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AI‑Assisted Zero‑Trust Optimization for Energy‑Efficient Microservices in Financial Systems

by Muzeeb Mohammad
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 187 - Number 67
Year of Publication: 2025
Authors: Muzeeb Mohammad
10.5120/ijca2025926171

Muzeeb Mohammad . AI‑Assisted Zero‑Trust Optimization for Energy‑Efficient Microservices in Financial Systems. International Journal of Computer Applications. 187, 67 ( Dec 2025), 34-45. DOI=10.5120/ijca2025926171

@article{ 10.5120/ijca2025926171,
author = { Muzeeb Mohammad },
title = { AI‑Assisted Zero‑Trust Optimization for Energy‑Efficient Microservices in Financial Systems },
journal = { International Journal of Computer Applications },
issue_date = { Dec 2025 },
volume = { 187 },
number = { 67 },
month = { Dec },
year = { 2025 },
issn = { 0975-8887 },
pages = { 34-45 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume187/number67/aiassisted-zerotrust-optimization-for-energyefficient-microservices-in-financial-systems/ },
doi = { 10.5120/ijca2025926171 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2025-12-24T19:35:21.452444+05:30
%A Muzeeb Mohammad
%T AI‑Assisted Zero‑Trust Optimization for Energy‑Efficient Microservices in Financial Systems
%J International Journal of Computer Applications
%@ 0975-8887
%V 187
%N 67
%P 34-45
%D 2025
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Financial institutions increasingly rely on cloud-native microservices to deliver low-latency trading, payments, and risk-analysis platforms. While this architecture improves agility and scalability, it simultaneously amplifies authentication overhead, security risk exposure, and overall energy consumption. Earlier work by the author introduced a performance-optimized Zero-Trust API security model using token-less authentication and optimized mutual TLS (mTLS), while a companion study demonstrated that asynchronous communication and ARM-based serverless/container runtimes can reduce energy consumption by up to 60% without compromising latency or compliance. This paper unifies and extends these contributions by introducing an AI-assisted optimization framework that jointly minimizes energy usage and enforces Zero-Trust guarantees. The system employs reinforcement learning to dynamically select authentication modes (token-less vs. cached mTLS), choose energy-efficient compute platforms (AWS Lambda, Fargate on ARM or x86), and tune autoscaling thresholds based on real-time workload, risk, and carbon-intensity signals. Experimental evaluation on a synthetic financial workload shows that the proposed framework reduces normalized energy consumption by 50% relative to a baseline, while simultaneously improving average latency by 15%. By demonstrating that strong Zero-Trust enforcement and energy-efficient operations can be co-optimized rather than traded off, this work provides a practical and scalable blueprint for secure, sustainable, and high-performance microservices in modern financial systems.

References
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  2. Mohammad, M. (2025). A Performance-Optimized Zero Trust Architecture for Securing Microservices APIs. International Journal of Computer Engineering and Technology (IJCET), 16(3), 177–187. (Token-less auth + optimized mTLS performance model).
  3. Mohammad, M. (2025). Green Microservices: Energy-Efficient Design Strategies for Cloud-Native Financial Systems. International Journal of Computer Applications (IJCA), 187(56), 45–54. doi: 10.5120/ijca2025925975. (Async comms, ARM, autoscaling, carbon-aware scheduling).
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Index Terms

Computer Science
Information Sciences

Keywords

Zero-Trust security; energy efficiency; reinforcement learning; microservices; cloud computing; carbon awareness

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