International Journal of Scientific Research and Engineering Development

Artificial Intelligence (AI) has become a foundational capability for modern intelligent enterprises,transforming business operations, customer engagement, decision-making, supply chains, cybersecurity, enterprise architecture, and digital innovation. Organizations increasingly deploy Generative AI, Large Language Models (LLMs), predictive analytics, intelligent automation, machine learning, AI assistants, digital twins, and autonomous business processes to improve operational efficiency and create competitive advantage. However, widespread AI adoption introduces significant governance challenges related to transparency, explainability, fairness, accountability, privacy, security, regulatory compliance, ethical decision-making, and organizational trust. Without a structured governance framework, AI systems may generate biased decisions, expose sensitive enterprise data, violate regulations, create cybersecurity vulnerabilities, reduce stakeholder confidence, and increase operational and legal risks. Traditional governance models were developed primarily for conventional information systems and are often inadequate for governing modern AI ecosystems. Artificial Intelligence continuously evolves through learning, model retraining, data updates, autonomous recommendations, and dynamic decision-making, requiring governance capabilities that extend beyond traditional IT governance. Organizations therefore require enterprise-wide Responsible AI governance that integrates business strategy, Enterprise Architecture, Business Data governance, cybersecurity, cloud governance, risk management, compliance, and continuous monitoring throughout the AI lifecycle. This conference paper proposes a comprehensive Responsible AI Governance Framework for Intelligent Enterprises based on the SAP Enterprise Architecture Framework (SAP EAF), TOGAF® Standard, SAP Business Technology Platform (SAP BTP), SAP Business Data Cloud, SAP Datasphere, SAP AI Foundation, SAP AI Core, SAP Joule, SAP Integration Suite, SAP Cloud Identity Services, and SAP Cloud ALM. The framework establishes governance principles covering AI strategy, organizational governance, business alignment, ethical AI, Business Data governance, model lifecycle management, explainability, transparency, accountability, cybersecurity, regulatory compliance, operational monitoring, continuous auditing, and executive oversight. Artificial Intelligence itself is leveraged to strengthen governance by continuously monitoring AI models, Business Data quality, operational performance, security events, governance policies, compliance indicators, fairness metrics, explainability reports, and enterprise risks. Business Data Fabric provides trusted enterprise information that supports secure AI training, enterprise knowledge management, and high-quality decision-making. Platform Engineering, DevSecOps, Enterprise Observability, Infrastructure as Code, and cloud-native engineering further automate AI deployment, governance validation, compliance verification, operational resilience, and lifecycle management. The proposed framework enables organizations to establish trustworthy, explainable, transparent, secure, compliant, scalable, and continuously improving AI ecosystems that support sustainable business innovation while maintaining regulatory compliance, enterprise governance, operational resilience, and stakeholder confidence.

Sanjeeve Kumar Gajadi, "Responsible AI Governance Framework for Intelligent Enterprises" International Journal of Scientific Research and Engineering Development, V9(5): Page(57-84) September - October 2026. ISSN: 2581-7175. www.ijsred.com. Published by Scientific and Academic Research Publishing.