Integrating AI into enterprise operations has presented significant challenges, particularly regarding the adoption of the Model Context Protocol (MCP), which emerged to address the complexities of implementing AI across diverse data sources. Despite its potential, enterprise adoption of MCP is hindered by several issues, including security vulnerabilities, lack of enterprise-grade authentication features, and operational challenges like scalability and configuration. The security concerns are heightened by the absence of comprehensive OAuth 2.1 compliance and complex token management requirements, which complicate the transition from proof-of-concept to full deployment. Additionally, the permission model required for MCP demands a shift from traditional API-level controls to function-level granularity, complicating the integration with existing identity provider systems and single sign-on capabilities. Enterprises also struggle with visibility and control, needing robust audit and access management systems that MCP does not inherently provide. These challenges necessitate solutions that can bridge the gap between MCP's functionality and enterprise needs, such as Descope's Identity Hub, which offers tools to enhance authentication and manage permissions effectively.