How Data Connectivity Turns ChatGPT into Your Company’s Operating System

ChatGPT Enterprise is transformative for businesses. It becomes a daily tool when mission critical applications and systems are directly connected to it. Having the context of company data in your requests makes interactions more impactful and actionable. There are different levels and considerations for how to connect to your enterprise data safely.

Most enterprises should treat data access in ChatGPT as a three-layer stack. Start with the simplest approach and move to higher complexity only when the security requirements demand it. This holds true for almost all AI solutions you plan to build.

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Layer 1: The Simplest Path (Enable Native ChatGPT Apps)

Native ChatGPT Apps (formerly known as Connectors) are the new table stakes for connecting to enterprise data. These are pre-built integrations managed by OpenAI that allow ChatGPT to interact with common SaaS tools.

In 2026, these apps have evolved to support two primary behaviors:

• Synced Knowledge: For heavy hitters like SharePoint, Google Drive, and Notion, ChatGPT can periodically index your data. This allows for instant retrieval of strategy summaries or policy lookups.
• Ambient Data Pulls: For high-velocity tools like Salesforce, Jira (via Atlassian Rovo), or Slack, the AI reaches out in real-time to fetch specific tickets or conversation history only when requested.

Use Case: Unstructured Knowledge Discovery in Google Drive

A marketing manager needs to find brand voice guidelines buried in a folder from three years ago. By using the native Google Drive App, the user can ask ChatGPT to summarize the tone requirements for social media across multiple archived PDFs. The AI identifies the relevant documents and provides a concise summary without the user ever having to manually dig through a messy file system.

Governance Tip: Admins can block specific file paths (such as the /Payroll or /Legal folders) in Google Drive while allowing the rest of the company to access general documentation. Use ChatGPT Groups to ensure that sensitive apps like Stripe or HubSpot are only visible to the teams that need them.

When to use: Always. Use this for all standard SaaS tools where you want immediate value with zero developer overhead.

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Layer 2: The "Low Friction" Path (Custom MCP Apps)

This is the growing middle ground between buying a tool and building a full application. If you have ChatGPT Enterprise, you already have the infrastructure to bring custom data into your environment using the Model Context Protocol (MCP).

On this path, you host a private MCP server that exposes your internal tools (SQL databases, custom CRMs, or proprietary telemetry) and your employees interact with the tools directly inside the familiar ChatGPT UI.

The Security Profile of MCP

• Pros: It uses ChatGPT’s native Enterprise security, including SSO and data encryption. Admins have Role-Based Access Control (RBAC) to decide which specific employees can access which internal MCP tools.
• Cons: The primary risk is prompt injection. Because the model acts as the "operator," a malicious prompt could trick the AI into calling a "write" action that a user did not intend to trigger. There is also the risk of context leakage, where ChatGPT sends more conversation history to your internal server than is strictly necessary for the task.

Hardening Layer 2: The Secure AI Gateway

If you choose the MCP route, you should not connect ChatGPT directly to your sensitive databases. Instead, the industry standard is to place an AI Gateway (or MCP Proxy) in the middle to act as a “firewall” for your LLM.

This gateway performs three critical functions:

• Identity Propagation: It verifies that the user calling the tool has the actual OIDC/OAuth permissions to see that data.
• PII Redaction: It scans outgoing results and redacts Social Security Numbers or private keys before they reach the model.
• Human-in-the-Loop: For destructive actions, the gateway intercepts the request and sends a request to the user for manual approval.

Use Case: Real-Time Inventory Lookups via SQL

A warehouse supervisor can query live stock levels by connecting ChatGPT to a local PostgreSQL Database using a custom MCP server. When the user asks for the current count of a specific SKU, the MCP server translates that request into a database query and returns the live result. Note that depending on your database complexity, you may need a text-to-SQL middleware layer to ensure the model accurately maps natural language to your specific table schemas.

Governance Tip: Implement Scope Minimization by creating multiple, specialized MCP servers rather than one all-encompassing server with access to everything. Ensure your AI Gateway logs all "Model-to-Tool" calls in a centralized security information and event management (SIEM) system. This allows you to audit not just what the user asked, but the exact technical queries the AI attempted to run against your internal databases.

When to use: For proprietary data lookups and read-only workflows where you want to move fast without building a completely new frontend (ChatGPT Widgets can go a long way).

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Layer 3: The Hardened Path (AgentKit & Full Stack)

Finally, we reach the complete build option. This is the "hardened" path where you build a standalone web application using OpenAI AgentKit to manage the logic and your own authentication layer to manage users.

Why Complexity Equals Safety

• Deterministic Guardrails: In this environment, you can write hard code (e.g., if user_role is not Admin, block the request) that the LLM cannot bypass via prompt injection. The logic is in your code, not in a system prompt.
• Granular Auditing: You own the logs entirely. You can see exactly what prompt led to what API call with 100% transparency.
• Zero Trust Patterns: You can implement stricter network controls and egress filtering because you own the entire request path.

The Trade-off: This comes with a high "Developer Tax." You are now responsible for maintaining the frontend, the auth server, and the hosting infrastructure. The attack surface shifts from the AI model to traditional application security (XSS, CSRF, and secret management).

Use Case: High-Governance Financial Reporting on Snowflake

For sensitive financial data stored in Snowflake, an organization builds a standalone application using OpenAI AgentKit to ensure absolute data integrity. This custom interface connects to a high-precision text-to-SQL engine that strictly validates every query against the user's specific permissions before execution. This approach provides a "hard" shell of deterministic code that prevents the AI from accessing unauthorized schemas by passing user-identity tokens directly in every payload.

Governance Tip: Use Identity-Aware Proxying and ensure your AgentKit application uses "Short-Lived Tokens." By passing the user’s original identity (OIDC) through to the data layer (like Snowflake or a private API), the data source itself enforces the permissions. This ensures that even if the AI logic is compromised, the underlying data remains protected by the same "Least Privilege" rules used in your traditional IT stack.

When to use: For high-stakes environments (Finance, Healthcare, Legal) where you need absolute certainty about data flow or when you want to own the entire user experience.

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Conclusion: Choosing Your Integration Strategy

Selecting the right way to connect company data to ChatGPT depends on your specific balance of speed, security, and user experience.

• Native Apps are your baseline. They are managed by OpenAI and provide the fastest time to value for common SaaS tools. Every enterprise should start here to give employees the context they need for daily communication and planning.
• Custom MCP Apps represent the modern enterprise standard for internal data. They allow you to "rent" the ChatGPT interface for your own databases, offering a high-speed deployment path. When paired with a Secure AI Gateway, they provide a robust middle ground that mitigates most injection and data leakage risks.
• Full Stack with AgentKit is your "Zero Trust" option. It is the most secure path because it places a hard barrier between the AI's reasoning and your data's execution. While it requires the most development effort, it is the only choice for highly regulated workflows where manual approvals and deterministic logic are non-negotiable.

By following this tiered approach, you can ensure that your organization reaps the benefits of AI-powered context without compromising the security of your most sensitive assets. 

Navigating the layers of enterprise data involves non-trivial decisions at every turn. Let Eliza be your guide to mastering those nuances and unlocking your data’s full potential.