A Linear Programming-Based Optimization Model for Cost-Efficient Regulatory Compliance with Multi-Standard Cybersecurity Frameworks

This study explores the development of a Linear Programming (LP)-Based Optimization Model for cost-efficient regulatory compliance with multi-standard cybersecurity frameworks. The proposed model integrates traditional linear programming techniques with adaptive resource allocation strategies to optimize compliance costs while ensuring adherence to frameworks such as GDPR, HIPAA, and NIST. Over a 12 -month period, resource allocation increases from 25 % to 80 %, reflecting the growing complexity of compliance as the organization scales. The model dynamically adjusts resources to ensure that regulatory requirements are met without exceeding budgetary constraints. The resource efficiency factor, <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$z(t)$</tex>, plays a crucial role in reducing compliance costs, as it improves from 0.5 to 1, indicating a significant decrease in overall costs. Experimental validation using the CICIDS 2017 dataset demonstrated a 20 % reduction in compliance costs, validating the model's effectiveness in optimizing resource distribution and cost efficiency. By aligning with established frameworks and optimizing resources, the model provides a practical solution for businesses to balance security and compliance costs in the face of evolving regulatory requirements.