Artificial Intelligence has gained significant traction with the rise of generative AI applications like ChatGPT. While businesses are embracing generative AI for a competitive edge, they face challenges in establishing a robust AI governance framework to ensure responsible and ethical AI development, deployment, and usage. This article explores how Orion Governance’s Enterprise Information Intelligence Graph (EIIG), a self-defined data fabric, can expedite the implementation of an AI governance regime.
Charting Data Pathways to AI Models
The journey begins with metadata. The initial step in AI governance involves comprehending the data feeding into AI models. As per Gartner analysts, code is metadata. EIIG autonomously scans code across diverse technologies, spanning from mainframes to Python and Java, to establish both intra- and inter-system connectivity. This fosters a federated data view across organizational data silos. Essentially, EIIG furnishes a detailed map of data pathways to AI models, including precise data lineage information crucial for tracing data sources, ownership, and factors influencing data integrity for AI models.
Enhanced Knowledge Graph with Ontology
EIIG not only ingests metadata automatically to create a unified data view but also intelligently weaves it into a knowledge graph enriched with ontology. EIIG’s ML-driven term-to-asset mapping standardizes business glossaries and links business concepts with their technical counterparts. Employing a bottom-up approach, EIIG derives ontology from existing technical assets like database schemas or business intelligence models, facilitating the derivation of business concepts, definitions, and their relationships from relational database structures. Additionally, EIIG visually represents relationships between business terms, enriching the knowledge graph with ontology for semantically enabled data governance, crucial for AI applications.
Active Metadata Management
Apart from ontology-driven classification, EIIG offers a suite of analytical features to activate metadata, ensuring data integrity, quality, and trustworthiness for AI models. EIIG’s active metadata platform encompasses:
- Contextualized Lineage Tracking: Tracing data journeys allows users to understand how data has been transformed, processed, and integrated along its path, aiding in quality control, compliance, and accurate reporting.
- Data Usage Insights: By actively monitoring data usage patterns, EIIG can provide insights into which datasets are frequently accessed, who is accessing them, and for what purposes. This information is useful for optimizing resource allocation and assess/implement cloud migration/modernization initiatives.
- Relationship Mapping: EIIG captures the relationships between different data elements, datasets, and processes as well as business terms. This interconnected view of data is essential to effective impact analysis.
- Dynamic Cataloging: With active metadata, EIIG offers a data catalog that is dynamically updated based on changes in data. This ensures that users always can easily discover, access, and consume the most up-to-date data.
- Augmented Quality: EIIG can augment data quality by monitoring data quality and visualizing quality score throughout the data journey. As a result, data citizens know exactly how trustworthy the data is and fix any quality issues in a timely manner.
EIIG and Generative AI Synergy
The synergy between EIIG and Generative AI is symbiotic: EIIG establishes a trusted data foundation for Generative AI and leverages Generative AI to augment Data and AI Governance.
EIIG aids in the establishment of a trusted data foundation for Generative AI in a few ways including:
- Set up and institute AI governance policies and guidelines:
- Specify what data can be used for Large Language Models.
- Dynamically curate data.
- Visualize provenance (source, rights, usage, transformation, ownership, and audibility); one of the effective methods to show explainability.
- Tag sensitive or confidential data.
- Control access with permission-based approval process.
- Conduct near real-time impact analysis to show what effects of certain changes to upstream and downstream systems and processes.
- Knowledge Management
- Leverage semantically enabled knowledge graph to build a knowledge base for using LLMs.
- Parse frequently used code into metadata “triples” and subsequently analyze them for overlaps, compatibility, inferred integrity and additional aspects. This detailed analysis can help increase explainability.
- Enhance RAG and eliminate hallucinations by providing data hygiene with visibility into the data flow into the vector databases.
- Improve data quality and consistency using standardized data models and semantics.
In short, by integrating the capability of defining, consolidating, and delivering data optimally with exposure of relationships and context as usable assets, EIIG unifies active metadata-empowered knowledge graph with machine learning to create a foundational layer for knowledge-enabled generative AI.
In essence, by integrating data definition, consolidation, and delivery capabilities with contextualized relationships, EIIG amalgamates active metadata and machine learning to create a foundational layer for knowledge-enabled generative AI. Moreover, EIIG leverages Generative AI to enhance data and AI governance, exemplified by Orion Wingman, a generative AI assistant for EIIG facilitating personalized access to enterprise intelligence through natural language interfaces. This amalgamation underscores EIIG’s role in democratizing metadata management for governance, modernization, and compliance.
Ready to learn more about how EIIG can strengthen AI governance? Schedule a demo today.
About the Author: Niu Bai, Ph.D. is the Head of Global Business Development at Orion Governance, Inc. Connect with Niu on LinkedIn.
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