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1. Introduction: Why is governance the invisible foundation of GenIA?
It's no longer a surprise: GenIA (generative artificial intelligence) is becoming a concrete productivity tool within companies.
According to McKinsey , 71% of organizations that adopted GenIA by 2024 already incorporate it into at least one relevant business process. However, adoption is growing rapidly, but the underlying structure doesn't always keep pace.
This is where the imbalance lies : poorly prepared data, without clear governance criteria, doesn't generate reliable decisions, but rather rework, noise, and exposure to risk. And this impact isn't limited to the technical team. It permeates the entire organization , influencing the accuracy of analyses, information security, and even brand reputation.
In this article, we look at what rarely receives the spotlight: the database . Not as a repository, but as a trusted infrastructure, where quality, traceability, and compliance must work together.
Because GenIA only delivers real value when it operates on a well-built foundation. And that starts with governance.
Enjoy the read!
2. Trusted Data for AI: What Goes Beyond Compliance and Quality
When we talk about governance for GenIA, quality and compliance are starting points, but not the end.
Keeping data organized, up-to-date, and LGPD-compliant is important, of course. However, many projects face a more subtle challenge : the difference between technically valid data and truly useful data for generative models.
GenAI doesn't just operate with well-defined tables and categories. It learns from language, interprets patterns, and generates responses. To do this, it needs data with context, consistency, and traceability . Data that's out of sync with the business, even if clean and secure, can lead to misinterpretations or ineffective applications.
Consider, for example, product data that contains only the value "100," with no unit of measurement, category, or history. It may be technically correct, but it's practically useless for a model that needs to understand demand, predict stockouts, or suggest pricing.
Having reliable data doesn't mean unnecessary complexity. It means alignment between the data structure and the AI's purpose . Knowing where the data came from, why it was collected, who can access it, and how it will be reused are decisions that need to be clear and documented. This often-overlooked care is what separates truly useful applications from limited experiments.
Therefore, the role of governance, at this point, is not to impose more rules , but to enable AI to have a reliable, understandable foundation that connects to the business reality.
And how does this structure take shape in practice? That's what we explore next.
3. Fundamentals for structuring governance with a focus on generative AI
When it comes to data for GenIA, it's common to think that it's enough to simply organize, classify, and protect it. But in practice, the governance that truly enables this technology needs to work at the pace of the business and AI .
We're dealing with models that not only query data, but also learn, transform, and generate content from it. And this changes the logic of governance : it's not just about who accesses the data, but also how it was produced, in what context it was processed, and for what purpose it will be used.
It is from this logic that the pillars for structuring governance oriented towards generative AI emerge:
- Purposeful traceability : recording the origin and trajectory of data in an accessible and useful way for those developing and operating AI models. This reduces uncertainty, improves explainability, and speeds up audits, without relying on manual processes or rework;
- Context as a primary criterion : data is only useful when related to its intended use, and governance must ensure this connection. Without context, the model can generate inaccurate, biased, or irrelevant content, undermining business trust;
- Lifecycle management : Data can become outdated over time. Therefore, continuous curation is part of the responsibility of keeping AI relevant. Updates, revisions, and deletions should be a natural part of the process, not an exception.
- Applied interoperability : More than just standardization, it's important to ensure that data flows consistently across different environments and systems. This reduces technical bottlenecks, speeds up integrations, and prevents AI from operating with fragmented versions of reality.
These fundamentals should not be seen as technical requirements, but as conditions for AI to generate real and sustainable value. Without them, the risk lies not in the AI itself, but in the foundation that supports it. And when we talk about sustainability, we cannot ignore the role of security. After all, effective governance also means protecting, monitoring, and controlling—of course, without hindering operations. Stay tuned!
4. Governance with Security: Control and Trust in AI Environments
There's no reliable foundation without security. And this becomes even more evident when we talk about GenIA, a technology that relies on large volumes of data circulating between different systems, teams, and contexts. In this scenario, protection isn't about locking down : it's about ensuring continuity, traceability, and trust.
But security here goes beyond the traditional. It's not just about protecting against unauthorized access, but also about monitoring the data's lifecycle with clear criteria for control, visibility, and accountability. Who accessed it? In what context? Has the data been altered? Is it being used in accordance with defined policies? These questions need quick and consistent answers, including for the data that feeds (and is generated by) AI.
Secure governance requires active mechanisms : granular access control, robust authentication, continuous monitoring, and audit trails that go beyond mere theory. All this without compromising operational fluidity, as GenIA demands agility as much as integrity .
This balance between freedom and control is what allows AI to generate value without putting the business at risk. And when security and governance work hand in hand from the outset, data goes from being a vulnerability to a competitive differentiator.
5. Conclusion: How to start structuring your base for GenIA
GenIA is not a plug-and-play . To generate real value, it needs to operate on trusted data, with clear provenance, preserved context, active security, and live governance. And this doesn't happen by accident: it's built.
Companies that treat data governance as a strategic pillar , not as " compliance ," reap more than just compliance. They reap confidence in results, scalability in initiatives, and speed with responsibility.
This is the journey we at Skyone are on. We help organizations transform their database into an innovation-ready platform, connecting cloud, security, and governance in a practical, scalable, and business-aligned way.
If your company wants to structure an environment better prepared to evolve safely, talk to one of our experts and find out how we can support this transformation!
And if you want to continue exploring this topic, also check out this article on our blog : Cloud Data for AI: How Cloud Computing Drives Artificial Intelligence .
FAQ: Frequently Asked Questions about Data Governance for Generative AI
Data governance has gained prominence with the advancement of GenAI (generative artificial intelligence), but the topic still raises questions, both conceptual and practical. Below, we answer the most frequently asked questions to help your company understand how to build a solid, secure, and useful foundation for responsibly scaling AI projects.
1) What changes in data governance when we enter the GenIA universe?
Data governance for GenIA needs to keep pace with how this technology learns and generates content. This means that, in addition to quality and compliance, it's also necessary to ensure context, traceability, and intended use. Governance goes beyond mere control and becomes a framework of trust, connecting data to the practical and strategic application of AI.
2) What is the difference between LGPD compliance and good data governance?
Compliance with the LGPD is a legal requirement, mandated by law, but not necessarily sufficient to guarantee useful data for AI. Good governance includes, in addition to compliance, practices that ensure consistency, traceability, and alignment of data with business objectives. This is what allows GenIA to operate with precision and reliability.
3) Where to start structuring data governance for generative AI?
The starting point is mapping how data flows through the organization: where it comes from, who accesses it, how it's processed, and for what purpose. From there, pillars such as purposeful traceability, continuous curation, interoperability, and active security come into play. Most importantly, the governance structure must be connected to the actual use of AI, not just a generic model.
