How Box supports EU AI Act compliance in Life Sciences

⚡𝗗𝗮𝘁𝗮 𝘄𝗶𝘁𝗵𝗼𝘂𝘁 𝗺𝗲𝗮𝗻𝗶𝗻𝗴 𝗶𝘀 𝗷𝘂𝘀𝘁 𝗻𝗼𝗶𝘀𝗲 — 𝗮𝗻𝗱 𝘄𝗶𝘁𝗵 𝗔𝗜 𝗮𝗱𝗼𝗽𝘁𝗶𝗼𝗻 𝗮𝗰𝗰𝗲𝗹𝗲𝗿𝗮𝘁𝗶𝗻𝗴, 𝘁𝗵𝗮𝘁 𝗻𝗼𝗶𝘀𝗲 𝗰𝗮𝗻 𝗾𝘂𝗶𝗰𝗸𝗹𝘆 𝘁𝘂𝗿𝗻 𝗶𝗻𝘁𝗼 𝗿𝗶𝘀𝗸. A Semantic Layer turns raw data into business-ready intelligence: providing shared context, enforcing consistency, embedding governance, and preparing data that AI can actually trust. How Alex helps: 🧡 Single semantic layer across the enterprise → No more silos. ⌛ Automated standardization & lineage → Saves time and reduces errors. ⚖️ Built-in governance & policy enforcement → Compliance-first by design. ✨ AI-ready metadata → Meaningful, trusted, and ready for safe AI adoption. ➡️ De-risk AI: https://lnkd.in/d3Aya2KC #AlexActivatesMetadata #SemanticLayer #DataGovernance

If you need to represent the complex and raw understanding of your data landscape into a unified and business-friendly view, then one of the best ways to do this, is with a #metadata #catalog that serves as a critical bridge between data sources, manipulators like ETL, staging stores like data warehouses and data-lakes and analytics tools like Tableau Qlik Streamlit SAS Viya for both human users and AI systems. Standardizing business metrics and enforcing governance rules centrally, it can creates a "single source of truth" that breaks down data silos, ensures data consistency, and accelerates trustworthy AI adoption by providing meaningful and accurate data for models to use. Alex Solutions offers the ability to have a universal semantic representation of your data landscape that performs these functions, automating standardization, embedding governance for compliance, and preparing AI-ready metadata to enable safe, enterprise-wide AI adoption. 

Forget being "AI-enabled." The next-generation organization is AI native. 🧠 This isn't just about using AI tools. It's about building your entire operational DNA around AI from the ground up. What does this look like? It may mean: 💻 Building new internal systems that use AI as part of a decision-making layer 🛠️ Rebuilding products or building new products from scratch with AI at the core instead of retro-fitting existing ones with AI ⚖️ Designing your governance approach to plan for AI, not just tolerate it "An AI-native company is architected from the ground up with artificial intelligence as a foundational layer, designed to operate through AI rather than layering it on top of legacy systems," writes Tiffine Wang of Onsen Global. "These companies treat data as core infrastructure, embed automation across operations, and build continuous feedback loops that enable systems to learn and adapt with every interaction." (Tiffine's full article is linked in the comments) For compliance leaders, the AI-native organization presents challenges we've never seen before. Is your governance strategy designed to police a few tools, or is it being architected for the systemic risk of an entire AI-native enterprise? #AIGovernance #AInative #Compliance #RiskManagement #DigitalTransformation #FutureOfWork #CLAIRcommunity

Enterprise AI agents are destined to fail because of outdated data foundations, not language model weaknesses. What does that mean? To work right, agents need on-demand access to living event streams (i.e., business events as they naturally occur, with relationships intact) that preserve and replay sequence and context, not static records that strip away how things actually happen. Companies that fix their data infrastructure gap will see faster AI deployment cycles, better insights from complete event histories, more accurate predictions based on full context and agents that can adapt in real-time. Plus they’ll be future-proofing their organizations for what comes next. https://lnkd.in/evJpz4Dx

#ai driven #datagovernance takes you out of previous non-scalable DG practices and helps you face today's challenges. Best practices are: 1. Adopt a Unified Governance Framework 2. Automate #Metadata and #Lineage Tracking 3. Govern #GenerativeAI Use Cases 4. Continuously #Monitor AI Outcome 5. Educate Stakeholders on #ResponsibleAI with expected outcomes of: - Innovate with confidence - Meet regulatory expectations - Reduce reputational and legal risks - Improve stakeholder trust and adoption - Differentiate their brand as a responsible AI leader

🚀 From Proof-of-Concept to Enterprise-Grade Generative AI One of today’s biggest leadership challenges is moving from a promising Generative AI demo to a secure, scalable, and reliable enterprise solution. The gap between experimentation and execution is wide — but not insurmountable. 📑 A new white paper from Booz Allen’s GenAI Team offers a much-needed blueprint. It lays out a six-layer enterprise technology stack that transforms ad-hoc pilots into intelligent applications ready for real business impact. 🔎 The Six Layers cover the full ecosystem — from Infrastructure and Large Language Models (LLMs), through Data Pipelines and Capabilities, all the way to the User Interface. This layered approach ensures performance, security, and scalability are built in from the ground up. ⚖️ But the paper goes beyond technology. It emphasizes the necessity of: 🔄 LLMOps → Continuous monitoring, evaluation, and improvement 🛡️ Governance, Risk & Compliance (GRC) → Navigating ethical and regulatory complexity 💡 The key insight: Successful GenAI deployment is as much about responsible management and human oversight as it is about algorithms. For organizations serious about unlocking Generative AI’s potential, this guide is essential. True value isn’t created by simply plugging in an LLM — it comes from architecting a trusted, resilient, and ethically sound ecosystem. The question for leaders is no longer if to use GenAI — but how to build it to last. #GenerativeAI #EnterpriseAI #AIStrategy #LLMOps #TechLeadership #DigitalTransformation #ResponsibleAI #BoozAllen

Your AI initiatives aren't failing because of the algorithms. They're failing because of data trust—or the lack of it.   Jessie Smith VP of Product at Ataccama, cuts straight to the core issue: large enterprises operate in sprawling data ecosystems where AI can't perform without a trusted foundation that spans every system, source, and user.    In this essential session, Jessica reveals how to operationalize data quality at scale and turn enterprise data complexity into AI-enabling clarity:  → Building confidence in data across fragmented sources → Embedding quality checks into daily workflows → Creating a single source of truth that AI can rely on → Strategies for measurable AI business outcomes    Essential for data leaders who know their AI success depends on getting the fundamentals right first: https://bit.ly/4kG6cJU    #DataStrategy #DataQuality #AIReadiness #TrustedData #EnterpriseData #DataGovernance #ArtificialIntelligence #Ataccama #CData #CDataFoundations #TechEvents

Interesting article in #OpenAI on why #LLMs hallucinate: https://lnkd.in/gFdHRQ_F Causes: LLMs are trained to predict plausible text, not to verify facts. Rare or missing data in training → “best guesses” instead of correct answers. Evaluation and incentives push toward confident guessing rather than admitting uncertainty. #Denodo helps by: Providing real-time access to authoritative enterprise data, so AI can pull accurate information instead of guessing. Making even rare or domain-specific data sources available, filling gaps in knowledge. Enforcing data governance and semantic consistency (shared definitions, data catalog), reducing contradictions or invented responses. Check out here: https://lnkd.in/g6nTrrPC

Agentic AI isn’t just another tech trend; it is redefining how data engineering scales and delivers impact. As data ecosystems become more fragmented and business needs evolve, rule-based automation falls short. What enterprises need now are intelligent, adaptive systems that go beyond static pipelines. Our latest blog explores how Agentic AI, powered by LLMs and multi-agent frameworks, is driving real transformation across the data lifecycle. From faster data ingestion to smarter governance, these AI agents collaborate with engineers, continuously learn, and respond to complexity in real time. What’s changing with Agentic AI: 🔹 Schema validation and lineage tracking that adapts 🔹 Context-aware data quality and governance 🔹 Conversational data discovery and metadata enrichment 🔹 Self-healing pipelines with built-in observability 🔹 Scalable, AI-powered Master Data Management Read the blog: https://lnkd.in/gemn_A-4 #EnterpriseAI #AgenticAI #GenAI #LLM #AIagents #DataEngineering #DigitalTransformation #Sigmoid

Every serious conversation about #AI eventually turns into a conversation about 𝗱𝗮𝘁𝗮. In 𝗿𝗲𝗴𝘂𝗹𝗮𝘁𝗲𝗱 industries, there’s no margin for shortcuts. A biased credit dataset or an unvalidated clinical record can cause more damage than a failed experiment ever could. That’s why data leaders are moving toward true product-level ownership models: ➛ Datasets managed with SLAs. ➛ Pipelines built with automated bias evaluation. ➛ Lineage and governance enforced at enterprise scale. Handled this way, data doesn’t just “feed” AI, it makes the outcomes reliable, compliant, and credible enough to shape board-level decisions. The real question for leaders is whether the organization has built accountability structures to treat data with the same rigor as any other mission-critical product. What’s holding your enterprise back from putting that rigor in place? #DataQuality #DataGovernance #DataProducts #EnterpriseAI