Towards LGPD Compliance: Analysis and Support to Prepare Your Computing Environment
Resumo
The current business landscape has witnessed an exponential proliferation of technologies, such as big data, artificial intelligence, and the Internet of Things, which significantly impact the collection, storage, and processing of data. Innovations like mass data collection, predictive analytics, and process automation enhance companies’ ability to gain valuable insights, optimize operational efficiency, and improve the customer experience. However, these technological advances also bring new responsibilities. Despite the progress, adequate data protection practices are still not consistently applied, and gaps remain in the awareness of data subjects’ rights. The importance of the right to privacy and control over the use and sharing of sensitive personal data has driven the development of various solutions and regulations worldwide. In Brazil, the General Data Protection Law (LGPD), in force since 2020, emerged as a regulatory framework to guide companies’ actions, encouraging the adoption of best practices in the use of technologies to protect individual rights. This work aims to present a guideline for the use of technological solutions that, if adopted by companies, can minimize their vulnerabilities related to data protection and bring them closer to compliance with the LGPD. First, compliance with the LGPD is investigated through a questionnaire conducted with Brazilian companies. Next, available technological solutions are analyzed. Finally, the two sets of results are combined to develop an interactive guideline designed to help companies assess their own compliance status and gain insights to improve their computational environment.
Publicado
27/10/2025
Como Citar
PEDRAZOLI, Aldrey; IVAKI, Naghmeh; MORAES, Regina.
Towards LGPD Compliance: Analysis and Support to Prepare Your Computing Environment. In: LATIN-AMERICAN SYMPOSIUM ON DEPENDABLE COMPUTING (LADC), 14. , 2025, Valparaíso/Chile.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 54-71.
