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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