Secuvy AI Data Security is an agentless data security platform designed to prevent sensitive data from being exposed through AI tools such as ChatGPT, Microsoft Copilot, and other large language models (LLMs). The platform operates using a Model Context Protocol (MCP)-based architecture that sits between an organization's data and LLM surfaces. It intercepts, classifies, and sanitizes sensitive inputs in real time before they reach AI systems, without requiring agents, browser extensions, or regex rule writing. Data classification is performed using an unsupervised, self-learning engine that adapts to organizational context rather than relying on static patterns. It can identify PII, PHI, intellectual property, financial models, legal documents, CUI, ITAR-controlled data, source code, and other sensitive content types across unstructured data. Policies can be configured to mask, block, or allow content based on sensitivity level, and these policies apply consistently across web-based AI usage, API-driven workflows, and retrieval-augmented generation (RAG) systems. The platform is designed for compliance with HIPAA, CMMC, NIST, and emerging AI governance frameworks. It provides audit-ready logs, LLM usage dashboards, and risk analytics for security, privacy, and compliance teams. Deployment is intended to be completed within minutes using the Secuvy MCP Server, and the tool is LLM-agnostic, requiring no changes to the existing LLM stack. Classification runs within the customer's tenant, and Secuvy does not train on customer data.