The field of artificial intelligence security is moving towards the development of more robust and standardized frameworks for securing AI systems and their interactions with external data sources. A key area of focus is the Model Context Protocol (MCP), which provides a standardized framework for AI systems to interact with external data sources and tools in real-time. However, this introduces novel security challenges that demand rigorous analysis and mitigation. Recent research has focused on developing enterprise-grade security frameworks and mitigation strategies for MCP, including the use of machine learning-based security solutions and security-first layers for safeguarding MCP-based AI systems. Notable papers include:

• DaemonSec, which explores the role of machine learning for daemon security in Linux environments and presents a systematic interview study on the adoption, feasibility, and trust in ML-based security solutions.
• Enterprise-Grade Security for the Model Context Protocol, which delivers enterprise-grade mitigation frameworks and detailed technical implementation strategies for MCP security.
• MCP Guardian, which presents a framework that strengthens MCP-based communication with authentication, rate-limiting, logging, tracing, and Web Application Firewall scanning.