Model Context Protocol (MCP)

Model Context Protocol (MCP) is an open standard that enables AI applications and AI agents to securely connect with external tools, data sources, and services through a standardized interface. Rather than requiring developers to build custom integrations for every application or data repository, MCP provides a standardized method for AI models to request information, invoke approved tools, and exchange context with enterprise systems. This allows organizations to extend the capabilities of AI applications while maintaining governance over what information they can access and which actions they are permitted to perform. As organizations increasingly deploy AI across business operations, MCP standardizes how AI applications interact with enterprise systems, making integrations more scalable, portable, and easier to govern using existing identity and access controls.

One emerging use case for Model Context Protocol is enabling an agentic AI chatbot capability that allows AI systems to move beyond answering questions based solely on training data. Through MCP, an AI agent can retrieve current information from approved business systems, analyze operational data, recommend actions, and perform authorized tasks across multiple applications, subject to the organization's security controls. This approach allows organizations to build AI workflows that are more accurate, context-aware, and useful, while reducing the need for proprietary integrations between every AI model and enterprise application.

Because AI systems increasingly interact with sensitive business data and critical infrastructure, Model Context Protocol also plays an important role alongside AI security tools. Organizations must ensure that AI agents only access authorized resources, that user identities are verified, and that every action is governed by existing security policies. Identity-aware security controls, continuous monitoring, and modern authentication methods help reduce the risk of unauthorized access, privilege misuse, or compromised AI agents. As organizations adopt passwordless access and other modern identity protections, these security measures help ensure AI systems operate within established trust boundaries while supporting enterprise security and compliance objectives.

Although MCP standardizes how AI systems communicate with enterprise resources, organizations still need identity-centric security controls around those interactions. Imprivata helps strengthen identity security for employees, customers, and third-party vendors through Identity Assurance and Threat Detection (IATD) and advanced passwordless access. Using natural language prompts that simplify investigation and operational workflows, organizations can more quickly investigate suspicious activity, understand security findings, and take appropriate action. These capabilities help security teams operationalize identity intelligence while supporting secure adoption of AI-enabled workflows.