Big Data Confidentiality: An Approach Toward Corporate Compliance Using a Rule-Based System

Area of Science:

• Information Security
• Data Management
• Computer Science

Background:

• Organizations invest in data analytics for competitive advantage, facing legal and regulatory challenges in highly regulated sectors.
• Big data's dimensions (Volume, Velocity, Variety) intensify data loss risks, with Variety being a significant, often neglected, challenge.
• Existing data governance frameworks struggle to address the complexities of data Variety, increasing exposure and loss risks.

Purpose of the Study:

• To propose a novel framework for consistent data evaluation across organizations, mitigating data exposure and loss risks.
• To address the challenges posed by the Variety dimension of big data through algorithmic classification and workflow capabilities.
• To provide a practical solution for enforcing data classification policies and managing exceptions.

Main Methods:

• Development of a rule-based system implementing corporate data classification policy.
• Integration of algorithmic classification and workflow capabilities for consistent data evaluation.
• Implementation of an exception handling process with approval mechanisms.

Main Results:

• A proof-of-concept prototype was developed and evaluated by academics and executives with extensive experience in security and data management.
• 90% of commentators identified data Variety as the most troubling big data dimension.
• Approximately 60% confirmed the existence of appropriate policies and procedures, but highlighted lagging implementation tools.

Conclusions:

• The proposed framework effectively addresses data exposure and loss risks, particularly those related to data Variety.
• There is a recognized need for improved implementation tools to support data classification policies.
• The study underscores the critical importance of managing data Variety in the big data era.