The Intelligent Governance Core: A Multi-Layer AI Framework for Predictive Compliance and Autonomous Digital Analytics
DOI:
https://doi.org/10.63282/3050-9416.IJAIBDCMS-V6I1P117Keywords:
Intelligent Governance Core, Multi-Layer Ai Framework, Predictive Compliance, Autonomous Digital Analytics, Ai-Driven Governance, Data Integrity Engineering, Compliance Automation, Privacy-Preserving Ai Systems, Full-Stack Governance Architecture, High-Fidelity AnalyticsAbstract
Cloud computing, artificial intelligence (AI), Internet of Things (IoT), and platform-based services have very fast augmented the digital eco systems leading to a greater increase in organizational vulnerability to regulatory complexity, operational risk and governance downfalls. Conventional governance, risk, and compliance (GRC) solutions are perceived to be relying on manual audit, rule engines, and post-factum reporting, which is not sufficient in real-time, data-intensive, and autonomous digital-driven contexts. This paper responds to these constraints by presenting The Intelligent Governance Core (IGC) - a multi-layer AI-informed structure that will facilitate predictive compliance, autonomous digital analytics and adaptive governance orchestration.The suggested framework incorporates machine learning, knowledge graphs, natural language processing (NLP), and reinforcement learning in multi-layers of governance, such as data acquisition, regulatory intelligence, predictive risk modelling, compliance automation, and decision optimization. The IGC framework is proactive to predict compliance deviations unlike the conventional type of GRC systems which are reactive to administration of compliance; it interprets the regulatory requirements in a dynamic manner and its advice is autonomous and corrective. Its architecture aims at providing continuous operation over distributed enterprise systems in order to be transparent, accountable, and explainable in matters of AI-driven governance decisions.It is a thorough methodological design of the IGC framework, backed by formal models, governance processes and compliance intelligence pipes. Simulated enterprise scenarios continually show that there are enhancements and improvements in regulatory compliance, efficiency in auditing, resilience in operations, and reduction in the decision latency. The findings show that predictive compliance models can detect any possible violation much earlier than regulation-based models, whereas autonomous analytics enables organizations to respond better to new laws.The article has not only contributed to the academic literature but also to the practice in the industry since the research offers a single theoretical framework to intelligent governance infrastructure and provides a scalable, explainable, and morally consistent AI governance framework. The results elucidate the rebirth promise of AI-supported governance cores in facilitating resilient, trustful and law-abiding digital businesses
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