The Evolution to Unified Data and AI Governance
HOLISTIC DATA AND
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.
Proactive bias assessment by testing model outcomes across diverse datasets and user groups.
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.
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.
Embedding compliance to data protection and AI regulations within data sourcing, model development and operations.
Developing blended teams encompassing data engineers, scientists, and governance experts.
Deploying integrated tools spanning metadata, data quality, bias detection and model risk management.
Want to explore the true scope of Data Governance