Introduction: 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.
Materials and methods: Sr Data Scientist (Independent
Researcher), Cloud Software
Group Inc., Austin, Texas, USA
ORCID: 0009-0009-3413-1344
Results:
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.
DOI:
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How to cite:
Vinoth Manamala Sudhakar. DESIGNING SECURE AND SCALABLE PYTHON EXECUTION FRAMEWORKS IN BUSINESS INTELLIGENCE SYSTEMS. Vol.1 (2025); DOI:
