From Governance Principles to Enterprise Controls: A Responsible Framework for Agentic AI and Retrieval-Augmented Generation in Regulated Energy and High-Risk Industries

Abstract

Agentic artificial intelligence (AI) and Retrieval-Augmented Generation (RAG) are revolutionizing decision support, automation, knowledge management, and operational processes in regulated energy and other high-risk sectors. But there are a lot of issues with accountability, data integrity, hallucination, cyber security, unauthorized actions, model risk, and regulation compliance. This study introduces a responsible enterprise framework for turning abstract high-level principles of AI governance into concrete, measurable, and enforceable controls for agentic AI and RAG environments. It puts in place a multi-layered control framework with governance, data management, model risk, security, agent autonomy, human oversight, monitoring, and assurance. Specific attention is given to retrieval provenance, access authorisation, tool-use restrictions, approval gates, auditability, continuous monitoring and risk tiered autonomy. The framework then calls for differentiated control requirements based on the operational, financial, environmental, safety and regulatory impacts of decisions made with the help of AI. The proposed approach integrates the governance principles with technical and organizational measures, ensuring that AI systems are deployed responsibly while maintaining their efficiency and adaptability. The study provides a foundation that will be useful for organizations looking to implement responsible AI governance in safety-critical and highly regulated settings.