DESIGNING SECURE AND SCALABLE PYTHON EXECUTION FRAMEWORKS IN BUSINESS INTELLIGENCE SYSTEMS

Vol.1, Issue.1 - 2025

Original Research
Vinoth Manamala Sudhakar

Author Affiliations: Sr Data Scientist (Independent Researcher), Cloud Software Group Inc., Austin, Texas, USA ORCID: 0009-0009-3413-1344

Article Received Date: 2025-08-20

Article Accepted Date: 2025-07-16

Article Publication Date: 2025-08-15

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Abstract: By creating and assessing a containerized, policy-driven execution framework, the study addressed the increasing demand for scalable and secure Python execution in Business Intelligence (BI) applications. The study used a design science research technique to produce a system that included auto-scaling clusters, dynamic load balancing, Role-Based Access Control (RBAC), and sandboxed environments. When compared to native BI Python execution environments, performance benchmarking showed up to 32% quicker execution times and lower resource consumption. Security testing achieved near-complete mitigation and showed excellent detection and prevention rates against denial-of-service, privilege escalation, and malicious code injection attacks. Scalability tests veri?ed a 36% increase in throughput during periods of high workload. The results con?rmed that the suggested framework provided a strong solution for enterprise-scale, data-intensive BI operations by greatly improving performance, security, and dependability.

Conclusion: In comparison to native BI Python execution environments, the examination of the suggested secure and scalable Python execution framework for business intelligence systems showed notable gains in scalability, security, and speed. The framework lowered CPU and memory use under high workloads and delivered up to 32% quicker execution times through resource optimization, dynamic load balancing, and containerized isolation. Superior detection and prevention rates against denial-of-service attacks, privilege escalation, and malicious code injection were validated by security testing, guaranteeing enterprise-grade protection without sacri?cing functionality. Up to 36% more throughput was also found by scalability research, allowing for the dependable management of massive concurrent workloads. Together, these results showed that the framework offered a reliable, efficient, and safe way to integrate Python with BI platforms, which made it ideal for contemporary, dataintensive business settings.

Keywords: Python Execution Framework, Business Intelligence, Secure Computing, Scalability, Containerization, Role-Based Access Control, Distributed Processing, BI Security.

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How to cite:

Sudhakar, V. M. (2025). DESIGNING SECURE AND SCALABLE PYTHON EXECUTION FRAMEWORKS IN BUSINESS INTELLIGENCE SYSTEMS. International Journal of Information Systems in Engineering and Management (IJISEM), PP 1-7.