Artificial intelligence (AI) has burst onto the scene in organizations with the promise of generating value, automating complex tasks, and transforming the way they operate.
However, many companies soon discover, after conducting various proof-of-concept tests, that it is not enough to simply develop AI systems: the quality, traceability, and context of the data are fundamental. In this new scenario, data governance is not a bureaucratic requirement, but an essential condition for scaling AI in a sustainable way.
AI Drives Governance, and Governance Is a Lever for AI Success
This context opens a window of opportunity: Using AI acts as a lever to strengthen technical data governance. In other words, starting off by building foundations that enable generative models, LLM-based solutions, or RAG (retrieval-augmented generation) architectures to function with quality, context, control, and ethics. Thus, management begins to understand that it is not possible to seriously develop Artificial Intelligence without solid governance to enable it.
AI As a Catalyst for Technical Data Governance
Every time a team proposes a use case for generative AI—for example, building an assistant with LLMs that answers internal/external questions using corporate documentation—it faces six key challenges, all related to data governance. Below, we analyze how each of these challenges is directly enhanced (or blocked) by the use of AI:
- Demand Control and Usage
In contexts with multiple business units wanting to exploit AI, demand control helps prioritize initiatives with greater impact and technical feasibility. Here, governance acts as an intelligent filter, helping to identify which use cases are viable based on data quality, source availability, the maturity level of the environment, and the intended use. Regarding the last point, it is important to understand the intended use in order to apply the necessary ethical and technical considerations, as well as regulatory compliance where applicable.
- Agile Experimentation Environments
Many of the elements discussed above intersect here. Being able to quickly test a model, integrate a new dataset, refine a prompt, or test an RAG architecture requires a controlled, agile environment with governed access to validated data. Data governance must enable these secure spaces where AI can experiment without compromising the quality, privacy, or integrity of corporate data. In addition, systems can incorporate automatic prompt evaluators or consistency metrics to accelerate trial and error cycles. Finally, having development artifacts or best practices in place enables a reduction in time to market for faster commercialization.
- Business Glossary, Data Catalog, and Lineage
Common understanding is essential in any AI project. When terms are ambiguous or vary between departments, results suffer. In generative solutions, the lack of a glossary can lead to misinterpretations, semantic confusion in prompts, or inconsistent results. Therefore, having a well-governed glossary allows teams to create more accurate prompts, train models with consistent terminology, and deliver responses aligned with business language.
- Quality
An AI model, no matter how advanced, is only as reliable as the data that feeds it. Data quality becomes especially critical in generative contexts, where subtle errors can be amplified in responses. It contributes to the famous “hallucinations,” or can lead to wrong decisions. Data governance must ensure proactive mechanisms for the automatic detection of anomalies, inconsistent values, information gaps, or obsolete data that may affect the model's performance in order to remedy them. Incorporating validation rules, real-time controls, and quality supervision models becomes an indispensable measure to avoid biases or repetitive errors in AI solutions.
- Regulatory Compliance
The deployment of artificial intelligence solutions in organizations cannot be separated from the regulatory framework that governs their use. In Spain and Europe, AI is subject to an increasingly demanding regulatory ecosystem that applies to all sectors, imposing principles of transparency, explainability, traceability, and human oversight.
The AI Act, recently approved by the European Union, classifies AI systems by risk levels and imposes strict requirements for high-impact systems, such as those used in clinical decisions, credit assessments, or critical infrastructure management. In turn, the AEPD has defined more than 140 recommendations to ensure the responsible use of AI in line with the GDPR, emphasizing data minimization, impact assessment, quality, and algorithmic oversight.
In the financial sector, regulations such as DORA and the EBA Guidelines on ICT and security risk management require entities to robustly manage the technological risks associated with automation, including model validation, operational resilience, and control of the algorithm lifecycle.
Meanwhile, in the healthcare and pharmaceutical sector, both European regulations on medical devices and emerging EMA guidelines are strengthening control over the use of AI in diagnostics, treatments, and clinical trials, prioritizing the safety and explainability of systems.
Elements such as lineage, traceability, technical documentation, and consent management are not just good practices: they are regulatory requirements. This regulatory environment reinforces the role of data governance as a guarantor of compliance.
How AI Empowers Data Governance
Just as AI needs governance to be effective, it can also revolutionize the way we govern data. AI applied to data governance itself is transforming traditionally manual tasks into automatic, scalable, and smarter processes. This has a direct impact on how we operate data governance and how governance responsibilities are perceived within the organization. Below are some specific examples of the use of AI for data sovereignty:
- Automatic and Enriched Cataloging
LLM models can analyze datasets, pipelines, or schemas and generate automatic descriptions, classify columns, suggest tags, and detect relationships between elements. In addition, they can interpret field names and associate them with terms in the existing glossary, facilitating continuous and contextualized cataloging.
- Generation of Terms in Glossaries and Federation Governance
Based on internal documentation, processes, knowledge bases, or semantic layers, AI can extract relevant terms, identify synonyms, group by domain, and propose definitions. These models can learn from the organization's own language, generate initial glossary proposals, and automatically keep it updated as the internal vocabulary evolves.
When the glossary is built in a federated manner, with contributions from different areas, AI facilitates its governance by detecting redundancies or synonyms, suggesting existing terms as alternatives to new proposals, recommending adjustments to definitions based on recent usage, and automatically classifying terms by domain or functional manager. Additionally, it allows for the management of the glossary's traceability and evolution, ensuring consistency without hindering the scalability of the federated model.
- Conversational Democratization
Using RAGs or LLMs trained on internal sources such as the glossary, catalogs, or documentary sources, it is possible to build conversational assistants for government and business teams: they allow you to ask, for example, “Where is monthly income data used?” or “Which fields contain sensitive information in the customer area?” and obtain explainable and traceable answers. This improves the accessibility and usability of information by democratizing its use if it is also published on the company's information portals.
- Intelligent Data Quality Monitoring
AI models—both proprietary developments and functionalities integrated into data governance tools—can learn the expected behavior of a dataset and automatically detect anomalies, inconsistencies, or sudden changes, generating understandable insights so that users can quickly identify what has happened, where, and with what potential impact. This capability is multiplied when combined with lineage information, allowing the origin of the problem to be traced and its downstream effects to be anticipated, whether in analytical models, business decisions, or responses generated by an AI system.
In addition, the generative capabilities of AI are beginning to transform the way quality rules are defined and applied. Thanks to models such as LLMs, users can describe the expected conditions of a piece of data in natural or functional language (for example, “the ‘registration date’ field must never be later than the ‘termination date’”), and the system can automatically translate that description into operational validation, monitoring, or alerting rules. This not only speeds up the deployment of quality controls, but also democratizes their definition, allowing business profiles to actively contribute without the need for in-depth technical knowledge.
- Intelligent FinOps With AI
In cloud environments, managing spending efficiently is key. AI becomes a natural ally of the FinOps approach by automating analysis, forecasting, and decision-making on costs and consumption.
Thanks to its ability to detect patterns, anticipate trends, and generate recommendations, AI makes it possible to identify spending deviations or inefficient use in real time, predict future spending based on historical data and seasonality, recommend technical optimizations with economic impact, infer cost centers even when tags are missing, and explain financial behavior in natural language for non-technical profiles.
This allows financial operations to scale from reactive control to intelligent and continuous financial governance, where IT, business, and finance share visibility, decisions, and responsibility.
Conclusion: Governing Isn’t About Slowing Things Down, It’s About Making Progress Possible
In the age of AI, data governance is not an option, it is a strategic requirement. The ambition to develop advanced use cases—especially with LLMs, RAGs, or generation systems—forces organizations to build solid governance foundations.
At the same time, these exact technologies allow us to govern more efficiently, automatically, and accessibly. Governance in the age of AI is not about putting up barriers, but about building the infrastructure and operating models that enable innovation with speed, security, and scale. Ultimately, it means impacting how people in the organization interact with it, and therefore requires change management that is adaptable to each profile, training, and patience.
Organizations that understand this two-way relationship between governance and AI will be the ones that lead the change, turning artificial intelligence into a real and sustainable asset that transforms people and the organization, not a isolated experiment.
Written by Ángel Fernández, SDG Group's Executive Manager.