AI/MCP Concepts

AI/MCP concepts focus on the principles and technologies behind Artificial Intelligence (AI) and Machine-Controlled Processes (MCP). These include automation, machine learning, decision-making algorithms, and intelligent systems that enhance operational efficiency and reduce human error.

Categories:

Device Identity

AI/MCP Concepts

Device identity refers to the unique, verifiable characteristics that allow an organization to recognize a device as legitimate. It is the foundation for determining whether a laptop, mobile phone, server, or IoT endpoint should be allowed to access corporate networks, applications, or data.

AI Agent Gateway

AI/MCP Concepts

An AI agent gateway is an architectural component that sits between AI agents and the external systems, APIs, and data sources they need to access. It manages routing, security enforcement, traffic management, and observability for agent-to-agent and agent-to-resource communications.

MCP Gateway

AI/MCP Concepts

An MCP gateway is a specialized security and routing service that sits between your AI agents and the resources they access (like databases and APIs). It enforces authentication and authorization decisions, policy enforcement, and audit logging for all MCP-based communications.

Credential Provider

AI/MCP Concepts

A credential provider is a system that securely issues, manages, and delivers credentials, such as API keys, access tokens or certificates, to software workloads that need to access protected data. Unlike traditional secrets storage, credential providers generate or deliver these credentials dynamically based on a workload identity that has already been verified by a trust provider and evaluated against policy. They often issue short-lived credentials that expire automatically, reducing exposure if they are compromised.

Cloud-Native Application Protection Platform (CNAPP)

AI/MCP Concepts

A Cloud-Native Application Protection Platform (CNAPP) is a unified framework that combines many security tools into one single platform.It combines vulnerability management, misconfiguration detection, runtime threat protection, and workload security into a single platform that understands the dynamic, distributed nature of modern cloud infrastructure.

Cloud Identity

AI/MCP Concepts

Cloud identity systems handle authentication for workloads, services, and users running in cloud platforms like AWS, Azure, and GCP using API-first approaches with standardized protocols like OAuth 2.0, OpenID Connect, and SAML 2.0. They issue short-lived, cryptographically verifiable tokens that replace the long-lived credentials found in older authentication systems.

Blended Identity

AI/MCP Concepts

Blended Identity refers to a modern identity model for user-driven AI agents in which the agent operates using a composite identity derived from two simultaneous sources of trust: The agent’s own workload identity (cryptographically verifiable, rooted in a trust provider), and The identity of the human user currently engaging or instructing the AI agent. The combination produces a dynamic, runtime-only identity that shapes what the agent can do, enforces least privilege, and preserves full accountability for user-initiated actions taken by the agent. What makes Blended Identity unique is that, although anchored in workload identity, it must also integrate with an enterprise’s workforce identity system to generate this composite identity just-in-time at the moment of invocation.

Self-RAG

AI/MCP Concepts

Self-RAG (Self-Retrieval Augmented Generation) is an emerging AI architecture in which a model autonomously retrieves, filters, and evaluates its own contextual information during the generation process, without relying on an external retriever service. It merges retrieval and reasoning within the model itself, allowing for adaptive, self-supervised access to relevant knowledge or memory.

Retrieval-Augmented Generation (RAG)

AI/MCP Concepts

Retrieval-Augmented Generation (RAG) is an AI architecture that enhances large language models (LLMs) by combining real-time information retrieval with generative reasoning. Instead of relying solely on pre-trained model knowledge, RAG systems query external data sources, retrieve relevant content, and feed it into the model’s prompt context to generate accurate, up-to-date, and domain-specific responses.

Multi-Agent System (MAS)

AI/MCP Concepts

A Multi-Agent System (MAS) is a coordinated network of autonomous AI agents that communicate and collaborate to accomplish shared goals. Each agent operates independently, perceiving its environment, making decisions, and taking actions, but the system as a whole behaves collectively, distributing intelligence across agents rather than centralizing it in one model.