Principal directions for deploying secure document workflow on artificial intelligence platforms

The article explores the principal directions for deploying secure document workflow on artificial intelligence platforms using local large language models (LLMs). It analyzes the technological features and functional capabilities of tools such as Ollama and DeepSeek-R1 in the context of ensuring confidentiality in document processing. The advantages of containerizing AI applications based on Docker Desktop are considered, particularly for enhancing system security and component isolation. A conceptual model of a multi-level architecture for a secure document workflow system is proposed, including the infrastructure layer, document processing layer, data protection layer, and monitoring and response layer. Key security risks associated with the use of large language models in document workflow systems are identified, and methodologies for their mitigation are described, notably through the application of the specialized security testing tool Garak.Based on the research findings, practical recommendations have been formulated for the deployment and configuration of secure document workflow systems, taking into account the requirements for autonomy, scalability, and regulated handling of sensitive information. In particular, the feasibility of using local LLMs in environments with strict data transfer policies beyond the organization’s controlled zone (such as government institutions, medical facilities, and defense industry enterprises) has been substantiated. It is established that combining local language models with a containerized infrastructure provides an optimal balance between functionality, performance, and security.The research results demonstrate that integrating local LLMs with containerized infrastructure ensures an optimal balance among functionality, productivity, and security of document workflow systems, especially in sectors with high confidentiality requirements.It is worth noting that a promising direction for further research lies in the development of intelligent, secure, and flexible next-generation public administration systems capable of functioning effectively in complex information environments.