ML to Predict Effectiveness of the MCP Authorization Model for LLM-Powered Agent

In today’s AI-driven world, unlocking AI potential and enabling AI models to communicate with external data sources is vital for enhancing the efficiency and security of AI-driven applications. The Model Context Protocol (MCP) serves as a standard for maximizing AI potential. This study leverages a machine learning approach to predict the effectiveness of the MCP Authorization Model for an LLM-powered agent. It utilizes logs from Azure services such as Azure Monitor, Azure Sentinel, and Azure Active Directory, which are used to monitor MCP server activity, to create a sample dataset. This dataset includes features such as source_ip, destination_ip, event_type, alert_severity, and target_variable. These features are used to train the ML model to assess the effectiveness of the MCP Authorization model for LLM-powered agents, enabling organizations to better understand the importance of a secure connection between AI models. This approach contributes to unlocking AI’s full potential while improving application security and operational efficiency.

@article{Bhatta2025,
  title     = {ML to Predict Effectiveness of the MCP Authorization Model for LLM-Powered Agent},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {12},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Upakar Bhatta},
  doi       = {10.14569/IJACSA.2025.0161201},
  url       = {https://doi.org/10.14569/IJACSA.2025.0161201}
}