Adaptive Data Analytics Using Ethical AI Agents and Logic-Based Compliance Engines
Abstract
This paper presents an integrated framework for Adaptive Data Analytics that leverages Ethical AI Agents and Logic-Based Compliance Engines to ensure responsible, transparent, and regulation-aligned decision-making in complex data environments. The proposed architecture combines autonomous analytical agents capable of dynamic learning with a formal logic–driven compliance layer that continuously evaluates data operations against ethical guidelines and regulatory constraints. The system adapts to evolving data patterns, user intents, and contextual risk factors while maintaining traceable and explainable reasoning. By embedding rule-based ethical safeguards and automated compliance validation into the analytics workflow, the framework enhances trustworthiness and mitigates biases, security concerns, and policy violations. Experimental evaluation demonstrates improvements in accuracy, fairness, and compliance consistency compared to traditional analytics pipelines. This work contributes a scalable, interoperable model for deploying ethical, resilient, and governance-ready AI-driven analytics across sensitive domains such as finance, healthcare, and intelligent enterprises
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