Model Context Protocol (MCP) Security and Tenancy Boundaries

Abstract

The Model Context Protocol (MCP) is the mainframe that can control a multitude of contextual interactions in multi-tenant AI as well as cloud scenarios. In such conditions, users or organizations may use the same computational and data resources. AI systems that run on common infrastructures are calling for the securing of contextual data exchanges between tenants and models as a very serious issue. By introducing standard channels, MCP solves this problem for models in order to have access to, comprehend, and use tenant-specific contexts without violating isolation boundaries. Nevertheless, keeping contextual cues tenant-confined so as not to allow data leakage, inference crossover, and unauthorized model memory persistence necessitates very strong and strict boundary control. Establishing clear tenancy boundaries goes a long way in protecting sensitive metadata, prompts, and dynamic session states that are crossing AI-driven workflows. The paper investigates the security of the MCP architecture by first looking at its core principles and the risks it faces, including implicit data propagation and model-based context drift. A possible methodology drafts in the MCP framework the use of local encryption, dynamic policy enforcement, and tenant-aware context tagging to mitigate the risks. The work significantly achieved better containment levels and fewer cross-tenant incidents in adversarial simulations, as shown by the experiments carried out. The findings emphasize the need for integrating security and tenancy-awareness into the model context layer itself rather than depending externally on access control mechanisms. In the end, this research offers a viable route to companies that are willing to implement secure multi-tenant AI systems where contextual intelligence is free to develop without the risk of privacy or compliance infringements.