Artificial Intelligence is becoming a core enterprise capability across customer service, software engineering, finance, supply chain, analytics, knowledge management, cybersecurity, and autonomous operations. However, the rapid adoption of Generative AI, Large Language Models, Retrieval-Augmented Generation, AI copilots, and agentic AI introduces a new attack surface that extends beyond conventional application security. Enterprises must protect models, prompts, embeddings, vector stores, APIs, tools, identities, business data, AI agents, orchestration layers, and cloud platforms while maintaining privacy, compliance, explainability, availability, and business trust. This paper proposes an Enterprise AI Security Architecture that integrates Zero Trust, Identity and Access Management, DevSecOps, model security, data security, prompt protection, API security, agent governance, threat detection, secure AI operations, incident response, and Responsible AI into a unified enterprise architecture. The framework establishes security controls across the complete AI lifecycle, including design, data preparation, model onboarding, RAG, agent execution, deployment, monitoring, and retirement. The proposed architecture enables organizations to scale enterprise AI securely across hybrid and multi-cloud environments while reducing cyber risk, protecting sensitive information, supporting regulatory compliance, and strengthening operational resilience.

Sanjeeve Kumar Gajadi, "Enterprise AI Security Architecture" International Journal of Scientific Research and Engineering Development, V9(5): Page(554-558) September - October 2026. ISSN: 2581-7175. www.ijsred.com. Published by Scientific and Academic Research Publishing.