The Future Of Regulated Ai: Scaling Llms With Oversight And Precision
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Abstract
Large Language Models (LLMs) possess transformative generative capabilities; however, their large-scale deployment in regulated domains—specifically finance and healthcare—demands robust infrastructure, continuous monitoring, and rigorous safety guardrails. This paper investigates best practices for cloud-based LLM deployment, proposing architectures that prioritize scalability, compliance, and reliability. We delineate secure infrastructure designs incorporating container orchestration and hardware acceleration to satisfy high-performance requirements. Additionally, the study details real-time monitoring frameworks for anomaly detection and comprehensive guardrail mechanisms—ranging from prompt filtering to human-feedback fine-tuning—to ensure alignment with legal and ethical standards. Through an analysis of financial and clinical use cases and associated challenges such as data privacy and bias, this work demonstrates that strategic design and oversight enable the effective, compliant scaling of LLMs in sensitive industries
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