Collibra AI Governance

AI starts with trusted data


Define, measure and monitor AI models across the enterprise to maximize results while minimizing risks. Built with the same principles of Collibra Data Governance, Collibra AI Governance serves as the backbone for your AI strategy.


AI Governance beta now open

Implement streamlined and ethical AI practices to mitigate risk and maximize value. Register your interest in the Collibra AI Governance beta program by signing up below.

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Gain confidence in AI models

Deliver trusted AI with automated workflows, processes and policies that govern AI and the data that drives it.


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Collaborate across your organization

Engage technical, legal and business stakeholders with easy to use interfaces and integrate with any data and AI infrastructures for governance tailored to your needs.

AI Governance collaboration screenshot

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Mitigate risk, protect data and ensure compliance

Easily identify areas of risk with well documented use case definitions, data requirements and AI outputs for simple go/no go decision making and workflow tasks for continuous monitoring, validation and updates.


Tailor AI Governance to fit your needs

Collibra provides a flexible platform that can be customized to the needs of any organization, ensuring exceptional usability and value.

Legal, ethics and compliance checks

Ensure ethical, unbiased usage and adhere to compliance updates with full lineage tracking of all changes to the model and the data that feeds it.

Complete insights

Create a well documented register of all AI models to stay ahead of regulations and increase trust with customers and partners.




Collibra powers your AI governance journey

As it is for all data governance initiatives, it’s critical to know where to start with AI governance. Our team of AI experts and data scientists have developed a framework as an easy-to-implement guide to getting your AI governance program in place. Below are the four steps for managing and governing AI across its cycle.

Define use case

Capture ideas from the business, assess feasibility and define the AI use case, including the data and model(s) leveraged and the intended purpose. Set desired business outcomes and KPIs, assess risks and assign ownership and accountability.

Identify and understand data

Collect and assess the data that’s available, whether or not it’s high-quality/certified, and whether its use in connection with the use case is legally permissible.

Document models and results

Document, trace and track the model, associated data products and usage, allowing for model analysis and reporting.

Verify and monitor

Verify your model and continuously monitor to ensure the quality and compliance of the underlying data products. Retrain, test and audit models regularly.

Data intelligence fuels data and AI governance

Underlying AI governance is the documentation, assessment and monitoring of the data products that feed into the models. At each step of the framework, data intelligence capabilities come into play. Because AI is driven by data, AI governance fits into your larger, enterprise data governance plan.

Govern AI across its entire lifecycle

Trusted data drives trusted AI

Extend data policies and governance guardrails to AI models, ensuring the data that feeds AI meets the same usage standards to deliver trusted AI.

Build a full service data depot

Provide a centralized location for data across your organization for AI use case understanding and faster model development.

Produce a common data lexicon

Drive data literacy for all users to help AI use cases get off the ground faster.

Deliver high-quality data

Continuously monitor and ensure the health of pipelines to deliver high-quality data to AI models.

Keep your organization out of the headlines

Ensure AI is legal, ethical and compliant.

Safeguard AI compliance

Determine if AI is compliant with AI and data laws and regulations.

Mitigate risk

Establish risk levels of use cases and how much you're willing to take on.

Gain full visibility across your AI landscape

Completely understand all AI use cases and the data being used to make informed decisions.

We needed to provide data dictionaries to our regulators in which we had some gaps in field level descriptions. It was as simple as the push of a button to generate and a few minutes to review and approve. We got the request done in a matter of hours vs. weeks all thanks to generative AI descriptions from Collibra.

Evan Loenser

Associate Director of Enterprise Data and Reporting at UCare Minnesota

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