Retrieval-Augmented Generation (RAG), Generative AI, and Agentic AI Governance: An Integrated Enterprise Governance Prioritization Architecture
Abstract
Abstract Enterprise adoption of artificial intelligence (AI) systems, including Generative AI (GenAI), Retrieval-Augmented Generation (RAG), and agentic AI, is advancing faster than many organizations can adapt their governance, audit, cybersecurity, and executive oversight practices. This paper develops a proposed integrated AI governance prioritization architecture using a design-science research approach, supported by exploratory platform-risk analysis, a reproducible platform-risk dataset, a Monte Carlo robustness assessment, a framework coverage matrix, a Governance Priority Score (GPS)-based demonstration, and a public-document relevance assessment. The primary contribution is an integrated governance decision-support architecture that uses the Enterprise AI Platform Taxonomy (EAPT) to estimate platform burden, the Enterprise AI Risk Taxonomy (EART) to organize governance-risk exposure, the Enterprise AI Governance Maturity Model (EAGM) to represent organizational readiness, and the Integrated Agentic AI Governance Framework (IAGF) to identify agentic AI oversight requirements within a unified workflow. These components are operationally connected through GPS, which links platform burden, governancerisk gaps, organizational readiness, agentic runtime-control needs, and executive decision support. The proposed architecture draws upon NIST AI RMF, NIST AI 600-1, ISO/IEC 42001, COBIT, OWASP LLM Top 10, MITRE ATLAS, the EU AI Act, and related AI governance literature. A…
Read the paper
Related studies
- Knowledge Management Perspective of Generative Artificial Intelligence
- Navigating Generative Artificial Intelligence Promises and Perils for Knowledge and Creative Work
- Characterizing the evolving research on enterprise content management
- Large language models and multimodal foundation models for precision oncology
- From Generative AI to Extended Reality: Multidisciplinary Perspectives on the Challenges, Opportunities, and Future of Educational Computing
- Generative Artificial Intelligence: A New Engine for Advancing Environmental Science and Engineering
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.