The Evolution to Unified Data and AI Governance

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HOLISTIC DATA AND MODEL CATALOG

A comprehensive catalog of all data and AI model metadata providing visibility into relationships, lineage and meaning to enhance traceability.

Continuous Data Quality Validation

Multilayered data quality checks using statistical analysis, rules-based profiling etc., to ensure training data and model input consistency.

Algorithmic Audits

Proactive bias assessment by testing model outcomes across diverse datasets and user groups.

Privacy Protection

Deploying data minimization, anonymization, federated learning and encryption to mitigate privacy risks.

Model Risk Management

Formal evaluation of risks across the AI model lifecycle pre-deployment to ensure controls adherence.

Human Oversight

Maintaining meaningful human oversight of data and models across the lifecycle.

Actionable AI Insights

Providing visibility into key metrics on model accuracy, data quality, bias rate and AI vs. human decision ratios.

Regulatory Compliance

Embedding compliance to data protection and AI regulations within data sourcing, model development and operations.

Cross-functional Teams

Developing blended teams encompassing data engineers, scientists, and governance experts.

Enabling Tools

Deploying integrated tools spanning metadata, data quality, bias detection and model risk management.

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