Building an AI governance framework: 3 real-world examples
AI is no longer a futuristic concept—it’s embedded in how modern organizations operate, make decisions, and deliver value.
Without the right checks in place, AI can introduce real risks, including bias, lack of transparency, and regulatory non-compliance.
Keep reading to learn more about AI governance - the strategic layer that ensures AI isn’t just powerful, but responsible.
This article explores the concept of AI governance, provides real-world examples from DataGalaxy customers, and underscores the importance of implementing a strong AI governance framework for modern organizations.
What is AI governance?
AI governance establishes the ground rules for AI to guarantee that it minimizes dangers and maximizes benefits for everybody. With proper care, AI governance can help guide how systems are built, monitored, and trusted across the organization.
Technical steps like creating explainable AI models and organizational procedures like assigning who monitors AI systems are both part of it. Governments and businesses already understand the possibilities and perils of AI, and AI governance is crucial to ensuring that AI solutions are not only efficient but also morally and socially responsible.
These structures, regulations, and procedures help guarantee that AI systems are created, implemented, and utilized ethically. It involves formulating rules prioritizing ethical decisions in AI, accountability, transparency, and fairness.
A typical AI governance system overview includes the following elements:
Ethical overview
Ensures AI systems align with societal values and do not perpetuate biases
Risk management
Identification and mitigation of potential risks associated with AI systems
Transparency
Helps make AI decision-making processes understandable and explainable
Compliance
Adherence to relevant laws and regulations governing AI use
Accountability
Establishes clear ownership and responsibility for AI outcomes
AI & the regulatory landscape
This integrated approach to privacy and governance is even more critical as AI morphs under increasing regulatory scrutiny. Governments and regulatory bodies worldwide are enacting new rules around transparency, accountability, and algorithmic fairness to keep pace with AI-driven decision-making.
AI-relevant regulations include:
- GDPR (EU): Expands individual data rights, requiring AI explainability and automated decision oversight
- CCPA (US): Grants consumers control over their data, impacting AI data collection practices
- EU AI Act (EU): Introduces a risk-based AI framework, mandating transparency and governance for high-risk AI applications
These regulations help make AI decision-making explainable, accountable, and privacy-compliant. They also redefine how AI operates within governance frameworks. Yet, compliance is only possible with complete visibility into how data moves and evolves across AI systems.
AI governance vs. data governance: A quick overview
AI and data governance are interconnected, but they address different aspects of big data management.
In general, data governance is the foundation upon which AI governance is built. It helps ensure high-quality, well-managed data, which is essential for effective AI systems. However, AI governance also ensures these systems operate ethically and within legal boundaries.
Data governance focuses on:
- Data quality: Ensuring data is accurate, complete, and reliable
- Data management: Overseeing data storage, access, and lifecycle tracking
- Compliance: Adhering to data protection regulations
- Security: Protecting data from unauthorized access and breaches
AI governance, on the other hand, addresses:
- Accountability: Ensuring AI models make explainable and justified decisions
- Ethics: Preventing AI from reinforcing societal biases or causing harm
- Monitoring: Continuously assessing AI performance and behavior
- Compliance: Adhering to AI-specific laws and guidelines
The importance of having a strong AI governance framework
Establishing and maintaining an AI governance framework helps modern organizations ensure responsible, ethical, and compliant AI use. Over time, this can help their customers gain trust, mitigate risks, and build trust while maximizing business value.
Regulatory compliance
Regulations like the EU AI Act require organizations to ensure their AI systems comply with strict international regulations.
Stakeholder trust
Transparent and accountable AI practices build trust among customers, employees, and partners.
Ethical responsibility
It’s no secret that AI systems can inadvertently perpetuate biases or make unethical decisions. A governance framework establishes ethical guidelines, ensuring AI aligns with societal values and organizational principles.
Efficiency
Transparent and accountable AI practices build trust among customers, employees, and partners.
Risk mitigation
AI systems can pose many risks for organizations, including security vulnerabilities and unintended consequences.
5 steps for getting started with AI governance
The following steps can help data-driven organizations begin the foundations for an AI governance framework:
1. Consider and define your ethical principles
Establish clear ethical guidelines that align with organizational values and societal expectations
2. Assess regulatory requirements
Identify applicable laws and regulations governing AI use and ensure compliance
3. Beef up your data governance
Ensure data quality, security, and accessibility, providing a solid foundation for AI systems
4. Develop oversight
Create structures for monitoring AI performance, addressing issues, and ensuring accountability
5. Foster a culture of responsibility
Educate employees about AI governance principles and encourage ethical decision-making
3 real-world examples from DataGalaxy customers
Over the last 10 years, DataGalaxy has had the privilege of partnering with many organizations to implement effective AI governance frameworks.
Our customers demonstrate how DataGalaxy facilitates the integration of data and AI governance, enabling organizations to harness AI's potential responsibly. Here are some of our favorite real-world examples:
1. Swiss Life: Enhancing data visibility & ownership
Swiss Life, a prominent insurance company, uses DataGalaxy to establish unified data ownership and visibility.
By implementing DataGalaxy, Swiss Life improved data quality and accessibility, laying the groundwork for responsible AI deployment. This initiative ensured that AI models were trained on reliable data, reducing the risk of biased or inaccurate outcomes.
2. CANAL+: Unifying teams with shared intelligence
CANAL+, a major media company, leveraged DataGalaxy to foster collaboration across departments.
By creating a shared data knowledge platform, CANAL+’s teams could access consistent information to facilitate the development of AI-driven solutions.
This approach promoted transparency and accountability in AI projects, aligning with governance best practices.
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3. Getlink: Enabling self-service data insights
Getlink adopted DataGalaxy to empower employees with self-service data capabilities.
By democratizing data access, Getlink ensured that AI initiatives were grounded in accurate and up-to-date information. This strategy supported ethical AI use by involving diverse stakeholders in data analysis.
Conclusion
As AI becomes more involved in everyday business decisions, AI governance isn’t just a checkbox: It’s how organizations stay in control.
A strong data and AI governance framework gives teams the tools and clarity to build AI systems that are fair, transparent, and aligned with real-world impact.
It’s not about slowing innovation down - it’s about scaling it with intention, accountability, and trust built into every step.
FAQ
- What is data governance?
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Data governance ensures data is accurate, secure, and responsibly used by defining rules, roles, and processes. It includes setting policies, assigning ownership, and establishing standards for managing data throughout its lifecycle.
- Why is data governance important?
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Data governance brings clarity and consistency, ensuring everyone uses and understands data the same way. It’s not just about control—it fosters collaboration, trust, and smarter decisions, turning data into a strategic asset that fuels innovation and growth.
- How do I implement data governance?
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To implement data governance, start by defining clear goals and scope. Assign roles like data owners and stewards, and create policies for access, privacy, and quality. Use tools like data catalogs and metadata platforms to automate enforcement, track lineage, and ensure visibility and control across your data assets.
- What is data quality management?
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Data quality management ensures data is accurate, complete, consistent, and reliable across its lifecycle. It includes profiling, cleansing, validation, and monitoring to prevent errors and maintain trust. This enables smarter decisions and reduces risk.
- How do you improve data quality?
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Improving data quality starts with clear standards for accuracy, completeness, consistency, and timeliness. It involves profiling, fixing anomalies, and setting up controls to prevent future issues. Ongoing collaboration across teams ensures reliable data at scale.
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