack. A few themes stood out: - 𝗠𝗼𝘃𝗲 𝗳𝗿𝗼𝗺 𝗿𝗲𝗮𝗰𝘁𝗶𝘃𝗲 𝘁𝗼𝗼𝗹𝘀 𝘁𝗼 𝗽𝗿𝗼𝗮𝗰𝘁𝗶𝘃𝗲 𝗰𝗼𝘄𝗼𝗿𝗸𝗲𝗿𝘀. On stage with @OpenAI’s @embirico and @CNBC’s @Kr00ney, we discussed why most companies barely tap AI’s potential: workers don't know what to ask. When AI has deep context—your role, OKRs, and daily tasks—it can proactively propose work by default, eliminating the friction of prompt engineering. - 𝗥𝗲𝗱𝗲𝘀𝗶𝗴𝗻 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀, 𝗻𝗼𝘁 𝗷𝘂𝘀𝘁 𝘁𝗮𝘀𝗸𝘀. The biggest constraint is often the coordination around a task: handoffs, context switching, and silos. If the workflow stays the same, much of AI’s productivity gain is lost. This is why we built Glean Transform, to map how work actually happens and redesign it around AI. - 𝗕𝗮𝗸𝗲 𝗶𝗻 𝗗𝗮𝘆 𝟬 𝘀𝗲𝗰𝘂𝗿𝗶𝘁𝘆. In my conversation with @Cisco’s @jpatel41 and @Deloitte’s Ashish Verma, a shared reality emerged. Enterprise AI cannot scale as a collection of ad-hoc initiatives. To give organizations the trust needed to hand over real, mission-critical work, Day 0 security and fine-grained data governance must be built directly into your core systems of record. - 𝗠𝗲𝗮𝘀𝘂𝗿𝗲 𝗼𝘂𝘁𝗰𝗼𝗺𝗲𝘀, 𝗻𝗼𝘁 𝗷𝘂𝘀𝘁 𝗮𝗰𝘁𝗶𝘃𝗶𝘁𝘆. @DaVita has saved 75,000 days of work using Glean. But as Madhu Narasimhan, CIO of DaVita, emphasized, the ultimate measure is human impact: giving a caregiver five more minutes with a patient. - 𝗖𝗿𝗲𝗮𝘁𝗲 𝗰𝗹𝗲𝗮𝗿 𝗼𝘄𝗻𝗲𝗿𝘀𝗵𝗶𝗽, 𝗼𝗳𝘁𝗲𝗻 𝘁𝗵𝗿𝗼𝘂𝗴𝗵 𝗮 𝗖𝗔𝗜𝗢 𝗿𝗼𝗹𝗲. In a panel with CAIOs from Capgemini, Mastercard, Ericsson, and Zapier, leaders emphasized that without clear ownership, AI remains a collection of disconnected initiatives instead of becoming a new operating model. Thank you to our customers, partners, speakers, and Glean team for showing what it looks like to build an AI-first organization in practice. We’re honored to be a partner in that journey.