Data Protection in AI-Driven Environments: The Model for Mapping Institutional Consciousness

Abstract

Artificial intelligence (AI) systems increasingly shape organisational decision-making, yet, the institutional capacities required to align these technologies with Brazil’s General Data Protection Law (LGPD) remain uneven. This article argues that many organisations, whether public or private, face institutional consciousness gaps, defined here as structural, cognitive, and cultural deficiencies that hinder their ability to understand and operationalise the ethical and legal implications of data-driven systems. Drawing on a theoretical–analytical approach, the study identifies how these gaps emerge in AI governance and how they distort accountability and normative interpretation. Building upon this analysis, the article introduces the Model for Mapping Institutional Consciousness (MMIC), a conceptual framework designed to support diagnostic assessment and strengthen governance practices in AI-mediated environments. The study argues that without addressing these internal deficiencies, organisations tend to reproduce formal compliance without substantive alignment with LGPD principles, resulting in governance structures that appear consistent but fail to adequately protect data subjects.