Trade Secrets, Personal Data, and the Law

This book examines how Brazil's LGPD and comparative regimes manage the clash between transparency and secrecy in the personal data economy. It shows how trade secrets are routinely invoked to expand algorithmic opacity, limiting explainability to data subjects and regulators (ANPD). Beginning with today's obstacles to data protection and transparency, the book maps the legal and technical frictions that undermine accountability, DPIA practice, data portability, and security incident notification.

Brazil is one of the world's most connected markets, with 183 million internet users (86.2% penetration), 144 million social media user identities (67.8% of the population), and 217 million mobile connections (102% of population) at the start of 2025. These scale effects make Brazil a bellwether for platform governance and data rights enforcement. Brazilians also rank among the top global users by time online (= 9h09 per day) and lead global time spent on social media (= 3h49 per day)-a usage profile that amplifies the societal impact of automated decision making and content curation. On the access side, 93.6% of households had Internet in 2024, underscoring mass exposure to data driven services. In payments, Pix has made Brazil a world leader in real time payments (second globally with 37.4 billion instant transactions in 2023), while Open Finance already counts 57.5 million consenting users-both ecosystems hinge on lawful data sharing and explainability.

Against this backdrop, the book argues that the legal framing of core assets-databases, personal data, and algorithms-is a strategic (not neutral) choice: controllers often classify them as trade secrets to restrict scrutiny of automated decision making (e.g., credit scoring) and public sector AI, producing a "legal black box" that distances both individuals and authorities from meaningful oversight. To counter this, the book proposes an interpretation of the LGPD that recalibrates the scope of confidentiality, sets sector specific disclosure baselines for ADM, and defines proportional safeguards for when authorities may access protected information. By prioritizing the fundamental right to data protection, it offers concrete pathways to balance intellectual property interests with algorithmic transparency, preventing secrecy from creating new barriers to rights, regulation, and democratic accountability.