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.
Gain confidence in AI models
Deliver trusted AI with automated workflows, processes and policies that govern AI and the data that drives it.
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.
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 privacy 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.
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
Are you ready for AI?
72%
Leaders who said problems with data are most likely to jeopardize the achievement of their AI goals between now and 2025. 1
78%
Leaders who said scaling AI and ML use cases to create business value is their top priority over the next three years. 1
70%
Internally developed apps that will incorporate AI or ML. 2
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