Secure Edge AI Pipelines for Intellectual Property Monitoring Using Retrieval-Augmented Multimodal Models

The rapid growth of digital technologies has significantly increased the creation and distribution of intellectual property across enterprise and research environments. Organizations now manage large volumes of digital assets such as patents, technical documents, engineering designs, and multimedia materials that require continuous monitoring to prevent unauthorized duplication, misuse, or infringement. Traditional centralized monitoring systems often face challenges related to scalability, processing latency, and the secure handling of sensitive proprietary information. These limitations highlight the need for more efficient and privacy-preserving monitoring architectures capable of analyzing diverse data formats in real time.This study proposes a secure edge artificial intelligence pipeline for intellectual property monitoring using retrieval-augmented multimodal models. The proposed framework integrates distributed edge computing infrastructure with multimodal machine learning techniques to process textual, visual, and multimedia intellectual property assets closer to their source. Multimodal transformer-based models are employed to generate unified semantic representations from heterogeneous data sources, while retrieval-augmented mechanisms dynamically access relevant knowledge repositories to enhance contextual analysis and similarity detection. The architecture also incorporates secure processing mechanisms to protect confidential information during distributed computation.Experimental evaluation demonstrates that the proposed edge-based framework improves monitoring accuracy, reduces system latency, and enhances knowledge retrieval capabilities when compared with traditional centralized artificial intelligence approaches. The results indicate that integrating edge intelligence with retrieval-augmented multimodal learning provides a scalable and secure solution for monitoring intellectual property assets in modern digital innovation ecosystems.