How AI governance is used
Drive AI visibility for stakeholders
Dynamic and customizable dashboards provide real-time model status and facilitate stakeholder collaboration for decision-making.
Enterprise AI monitoring
Monitor AI activities enterprise-wide
Trace and document the origin of models, the techniques used to train them, and any associated metadata needed for audits.
Manage risk and regulation
Simplify risk and regulation management
Use a unified and automated GRC platform that runs on any cloud and is designed to identify, manage, monitor and report on risk and regulatory compliance.
Benefits of AI governance
Operationalize AI governance
Trace and document the origin of datasets, models, associated metadata and pipelines at scale.
Manage risk with responsible AI
Monitor AI models for fairness, bias and drift that automatically identify the need for correction.
Protect against regulations at scale
Use protections and validation to ensure machine learning (ML) models in production are fair, transparent and explainable.
Satisfy ever-increasing stakeholders
Use automated and collaborative tools to shorten processes and increase AI lifecycle visibility.
Optimize AI strategy and planning
Increase efficiency and create balance across people, processes and AI technologies.
Scale your enterprise AI capabilities
Explore how to operationalize AI across an organization in the latest Data Differentiator chapter
Capabilities of AI governance
Catalog and monitor AI models with metadata capture, success identification and the ability to determine remediation initiatives.
Automate, identify, monitor and report on facts and workflows at scale, mitigating bias and drift to manage AI risk.
Translate external AI regulations into policies for automated enforcement. Use customizable dashboards to improve stakeholder collaboration.
AI governance case studies
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News and events
AI governance overview
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