Citation:

Narasimha Chaitanya Samineni , 2022. "Designing Masking Rules for 15+ Sensitive Attributes Across the Enterprise with 700+ Applications", ESP Journal of Engineering & Technology Advancements, 2(1): 206-218.

Abstract:

Enterprises operating at scale often manage hundreds of applications across multiple business units, platforms, and data domains. As sensitive data proliferates across customer, financial, healthcare, operational, and identity systems, organizations must implement consistent masking rules that protect more than 15 sensitive attributes while supporting compliance, analytics usability, and hybrid-cloud modernization. Manual or application-specific masking approaches are insufficient in environments with 700+ applications, where inconsistent patterns lead to security gaps, audit findings, and operational overhead [2], [4].This study presents a standardized enterprise-wide masking rule framework designed to achieve uniform protection across diverse technologies, data models, and regulatory obligations. The framework integrates attribute taxonomies, rule definitions, metadata governance, pattern-based detection, and platform-agnostic masking logic, supported by centralized governance workflows and automated distribution pipelines [1], [6]. Performance evaluation demonstrates improvements in masking consistency, classification accuracy, and operational efficiency, enabling organizations to meet privacy, regulatory, and modernization goals while reducing rule fragmentation and compliance risk.

References:

[1] R. Maddali, “Automating Data Quality Assurance Using Machine Learning in ETL Pipelines,” International Journal of Leading Research Publication, vol. 2, no. 6, pp. 1–11, Jun. 2021.

[2] A. Cavoukian, Privacy by Design: The 7 Foundational Principles, Information and Privacy Commissioner of Ontario, 2011.

[3] European Union, General Data Protection Regulation (GDPR), Regulation 2016/679, 2018.

[4] NIST, Guide to Protecting the Confidentiality of Personally Identifiable Information (PII), NIST SP 800-122, 2010.

[5] PCI Security Standards Council, PCI DSS 3.2.1: Requirements and Testing Procedures, 2018.

[6] ISO/IEC 27018, Code of Practice for Protection of PII in Public Clouds Acting as PII Processors, ISO, 2019.

[7] D. Loshin, The Practitioner’s Guide to Data Quality Improvement, Morgan Kaufmann, 2010.

[8] McKinsey & Company, “Managing Data Risk in Modern Enterprise Architectures,” McKinsey Insights, 2020.

[9] Oracle Corporation, Data Masking and Subsetting Guide, Oracle Documentation, 2019.

[10] Gartner Research, Best Practices for Enterprise Data Masking and Sensitive Data Management, 2020.

[11] IBM, Sensitive Data Discovery and Classification for Hybrid Cloud, IBM Redbooks, 2020.

[12] M. Bishop, Computer Security: Art and Science, 2nd ed., Addison-Wesley, 2018.

[13] R. J. Anderson, Security Engineering: A Guide to Building Dependable Distributed Systems, 2nd ed., Wiley, 2008.

Keywords:

Sensitive Data Masking, Enterprise Data Governance, PII/PHI Protection, Rule-Based Masking, Metadata-Driven Classification, Hybrid Cloud Security, Regulatory Compliance, Data Privacy, Multi-Application Governance, Sensitive Attribute Taxonomy.