Organizing AI-Governance with a Data Catalog

Artificial Intelligence offers enormous opportunities for companies, but also brings risks. This also applies to the algorithms used in AI. Think of risks due to opaque decision-making, ethical issues and data quality problems. These risks can damage your image and undermine trust in your organization! A well-organized AI governance helps to manage these risks and maximize the value of AI. A crucial tool for this is a data catalog. In this article, we discuss how you can organize AI governance using a data catalog.

AI governance encompasses the policies, processes and controls needed to implement AI in a responsible and effective manner. The goal is to keep AI systems trustworthy, transparent and compliant with laws and regulations. Key aspects of AI governance include:
• Data quality and -management: How reliable and complete is the data that AI uses? Unreliable data can lead to incorrect results.
• Transparency and explainability: Can you explain how AI decisions are made? What models and versions do you use? Who owns them?
• Ethical guidelines: Are AI systems used fairly and without discrimination? AI systems used by organisations (within the EU) must be safe and respect EU fundamental rights and values.
• Compliance and regulations: Are you GDPR, EU AI Act and other relevant legislation compliant? The AI Regulation came into effect in August 2024 and brings with it a number of obligations.

The role of a Data Catalog
A data catalog is an essential tool for AI governance. It is a central place where you document metadata, data flows and data usage. This gives you control over the data that feeds AI and allows you to apply governance effectively. A well-designed data catalog offers:
• Visibility into AI data: A catalog helps identify the datasets that AI models use, including their provenance, intended use case, quality, and sensitivity.
• Data quality and reliability: By defining quality indicators and validation rules, you prevent incorrect or outdated data from influencing AI decisions.
• Access control and security: With a catalog you can manage who has access to which data and ensure security compliance.
• Transparency and auditability: All data flows become traceable, so you can account for how AI models make decisions and which models have been used (AI Monitoring).
• Collaboration and knowledge sharing: A catalog acts as a knowledge base for data scientists, analysts, and compliance teams, making AI projects more efficient and consistent. Automated workflows ensure authorized use.

Prerequisite before you start implementing
Not every data catalog is suitable for AI governance. AI Governance is ideally part of a broader data management initiative. A data catalog will need to be flexible enough to not only allow for the necessary input, but also for monitoring of results to be managed. IntoDQ2 offers a.o. data catalog solution Precisely Data360 of Precisely Inc. that can seamlessly do the job.

How to implement a Data Catalog for AI Governance?
To successfully organize AI governance with a data catalog, follow these steps:
• Define the business case
Record the Business Cases or Use Cases of AI applications and make them transparent for all stakeholders. This way you record AI applications from idea to final implementation and you can account for the use of AI models in a manageable way. The project team estimates risks, describes the business value and determines the periodic costs. The AI Use Case owner plays an important role in this.
• Identify AI-critical datasets
Determine which datasets are core to your AI applications. Document provenance, owners and quality aspects in the catalog.
• Define governance guidelines
Establish rules for data usage, quality and security. Use the catalog to capture and apply these guidelines.
• Automate data tracking and lineage
Ensure the catalog automatically tracks what data is used where and how it is processed by AI models.
• Facilitate collaboration and education
Make the catalog accessible to relevant teams and encourage active use. Train employees in data and AI governance principles. From February 2025, the requirement applies that organizations that use AI systems must have sufficient knowledge about AI in-house. The level of knowledge per employee must match the context in which the AI systems are used and how the systems can affect (groups of) people.
• Monitor and improve continuously
Use insights from the catalog to continuously improve AI governance. Perform periodic audits and adjust policies where necessary (AI Monitoring).

Conclusion
AI governance is essential for deploying AI effectively, responsibly and compliantly. A data catalog helps companies manage, secure and optimize the data that feeds AI. By organizing AI governance well with a data catalog, you can minimize risks and maximize the value of AI at the same time.

But be careful: choose a data catalog solution that is flexible and rich enough to fully support your AI users. IntoDQ2 offers a.o. data catalog solution Precisely Data360 for that purpose.

Erik Schaap is Data Management specialist.