Artificial Intelligence is becoming embedded in enterprise decisions, customer interactions, software engineering, operations, and autonomous workflows. As adoption expands, organizations must manage not only model performance but also trust, risk, security, compliance, explainability, privacy, resilience, and accountability. This paper proposes an Enterprise AI Trust, Risk and Compliance (AI TRiSM) Framework that integrates enterprise architecture, AI governance, model risk management, cybersecurity, privacy, Responsible AI, legal compliance, observability, and lifecycle controls. The framework establishes governance domains for AI inventory, risk classification, model assurance, data and prompt governance, identity, security, human oversight, monitoring, incident response, regulatory evidence, and recertification. It supports predictive AI, Generative AI, Retrieval-Augmented Generation, and Agentic AI across hybrid and multi-cloud environments. The proposed approach enables enterprises to scale AI while maintaining measurable trust, bounded risk, defensible compliance, and clear accountability.

Sanjeeve Kumar Gajadi, "Enterprise AI Trust, Risk and Compliance (AI TRiSM) Framework" International Journal of Scientific Research and Engineering Development, V9(5): Page(940-942) September - October 2026. ISSN: 2581-7175. www.ijsred.com. Published by Scientific and Academic Research Publishing.