Collibra

Governance framework for managing AI agents including registration, lifecycle tracking, compliance assessment, and risk monitoring.

A unified control plane for managing and governing every AI agent and system across the full AI lifecycle, delivering real-time visibility, trust signals and built-in compliance for safe AI scaling.

Policy-driven governance with built-in assessments aligned to EU AI Act, NIST AI RMF and internal standards, with risk rating assignment and stakeholder engagement.

Real-time visibility dashboards for leadership that track lifecycle status, monitor risk concentrations and connect Trust Scores to operational impact across every AI use case, model and agent.

End-to-end visibility capturing lineage from source datasets through model training, inference, deployment and usage across any platform, with automatic data relationship stitching.

Platform-agnostic integrations that govern AI use cases and models across AWS, Azure, Google, Databricks, SAP and MLflow from a single system of record with consistent oversight and traceability.

A universal scoring metric that assesses AI readiness, risk and compliance at a glance by aggregating documentation, data integrity, lifecycle status and regulatory signals into a single metric.

Centralized intake workflows for documenting and managing AI initiatives with structured processes to ensure clarity, ownership, and alignment across teams.

Integration that stitches datasets, models, agents and use cases in Azure AI Foundry, automating lineage, lifecycle tracking and dataset matching to reduce risk and save time.

A developer CLI tool that captures AI use cases directly from code, generating structured manifests and syncing models, versions and metadata into the unified registry without manual intake forms.

A platform that unifies AI, data and risk teams around a shared system of record for all AI initiatives, guiding use-case documentation, model and agent governance and intelligent data recommendations.

A governance and control platform that sits above data platforms, models, and agents to govern context and ensure control across every source, model, and agent in an enterprise. Built on ontology engineering to turn scattered data into governed, certified context.

A knowledge graph system that creates formal digital representations of an organization's business concepts, terms, and relationships. Powers intelligent discovery, lineage tracking, and AI-ready governance for agents and data consumers.

Unified governance platform for data and AI that acts as a context and control engine connecting data producers and consumers. Delivers trusted, AI-ready data across structured and unstructured sources.

Centrally manages masking and access controls across data sources to simplify compliance, protect sensitive data, and accelerate analytics and AI initiatives without slowing teams.

A governance solution to create a trusted data foundation by driving understanding of business terminology, establishing clear roles and responsibilities, and protecting sensitive data for regulatory compliance.

Traces the complete journey of data through transformations and dependencies to provide clarity, enable root cause analysis, and establish a defensible audit trail for regulatory proof.

Unifies ownership, business context, quality, and policies across data products to drive ROI from data investments.

Automates data quality monitoring and remediation with natural language-based quality checks, continuous 24/7 monitoring for outliers and schema changes, and root cause analysis.

Automates data quality monitoring and remediation to eliminate quality blind spots. Features natural language quality check definitions, 24/7 monitoring for outliers and schema changes, and root cause identification with business impact analysis.

An AI-driven recommendation engine that proactively delivers high-quality, governed data products to data scientists and engineers, eliminating manual data hunting and catalog browsing.

A data preparation capability acquired by Collibra that transforms unstructured data (emails, PDFs, SharePoint) into curated datasets for AI agents. It automates metadata enrichment, sensitivity screening, and quality assessment.

Transforms unstructured data (emails, PDFs, SharePoint documents) into curated datasets for AI agents. Automates metadata enrichment, sensitivity screening, and quality assessment to prepare data for AI consumption.

Captures lineage from source datasets through model training, inference, deployment and usage with automatic stitching of data relationships and audit capabilities.

Pre-built compliance assessment templates aligned to EU AI Act requirements for documenting controls, evidence, and approvals.

Dedicated registries for tracking and managing AI models and agents with lifecycle tracking, metadata management, and governance controls.

Lifecycle management and governance controls for AI models including documentation, versioning, approval workflows, and compliance tracking.

Pre-built compliance assessment templates aligned to NIST AI Risk Management Framework for systematic AI governance.

Framework for implementing AI governance policies with risk ratings, compliance workflows, and automated stakeholder engagement aligned to regulatory standards.

A centralized system of record for managing all AI use cases, models and agents across their full lifecycle, with tracking of dependencies on data, policies and use cases.

Transforms unstructured data into AI-ready assets through active delivery and preparation for AI consumption.

Pre-built compliance assessment templates aligned to the emerging AI UC-1 standard, available alongside EU AI Act and NIST AI RMF templates for documenting controls, evidence and approvals.

Enables end-to-end AI traceability across Vertex AI, SageMaker and Databricks with automated lineage linking data, models and use cases. Reduces risk and ensures compliance with transparent, governed AI workflows.

Capability for promoting alignment across teams and delivering consistent data products through enhanced visibility and automation of data product delivery.

Increases compute flexibility by enabling data quality jobs to run in a Spark engine on Edge, scaling and simplifying data quality operations.

AI-powered capability that automatically generates a semantic layer connecting physical data with business terms using semantic agents.