AI and agentsCollibra blog: insights on data governance and AIJun 30 3mo3 months agoThe AI Trust Layer: Command-Center Architecture for Enterprise-Grade Models and AgentsAn AI trust layer is the architectural layer that sits between your AI systems and your business, enforcing context and control at runtime so every model and agent operates on data you trust, within limits you set.CollibraRead
AI and agentsCollibra blog: insights on data governance and AIJun 30 3mo3 months agoAI lifecycle governance: Governing models and agents from ideation to decommissioningAI lifecycle governance is the practice of keeping an AI system accountable at every stage of its life: from the moment someone proposes a use case, through development and deployment, to the day it's retired.CollibraRead
AI and agentsCollibra blog: insights on data governance and AIJun 30 3mo3 months agoAI audit readiness: A checklist for models, agents, and your first AI auditAI audit readiness is the state of having the evidence an auditor will ask for already in place before the audit begins: a complete inventory of your AI, records of how each system was assessed and approved, lineage and audit trails for...CollibraRead
AI and agentsCollibra blog: insights on data governance and AIJun 30 3mo3 months agoAI lineage tracking: From source data to model and agent decisionAI lineage tracking is the practice of recording the complete path an AI system takes to reach a decision: from the source data, through the features and model, to the output, and for agents, through to the action taken.CollibraRead
Reliability and performanceCollibra blog: insights on data governance and AIJun 29 3mo3 months agoAI model and agent monitoring: Metrics, drift detection, and runtime alerting in productionAI model monitoring is the continuous tracking of an AI system's performance, inputs and behavior in production, so teams catch degradation, drift and failure before they reach users.CollibraRead
AI and agentsCollibra blog: insights on data governance and AIJun 29 3mo3 months agoAI Metadata Management: The Context Layer That Makes Models and Agents TrustworthyAI metadata management is the practice of capturing, organizing and governing the metadata that describes an organization's data, so AI systems can understand what that data means and use it correctly.CollibraRead
Product and companyCollibra blog: insights on data governance and AIJun 29 3mo3 months agoAI assurance platforms: Definition, capabilities, and how they manage AI riskAn AI assurance platform is a system that gives continuous confidence that an organization's AI is operating within acceptable risk: detecting risk and drift early, validating behavior in production, and keeping evidence that every model...CollibraRead
AI and agentsCollibra blog: insights on data governance and AIJun 29 3mo3 months agoAgentic AI Governance: A Control-Plane Framework for Governing Autonomous AI Agents at RuntimeAgentic AI governance is the practice of controlling autonomous AI agents while they act, not just before they launch. It enforces policy, context and guardrails at runtime, so an agent that books a refund, queries customer data or calls...CollibraRead
AI and agentsCollibra blog: insights on data governance and AIJun 29 3mo3 months agoThe semantic layer for AI: Why LLMs and agents need business context to be trustworthyA semantic layer for AI is a governed layer of business meaning, the definitions, metrics, relationships and rules that sit between raw data and the AI using it, so a model or agent reasons from what your terms actually mean rather than...CollibraRead
Reliability and performanceCollibra blog: insights on data governance and AIJun 26 4mo4 months agoControl Tower: OOTB controls to govern AI by exceptionAI is only as trustworthy as the data and policies behind it.And yet, governance checks are still largely manual, inconsistent and impossible to scale as AI portfolios grow.CollibraRead
AI and agentsCollibra blog: insights on data governance and AIJun 26 4mo4 months agoOperationalize the AIUC-1 standard with the new out-of-the-box assessment templateAs enterprises move from AI experimentation to production agents, security and trust have become the gating factor for adoption.CollibraRead
AI and agentsCollibra blog: insights on data governance and AIJun 26 4mo4 months agoAI Model and Agent Inventory: How to Catalog Every AI System in Your EnterpriseAn AI model and agent inventory is a single, current record of every AI system an organization runs: models, use cases and autonomous agents, each with an owner, a risk classification and a record of what data it can reach.CollibraRead
AI and agentsCollibra blog: insights on data governance and AIJun 26 4mo4 months agoData lineage for AI: Tracing training data, RAG sources, and agent inputsData lineage for AI is the practice of tracing every data input that feeds an AI system, from training data and fine-tuning sets to the RAG sources, prompts and live data that ground a model or agent at inference.CollibraRead
Data engineeringCollibra blog: insights on data governance and AIJun 26 4mo4 months agoGovern your Microsoft Fabric estate with CollibraEnterprises are adopting Microsoft Fabric at remarkable speed, more than 28,000 organizations now build analytics on the platform.CollibraRead
AI and agentsCollibra blog: insights on data governance and AIJun 26 4mo4 months agoTurn AI activity into measurable business oversightAI adoption is outpacing the controls meant to govern it. Every new AI use case, agent, and model increases the complexity of the AI landscape, making it harder for leaders to understand what is running, who owns it, and whether it can be...CollibraRead
SecurityCollibra blog: insights on data governance and AIJun 26 4mo4 months agoAI regulatory compliance in 2026: EU AI Act, US orders, and state laws, and how to operationalize themAI regulatory compliance is the work of meeting the legal obligations that apply to how an organization builds, deploys and operates AI.CollibraRead
InfrastructureCollibra blog: insights on data governance and AIJun 26 4mo4 months agoAttribute-Based Access Controls: Right data, right usersToday, most business operations live in the cloud. But traditional methods of granting fine-grained access, one role, one ticket, one table at a time, breaks down at cloud scale.CollibraRead
Data engineeringCollibra blog: insights on data governance and AIJun 26 4mo4 months agoTake control of your Collibra homepage experienceAccording to Gartner, organizations that invest in role-based UX see up to 30% faster time-to-value for new platform users, yet most enterprise data platforms still deliver the same generic entry point to every employee, regardless of...CollibraRead
AI and agentsCollibra blog: insights on data governance and AIJun 26 4mo4 months agoGovern every AI agent across its full lifecycleAI no longer just generates answers, it now acts. Agents read data, make decisions, call tools and trigger processes across systems, often without a human in the loop.CollibraRead
Reliability and performanceCollibra blog: insights on data governance and AIJun 25 4mo4 months agoAI Observability Explained: How to Monitor Models and Agents in Production From One Command CenterAI observability is the continuous practice of measuring what AI systems do in production: the data they read, the outputs they produce and the actions they take. It lets teams catch drift, errors and risk before they reach a customer.CollibraRead
AI and agentsCollibra blog: insights on data governance and AIJun 25 4mo4 months agoThe AI risk management framework: NIST AI RMF for models and agents (with implementation steps)An AI risk management framework is a structured approach to identifying, measuring and controlling the risks an AI system creates across its life: bias, drift, security, privacy, and for autonomous agents, the actions they take.CollibraRead
AI and agentsCollibra blog: insights on data governance and AIJun 24 4mo4 months agoAI model catalog vs. AI model inventory: What's the difference (and where agents fit)An AI model catalog helps teams discover and reuse AI assets; an AI model inventory governs them. The catalog answers "What AI do we have that I can find and use?," and the inventory answers "What AI do we run, who owns it and how risky is...CollibraRead
AI and agentsCollibra blog: insights on data governance and AIJun 24 4mo4 months agoAI audit trails: What to log for models and agents, and how a Command Center captures itAn AI audit trail is a complete, tamper-evident record of what an AI system did and why: the data it used, the decision or output it produced, the action it took and the people and policies involved.CollibraRead
AI and agentsCollibra blog: insights on data governance and AIJun 24 4mo4 months agoGovernance by design: Automatically enforce standards with the AI-powered writing assistant, smart checks and lifecycle managementAs organizations accelerate AI adoption, deploying agents and automating decision workflows, the underlying assets those systems depend on will determine whether they succeed or fail.CollibraRead
AI and agentsCollibra blog: insights on data governance and AIJun 24 4mo4 months agoAI transparency: What regulators, auditors, and users need to see from models and agentsAI transparency is the ability to show what an AI system did, on what data, under what policy and on whose authority, with enough evidence that a regulator, an auditor or an affected person can verify it.CollibraRead
AI and agentsCollibra blog: insights on data governance and AIJun 24 4mo4 months agoWhat makes an AI platform 'trusted'? 7 non-negotiables for governing models and agentsA trusted AI platform is one you can hold accountable: it knows every model and agent you run, grounds them in, controls what they can touch and do, and proves all of it with evidence.CollibraRead
Product and companyCollibra blog: insights on data governance and AIJun 24 4mo4 months agoBad data doesn't slow AI down. It scales the wrong answerYour AI strategy is only as honest as the data underneath it. And right now, that data is quietly failing. predicts that through 2026, organizations will abandon that aren't supported by AI-ready data.CollibraRead
AI and agentsCollibra blog: insights on data governance and AIJun 18 4mo4 months agoAI needs context: Why LLMs still don’t understand real-world systemsAs AI moves from isolated use cases into real operational environments, a new limitation becomes visible. It is no longer about whether systems can generate answers, but whether they understand where those answers are being applied.CollibraRead
Product and companyCollibra blog: insights on data governance and AIJun 18 4mo4 months agoAWS and Collibra deepen partnership to bring business context and semantics to AWS SageMaker CatalogThe typical data estate is sprawling and heterogeneous, spread across platforms, clouds and on-premises systems, each with its own tools, owners and ways of describing things.CollibraRead
InfrastructureCollibra blog: insights on data governance and AIJun 18 4mo4 months agoData has gravity. Context does tooData has gravity. Context does too. Make sure context pulls your enterprise together, not apart. Every cloud platform is racing to build a context layer for AI. Databricks shipped Unity Catalog Business Semantics. Snowflake has Horizon.CollibraRead
Reliability and performanceCollibra blog: insights on data governance and AIJun 17 4mo4 months agoData observability platform: How to proactively monitor and trust your data at scaleData quality tools tell you when a rule is broken. They don’t tell you why it broke, when the problem started, which downstream systems are affected or whether anyone will be alerted before the issue reaches a report, a model or a...CollibraRead
Product and companyCollibra blog: insights on data governance and AIJun 17 4mo4 months agoMetadata management: The missing layer that makes AI actually workMetadata management: The missing layer that makes AI actually work AI doesn’t need more data. It needs better context. That’s especially true when teams are working with documents, emails, transcripts, support tickets, contracts, PDFs...CollibraRead
SecurityCollibra blog: insights on data governance and AIJun 17 4mo4 months agoCompliance monitoring tools: What to look for when choosing a platformMost compliance teams don’t need another tool. They need a tool that actually works the way regulators expect them to. That’s a higher bar than the vendor demos suggest.CollibraRead
Developer toolsCollibra blog: insights on data governance and AIJun 17 4mo4 months agoAI governance framework: A practical guide to governing AI at enterprise scaleAI pilots don’t fail because organizations lack imagination. They fail because imagination gets ahead of operating reality. A team launches a use case. Another team tests a model. A business unit experiments with an agent.CollibraRead
Reliability and performanceCollibra blog: insights on data governance and AIJun 17 4mo4 months agoData observability platform: How to proactively monitor and trust your data at scaleData quality tools tell you when a rule is broken. They don’t tell you why it broke, when the problem started, which downstream systems are affected or whether anyone will be alerted before the issue reaches a report, a model or a...CollibraRead
Machine learningCollibra blog: insights on data governance and AIJun 17 4mo4 months agoAI model governance: How to track, audit, and control AI models in productionHere’s a question most AI teams cannot answer: how many models are currently running in production? Not in the ML platform. Not in the model registry. In production.CollibraRead
Developer toolsCollibra blog: insights on data governance and AIJun 17 4mo4 months agoMetadata framework: Why your AI strategy needs a strong data foundationThe models are not the problem. The models are, by any reasonable measure, extraordinary. What is failing AI initiatives inside the enterprise is the data being retrieved to feed them: untagged, unclassified, contextually stripped and...CollibraRead
Data engineeringCollibra blog: insights on data governance and AIJun 17 4mo4 months agoWhat is a semantic layer? How shared business meaning powers better AI and analyticsThere is a specific kind of meeting that happens in almost every data-driven organization. Someone in finance runs a revenue analysis. Someone in sales runs a different revenue analysis. They reach different numbers.CollibraRead
SecurityCollibra blog: insights on data governance and AIJun 17 4mo4 months agoContinuous compliance monitoring: Moving from reactive audits to proactive controlMost compliance programs are built around a lie: that a point-in-time snapshot of your data controls tells you anything meaningful about your actual risk posture. It doesn’t.CollibraRead
Developer toolsCollibra blog: insights on data governance and AIJun 17 4mo4 months agoWhat is data democratization? How to give every team access to trusted, governed dataAsk most CDOs whether they want to democratize data and they will say yes without hesitation. Ask them whether their governance framework enables or obstructs that goal, and the answer gets complicated quickly.CollibraRead
Data engineeringCollibra blog: insights on data governance and AIJun 16 4mo4 months agoWhat are data products and why do they matter for AI, analytics and ROI?Most organizations don’t have a data shortage; they have a data usability problem. Data is scattered across cloud platforms, warehouses, lakehouses, business applications, spreadsheets and dashboards. Some of it is trusted.CollibraRead
Developer toolsCollibra blog: insights on data governance and AIJun 16 4mo4 months agoEnterprise risk management framework: Building a scalable foundation for regulatory complianceMost organizations have an enterprise risk management framework in name. They have the committee structures, the risk registers, the quarterly reporting cycles. What they rarely have is the data infrastructure underneath it.CollibraRead
Data engineeringCollibra blog: insights on data governance and AIJun 16 4mo4 months agoData quality management: A framework for reliable, trusted and AI-ready dataMost data quality problems are discovered too late. They aren’t identified during pipeline design or model development. No, they’re discovered in the wrong number on a board dashboard, the failed AI model that returns absurd outputs or the...CollibraRead
AI and agentsCollibra blog: insights on data governance and AIJun 16 4mo4 months agoHow to choose the right AI governance solutionICYMI: We are officially in the AI era. Since the explosive rise of mainstream generative AI, corporate leaders have been scrambling to turn the profound potential of machine learning into tangible business value.CollibraRead
Developer toolsCollibra blog: insights on data governance and AIJun 16 4mo4 months agoAI governance and ethics: How to build responsible AI from the ground upEthics is the part of AI everyone agrees is important and almost nobody operationalizes. Ask any executive whether their organization is committed to responsible AI and the answer is yes.CollibraRead
Reliability and performanceCollibra blog: insights on data governance and AIJun 16 4mo4 months agoEnterprise AI governance: How to scale safe and compliant AI across the organizationMost organizations have proved they can run AI pilots. The question they are struggling to answer is whether they can run AI at scale, safely, consistently and with the oversight their regulators and boards now expect.CollibraRead
Developer toolsCollibra blog: insights on data governance and AIJun 16 4mo4 months agoWhat is data lineage? How end-to-end traceability builds confidence in your dataLet’s start with a mildly provocative but entirely undeniable reality: most organizations have absolutely no idea where their data actually comes from, how it transforms or where it ultimately goes.CollibraRead
Product and companyCollibra blog: insights on data governance and AIJun 16 4mo4 months agoCollibra named Databricks' 2026 Data Governance Partner of the YearYour AI is only as trustworthy as the data and context behind it. Today, we're honored to share a milestone that speaks directly to that truth: Collibra has been named Databricks' in the 2026 ISV Partner Awards, recognized at the.CollibraRead
Developer toolsCollibra blog: insights on data governance and AIJun 16 4mo4 months agoData product management: How to build a scalable, business-aligned data product strategyMost data teams are still working on data projects. The leaders pulling ahead are working on data products, and they need someone to manage them. That someone is a data product manager.CollibraRead
Data engineeringCollibra blog: insights on data governance and AIJun 16 4mo4 months agoData as a product: How leading organizations are rethinking their data strategyMost organizations have more data than ever and less trust in it than ever. Data teams spend years building pipelines, warehouses and lakes, and still find themselves in the same weekly fire drill: someone questions the numbers, nobody...CollibraRead
AI and agentsCollibra blog: insights on data governance and AIJun 16 4mo4 months agoAccelerating enterprise AI with high-trust outcomesThe enterprise race to productionize generative AI has entered a new phase: the shift from passive LLM experimentation to autonomous, agentic systems.CollibraRead
SecurityCollibra blog: insights on data governance and AIJun 15 4mo4 months agoSolvency II compliance: What insurers need to know about data integrity and reportingLet’s not sugarcoat the reality of modern insurance regulation. Solvency II is the regulation that puts your data, reporting and governance under a microscope. Insurers who treat it as a mere compliance hurdle will always be scrambling.CollibraRead
Product and companyCollibra blog: insights on data governance and AIJun 4 4mo4 months agoClinical data is only as credible as its contextEveryone has had this experience: you're scrolling through your phone's photo gallery and you find a picture, but you can't remember when it was taken, where you were or which device captured it.CollibraRead
SecurityCollibra blog: insights on data governance and AIJun 2 4mo4 months agoTrusted data, smarter AI: Unified governance for SnowflakeThe race to deploy enterprise-grade AI has never been faster, but the stakes for compliance, privacy and trust have never been higher.CollibraRead
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