Managing and analyzing data in data lakes for big data environments requires robust protocols to ensure security, scalability, and compliance with privacy regulations. The increasing need to process sensitive data emphasizes the relevance of secure-by-design approaches that integrate encryption techniques and governance frameworks to protect personal and confidential information. This study proposes a protocol that combines the capabilities of Databricks and format-preserving encryption to improve data security and accessibility in data lakes without compromising usability or structure. The protocol was developed using a design science methodology, incorporating findings from a systematic literature review and validated through expert feedback and proof-of-concept experiments in banking environments. The proposed solution integrates multiple layers, data ingestion, persistence, access, and consumption, leveraging the processing capabilities of Databricks and format-preserving encryption to enable secure data management and governance. Validation results indicate the protocol is effectiveness in protecting sensitive data, with promising applicability in regulated industries. This work contributes to addressing key challenges in big data security and lays the groundwork for future developments in data governance and encryption techniques.

Context: Managing and analyzing data in Data Lakes for Big Data environments requires robust protocols to ensure security, scalability, and compliance with privacy regulations. The rapid growth of sensitive data processing highlights the importance of secure-by-design approaches that integrate encryption techniques and governance frameworks to safeguard personal and sensitive information. Aim: This study aims to design and validate a protocol that combines Databricks and Format-Preserving Encryption (FPE) to enhance data security and accessibility in Data Lakes, ensuring data privacy without compromising usability or structure. Method: The study uses Design Science a the main methodology, where a systematic literature review was conducted to identify existing encryption techniques and challenges in data governance. The proposed protocol was developed using a secure-by-design framework, validated through expert feedback, and tested using proof-of-concept experiments in banking environments. Results: The protocol features multiple layers—data ingestion, persistence, access, and consumption—that integrate Databricks’ advanced processing capabilities with FPE. It enables secure data ingestion, controlled access, and efficient governance while maintaining data usability. The validation demonstrated its effectiveness in safeguarding sensitive data, with potential for broader application in regulated industries. Conclusions: This work addresses critical gaps in Big Data security and proposes a scalable, secure framework for Data Lake management. It sets the foundation for future advancements in data governance and encryption methodologies across diverse domains.