Technological evolution creates the illusion that speed is more important than structure. And artificial intelligence is no exception. While public and private organizations accelerate the adoption of systems capable of automating decisions, predicting behaviors, and redefining workflows, there is also a growing perception that innovation without governance does not represent progress, but rather exposure. The race for AI is not just technological; it must be institutional. And the central challenge lies not only in implementing intelligent systems, but in ensuring that they operate within clear parameters of accountability.
A concrete indication of this change appears in a survey reported by Digital ConvergenceAccording to data, 95% of companies in Brazil have begun treating AI as a privacy alert and have strengthened their data protection programs because of this technology. This movement reveals a significant transformation: Artificial intelligence depends on data, and data implies governance. As automated decisions begin to influence critical processes, data protection ceases to be a peripheral issue and assumes a strategic position in the institutional architecture.
For a long time, data protection programs were perceived as structures primarily focused on regulatory compliance. Recent developments demonstrate that this view has become insufficient. Artificial intelligence demands informational quality, clear legal bases, traceability, and robust oversight mechanisms. In this scenario, privacy ceases to be understood as a brake on innovation and begins to function as a necessary infrastructure for it to occur sustainably. It's no longer a matter of innovating first and governing later, but of structuring governance to enable innovation itself..
This change reflects a broader convergence already observed in international studies. As summarized... Cisco research when analyzing nearly a decade of the topic's evolution:
"Across nearly ten years of Cisco research, one message is clear: privacy, data governance, and AI accountability are converging into a single framework for digital trust. As AI becomes embedded across every operational layer, privacy leaders now find themselves at the center of a new governance ecosystem — one that unites compliance, security, ethics, and innovation under a broad umbrella of data responsibility."
It is evident that contemporary governance can no longer be fragmented into technical or legal silos; it now integrates multiple dimensions under the same logic of digital responsibility.
This is because the main risk associated with artificial intelligence is not necessarily in the technology itself, but in the absence of institutional oversight capable of establishing limits, criteria, and responsibilities.
The accelerated adoption of algorithmic solutions often occurs in organizational environments that are still immature from a data governance perspective, which can result in opaque automated decisions, improper reuse of information, amplification of biases, and difficulties in assigning responsibility. AI does not create new vulnerabilities, but it makes existing vulnerabilities visible and more intense.
It is in this context that the role of the Data Protection Officer (DPO) must assume a strategic importance. More than a compliance agent, the DPO becomes integrated into the institutional architecture that connects technology, law, and risk management. This includes participation from the design of systems, monitoring impact assessments, ensuring meaningful human oversight, and building mechanisms for transparency and... accountability These elements become essential for the safe and legitimate implementation of artificial intelligence systems. Experience shows that AI failure rarely stems solely from algorithmic error: it arises when there is no clear governance over who decides, how they decide, and who is responsible for the consequences.
Another significant challenge lies in the quality and organization of the data used for training and operating the systems. Without structured information governance policies, algorithmic models tend to reproduce inconsistencies, increasing operational and legal risks. Unstructured data leads to weak decisions, and Poor decisions, when amplified by technology, can compromise institutional trust and the credibility of organizations.
Therefore, the contemporary discussion on artificial intelligence highlights that technological innovation cannot be treated as a purely technical matter. It is a question of institutional responsibility, of protecting rights and building trust in the organization, whether public or private, and the protection of personal data (not just LGPD) must be the ethical-normative filter for innovation. The next phase of digital transformation will be defined not only by the speed of AI adoption, but by the ability to govern it responsibly. In this scenario, the conclusion becomes inevitable: AI without a DPO is unthinkable.
