This website uses cookies so that we can provide you with the best user experience possible. Cookie information is stored in your browser and performs functions such as recognising you when you return to our website and helping our team to understand which sections of the website you find most interesting and useful.
Case Study
A framework to integrate AI Governance into your Data Catalog for Easier Compliance
AI Governance in Action: Leveraging Data Catalogs to Drive Compliance and Strengthen AI Governance
Context
Many organizations struggle to implement AI Act requirements effectively, seeking practical solutions that can be integrated into their operations. Our interactions with customers across various sectors show a growing need to leverage familiar tools to meet these new regulatory demands. The good news is that you can utilize existing tools like Data Catalogs to drive compliance and strengthen AI governance. You can create a transparent and aligned framework by connecting AI use cases with risks, controls, business context, objectives, and training data. These tools can act as catalysts, enhancing your AI organization’s compliance journey while ensuring data quality and governance are seamlessly integrated.

The picture shows a typical AI lifecycle proces with AI governance steps included as part of the original I lifecycle process. Getting these AI governance process steps executed and integrated with Data Governance & Data quality management is where a tool like a data catalog can help.
Challenge
Organizations consistently face several pressing challenges when implementing AI governance:
- Lack of clear understanding of AI Act requirements and their implications when developing AI use-cases
- Need to document Data Governance and Data Quality initiatives
- Difficulty in identifying and mitigating risks related to new and existing AI use-cases
- Challenges in developing a comprehensive AI governance framework with clear risks and controls
Roles
This initiative usually involves the following people & roles. They can be found either within your organisation or at a consulting partner:
- Business Owner/Sponsor
- AI Steward/Scientist
- Chief Data Officer (CDO)
Impact
In the picture you can see both the origination of an AI model, as well as it’s risks and (negative) impacts.
We are proud to announce that as of today, Data Trust Associates is one of the first companies on the market that is able to offer a fully integrated AI governance and Data Governance solution – making sure that your AI risk and your training data risks are fully integrated and visualized in a 360 view.
Data Governance and Data Quality are fundamental to successful AI governance strategies. An integrated framework provides the foundation for AI compliance:
- Documentation: Maintain current AI Act policies, use cases, and models
- Governance Framework: Establish clear responsibilities linked to Business Glossary
- Quality Assurance: Continuous monitoring of AI training data quality
- Innovation: Integration with existing Data Catalog tools
- Monitoring: Track AI use cases through compliance dashboards
- Expertise Development: Build in-house AI governance capabilities