What These Terms Actually Mean—and Why They Matter to Risk Teams

Cutting Through the AI Integration Buzzwords

AI capabilities are advancing at breakneck speed. With every major release, we're seeing new ways that third-party software can interact with AI tools like Claude, ChatGPT, and others.

But with that evolution comes a wave of new, fuzzy, and sometimes confusing terminology. Terms like MCP Server, connector, plugin, and RAG are now appearing in vendor announcements, product updates, and even security reviews.

• For security, compliance, and risk professionals, it can be challenging to determine:
• What do these terms actually mean?
• Are they just different labels for the same thing?
• Which ones create real risk for your systems or data?

In this guide, we'll cut through the jargon with clear definitions, practical examples, and intuitive analogies that help you identify and manage these emerging risks.

Realistic Scenario: When Separate Approvals Aren't Enough

Imagine you've already approved Atlassian for Jira/Confluence, Salesforce as a CRM, and Claude for AI-assisted work. Each passed your vendor risk assessments individually.

A few weeks later, you learn employees are:

• Connecting Claude to Atlassian to pull feature release schedules and product roadmap details from Jira
• Connecting Claude to Salesforce to access account executive notes on lost opportunities and competitive displacement reasons
• Combining this information inside Claude to identify patterns, then writing strategic recommendations back to both systems

From a user's perspective, it's just "working faster." But from a TPRM perspective, your risk surface has changed:

• Data Commingling: Sensitive data from Salesforce and Atlassian now coexists in Claude's context, potentially bypassing segregation controls.
• Expanded Attack Surface: Claude becomes a central hub—if compromised, multiple systems are exposed.
• Audit Complexity: Trails are fragmented across three platforms, complicating investigations.
• Unreviewed Access Path: Your original approvals didn't cover this combined, AI-mediated workflow.
• Key takeaway: When AI acts as a hub between vendors, the risk isn't just additive—it's multiplied.

Understanding the Key Concepts in This Evolving Landscape

As standards continue to emerge around AI integration, these definitions represent our current understanding of how these technologies work and the risks they present.

1. What Are Connectors and Plugins?

While often used interchangeably, there are important distinctions:

Connectors are user-authorized bridges that let an AI tool (like Claude or ChatGPT) access data from another application (like Google Drive, Jira, or Notion).

How connectors work:

• Set up in the AI tool's settings
• Uses OAuth or token-based authentication
• Scope is defined by the user
• Primarily focused on data retrieval

Plugins typically offer more functionality than simple connectors:

• May include both data access and action capabilities
• Can perform operations within the connected system
• Often have more complex permissions models
• May include custom UI elements within the AI interface

Risk lens: Both create potentially broad and unstructured access, governed by the AI platform with limited vendor-side visibility. Plugins generally create a larger attack surface due to their ability to perform actions.

2. What Is an MCP Server?

An MCP (Model Context Protocol) Server is a vendor-hosted API layer that defines exactly what the AI can do, see, or change inside the vendor's platform. This protocol, initially introduced by Anthropic, is still being formalized across the industry.

How it works:

• Vendor exposes specific functions/data via schema
• AI calls these functions programmatically
• Access is authenticated and logged by the vendor

Risk lens: More governable than connectors, but still expands the attack surface through AI-mediated requests.

Industry status: As this landscape evolves, implementations branded as "MCP servers" may vary significantly. Some vendors may adopt only portions of the protocol or implement their own variations while using similar terminology. Standards bodies are beginning to address these variations, but unified standards remain in development.

3. What Is RAG?

RAG (Retrieval-Augmented Generation) is a technique where an AI model retrieves relevant data before generating its response.

How it works:

• AI sends a retrieval request to one or more data sources
• Relevant documents or records are returned
• The AI uses that retrieved context to craft its output

Important distinctions:

• RAG can work with both internal knowledge bases and external connected systems
• The risk profile changes dramatically when RAG pulls from multiple systems simultaneously
• The primary security concern emerges when RAG retrieves from different security contexts or data classifications within a single operation

Risk lens: If retrieval scope is too broad or spans multiple systems, RAG can surface and combine sensitive information unintentionally, potentially bypassing established data segregation controls.

4. Standard Integrations (For Comparison)

A traditional point-to-point integration between software systems, typically built for specific purposes with pre-defined data exchanges and well-established governance models.

The Retail Analogy: Simplifying Complex Concepts

To better understand how these integration methods differ in terms of control, risk, and data handling, consider this retail shopping analogy:

• Connector = Shopping With No Supervision
  Like walking into a store with your own shopping list. You can browse any available section, access any item in inventory, and use it however you want after leaving. The store knows you entered but doesn't control or monitor what you take or how you use it.
   
• Plugin = Shopping with Enhanced Access
  Similar to connector shopping, but you also have the ability to rearrange store displays, place special orders, and access employee-only areas, based on your permissions.
   
• MCP Server = Guided Shopping Experience
  Like shopping with a store associate who has clear guidelines. You tell them what you need, and they retrieve specific items based on store policies. They'll only show you departments you're authorized to access, and all your interactions are logged in the store's system.
   
• Standard Integration = Pre-packed Purchase
  Like ordering a pre-selected outfit bundle. You get exactly what was advertised—no more, no less—with the contents clearly defined and approved in advance.
   
• RAG = Personal Shopper Across Multiple Stores
  Like hiring someone to gather items from different stores on your behalf. They might visit the clothing store (Atlassian), shoe store (Salesforce), and accessories boutique (Google Drive) in one trip, bringing everything back in a single bag. Items from different stores get mixed together, and the boundaries between where each item came from become blurred.

Note: While this retail shopping analogy provides a simplified introduction to these integration methods, it doesn't capture the full technical complexity, security considerations, and governance requirements of each approach. In practice, these integration methods involve sophisticated authentication mechanisms, data handling protocols, API permissions models, and various security controls that go well beyond the shopping metaphor. Organizations should consult with security and AI governance specialists when implementing any of these integration methods.

How Are They Different from Standard Integrations?

Why This Matters for Risk and TPRM Teams

AI integrations don't just add features. They add new access paths to your vendors' systems and your data. That means:

• You may be exposed to AI tools you haven't reviewed
• Vendors may enable real-time access you didn't account for
• Traditional SIGs or security questionnaires may miss it entirely

Questions to ask vendors now:

• Do you support AI tools like Claude or ChatGPT?
• Do you expose data via connectors, plugins, or MCP Servers?
• How is access authenticated, scoped, and logged?
• Are AI interactions covered by your DPA or security policies?
• What controls prevent inappropriate data commingling across systems?

When Should a Vendor's AI Integration Trigger a Reassessment?

• Launch of an MCP Server, connector, or plugin that enables AI-driven access to your data/workflows
• Use of RAG that retrieves from multiple connected systems, potentially mixing data
• Support for autonomous agent use that can initiate queries or actions without human prompt
• Any AI-mediated workflow that links two or more previously separate vendor systems

Trigger a targeted reassessment focused on:

• Data scope and sensitivity
• Logging and monitoring controls
• Revocation and restriction capabilities
• Contractual coverage for AI access
• Data classification handling

Emerging Security Controls

As these technologies mature, emerging best practices suggest implementing:

• For Connectors/Plugins:
  • Data classification controls that limit sensitive data exposure
  • User permission audits and regular access reviews
  • Content filtering for data leaving your systems
     
• For MCP Servers:
  • Detailed access logging requirements
  • Clearly defined function limitations
  • Regular security testing of the API surface
     
• For RAG Implementations:
  • Source attribution requirements
  • Cross-system data handling policies
  • Clear boundaries for information flow between systems

Final Takeaway

AI-native interfaces like MCP Servers, connectors, plugins, and RAG may look similar to end users, but from a risk perspective, they behave very differently. Understanding the difference—and recognizing when AI is linking systems in new ways—is essential for protecting your organization's data.

As this landscape continues to evolve, staying informed about emerging standards and implementation variations will be critical for effective third-party risk management.

Clarify the terms. Then clarify the risk.