Designing an Enterprise Engineering Platform for Integrating AI Tools and Preserving Intellectual Property Rights

Abstract

This article presents a novel architectural framework for enterprise engineering platforms designed to integrate diverse artificial intelligence tools while preserving intellectual property rights. Drawing from systems integration theory and digital rights management principles, the article proposes a layered architecture that addresses the unique challenges at the intersection of AI implementation and IP protection. The article incorporates comprehensive data lineage tracking, contextual access controls, and graduated protection mechanisms that adapt based on asset sensitivity and usage context. The architecture enables organizations to maintain clear ownership boundaries and attribution chains throughout AI-augmented workflows, addressing a critical gap in current enterprise implementation practices. Through case studies spanning financial services, pharmaceutical research, and manufacturing, the article demonstrates the flexibility and effectiveness of this approach across varying industry contexts and regulatory requirements. The article includes a comprehensive evaluation methodology that balances technical protection metrics with user experience considerations, acknowledging that successful implementation requires both technical robustness and organizational adoption. By providing a blueprint for secure AI integration that minimizes friction in legitimate workflows while preventing unauthorized IP access and usage, this article offers practical guidance for organizations seeking to harness AI's transformative potential without compromising their intellectual assets.