Using BigID Through MCP: The Future of Interacting with your Data Security Platform
Model Context Protocol (MCP) is rapidly becoming the standard interface for connecting AI agents to enterprise tools. For security teams, that shift has a concrete implication: the platforms you already rely on can now be driven through natural language, integrated into agentic workflows, and connected to the AI tooling you’re already using. BigID’s MCP server makes that possible for data discovery and securely, today.
What It Means to Use BigID Through MCP
MCP is an open protocol that lets AI models interact with external tools and services in a structured, controllable way. When BigID exposes its capabilities through an MCP server, it means you can invoke BigID functionality, building classifiers, configuring scan templates, launching scans, directly from any MCP-compatible AI client without needing to be platform expert.
The practical result: BigID becomes a first-class participant in your AI-driven security workflows. You describe what you need, the AI agent figures out how to do it in BigID, and you approve the changes before anything executes. This puts BigID in the hands of anyone with access to an agent, such as Claude, Cursor, Copilot Studio et al.
Walking Through a Real Workflow
Here’s what using BigID through MCP actually looks like for a security engineer scoping a new data discovery initiative.
Step 1: Describe the use case. The engineer provides a plain English prompt — in this case, scanning for semiconductor industry-specific sensitive data. The MCP server takes that context and gets to work.
Step 2: Intelligent classifier selection. The agent surveys what classifiers already exist, assesses relevance to the use case, and surfaces its recommendations. For the semiconductor use case, it identified 18 existing classifiers worth reusing and flagged 12 gaps requiring new classifiers, then creates and tests the new classifiers.
Step 3: Human approval, then execution. The engineer reviews and approves the recommendations. The agent then writes the new classifiers and assembles a complete scan template, with existing and new classifiers directly into BigID.
Step 4: Configure and launch. A second prompt targets an S3 bucket, sets scan parameters (exhaustive mode, result preservation enabled), and instructs the agent to configure and launch. It reads the scan template and data source, determines the required settings, presents them for review, and, once approved, creates the scan profile and kicks off the scan.
The entire workflow, from use case description to scan in progress, happens through conversation. No manual UI navigation. No cross-referencing documentation. No configuration errors from missed settings.
Why MCP Is the Right Interface for Security Work
Security teams operate under two competing pressures: move fast and don’t break things. Traditional platform UIs are built for precision, not speed. Custom scripts and API integrations are powerful but brittle and expensive to maintain. MCP strikes a different balance.
Because MCP interactions are explicit and auditable, the agent proposes changes, the engineer approves them, and you get the speed of natural language with the control of a structured approval workflow. Every action is traceable. Every configuration decision is auditable. That’s a meaningful property for security teams operating in regulated environments.
It also means BigID can be embedded into every agentic workflow when needed. A security automation that detects a new sensitive data type, triggers BigID to build the appropriate classifiers, scans the relevant data sources, and reports findings, all without human orchestration between each step, is no longer hypothetical. It’s achievable with BigID and MCP.
What This Unlocks for Your Team
Using BigID through MCP has compounding benefits the more you use it. Classifiers built through one workflow become available for reuse in the next. Scan templates accumulate and can be referenced by name in future prompts. Over time, the system gets faster and more precise as the BigID environment reflects your organization’s actual data landscape.
More immediately, it lowers the expertise barrier for day-to-day BigID operations. A security engineer who understands the business requirement, “scan this S3 bucket for semiconductor IP”, can drive the full workflow without needing to know BigID’s classifier taxonomy in depth. The MCP server handles the platform knowledge; the engineer provides the intent. Importantly with BigID you retain the ability to explain and audit the whole flow.
Getting Started
BigID’s MCP server is available for all enterprise deployments. If your team is already using an MCP-compatible AI client, connecting it to BigID is the fastest path to AI-assisted data discovery workflows.
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