AI-Powered Policy Calibration: A Framework for Dynamic Regulation Compliance in LLM Applications
DOI:
https://doi.org/10.32628/CSEIT25113352Keywords:
Policy-as-Code, LLM Governance, Regulatory Compliance, AI Ethics, Prompt Moderation, Language Models, AI Policy FrameworkAbstract
The recent spread of large language models (LLMs) into sensitive sectors like healthcare, finance, and the legal domain has pushed the need for their regulatory compliance into the spotlight. Unfortunately, traditional LLM deployments are static and do not respond to the dynamic differences in law or governance that jurisdictions around the world are increasingly developing. This paper introduces a novel framework, powered by AI, for what we term policy steering in LLM-based applications. Our approach integrates principles of policy-as-code into an operational context of LLMs that allows for dynamic injection of regulatory and ethical constraints during generation. Using real-world LLM applications as a basis for design, we built a system whose comparative advantage over traditional compliance systems is highlighted in several evaluation metrics.
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