Enterprise AI Governance Framework™ v1.0 is a practitioner reference framework for operationalizing AI governance across enterprise systems. It provides a structured approach for translating policies, regulatory obligations, risk requirements, and responsible AI principles into actionable controls, traceable evidence, lifecycle reviews, and accountable governance decisions. The framework is organized around four interconnected layers—Policy Logic, Control Families, Evidence Trails, and Review & Escalation—supported by the cross-cutting capabilities of Risk, Audit, and Accountability. It addresses the full AI lifecycle, including risk classification, control architecture, human oversight, agentic AI governance, continuous monitoring, exceptions, incidents, and material change management. Developed by Rakesh Kumar Agrawal and published by Enterprise Intelligence Lab, the framework is intended for enterprise leaders, AI governance professionals, risk and compliance teams, architects, security practitioners, AI engineers, researchers, and organizations seeking to establish scalable, auditable, and operational governance for enterprise AI systems.
Rakesh Kumar Agrawal (Mon,) studied this question.