Connect Strac to ChatGPT: Securely Vault and Redact Sensitive Data

If your security and IT teams use Strac for Data Loss Prevention (DLP) and sensitive data vaulting, giving an AI agent direct access to those systems represents a massive compliance risk. You want the productivity gains of ChatGPT automating risk audits and redacting documents, but you cannot hand over raw API keys or write sprawling custom integration code to handle the access patterns safely. (If your team uses Claude instead, check out our guide on connecting Strac to Claude, or explore our broader architectural overview on connecting Strac to AI Agents).

The mandate is clear: automate data security workflows using natural language without compromising the integrity of your vault. The engineering reality of connecting a Large Language Model (LLM) to Strac's REST API is complex. You have to translate raw binary streams into text, handle dynamic proxy schemas, and strictly enforce authentication scopes. You can either spend weeks building and hosting a custom Model Context Protocol (MCP) server, or you can use a managed infrastructure layer to handle the boilerplate.

This guide breaks down exactly how to use Truto to generate a secure, managed MCP server for Strac, connect it natively to ChatGPT, and execute complex security workflows using natural language.

The Engineering Reality of the Strac API

A custom MCP server is a self-hosted integration layer that translates an LLM's JSON-RPC tool calls into REST API requests. While the open MCP standard provides a predictable way for models to discover tools, implementing it against Strac's specific architecture requires solving several non-standard API challenges.

Binary vs. JSON Responses Many Strac operations defy standard REST paradigms. For example, when calling get_single_strac_document_by_id or get_single_strac_redacted_document_by_id, the Strac API does not return a structured JSON response. It returns an opaque binary file stream. An LLM cannot natively parse or ingest raw binary data through a standard MCP text content block. Your custom server must intercept this response, handle the buffer, and potentially extract or summarize the file metadata before passing it back to the agent. If you fail to account for this, the tool call will crash or output gibberish to the context window.

Dynamic Proxy Schemas Strac heavily utilizes outbound and inbound proxying (create_a_strac_proxy, create_a_strac_proxy_redact). These endpoints allow you to send an HTTP request to a third-party service while Strac sits in the middle, swapping sensitive data for tokens. The challenge here is that the response shape is entirely determined by the target URL. Because the schema is infinitely dynamic, you cannot write a static OpenAPI or JSON Schema definition for these endpoints. The LLM must be explicitly instructed on how to construct the proxy payload and how to interpret the raw response, making standard CRUD tool definitions useless.

Atomicity in Batch Tokenization When you use create_a_strac_tokens_batch to tokenize up to 200 elements, the entire batch operation is atomic. If a single element in the array fails validation, the entire request fails. Furthermore, creating tokens is not implicitly idempotent - sending duplicate input data generates new token IDs unless specifically configured otherwise. An LLM that retries a failed partial batch without understanding this constraint will rapidly pollute your vault with orphaned tokens.

Rate Limits and Pass-Through Architecture When dealing with rate limits, you must understand exactly where the responsibility lies. Strac enforces quotas on API requests. Truto does not retry, throttle, or apply backoff on rate limit errors. When the upstream Strac API returns an HTTP 429 Too Many Requests, Truto passes that error directly to the caller (your MCP client) and normalizes the rate limit information into standardized IETF headers (ratelimit-limit, ratelimit-remaining, ratelimit-reset). Your client application must implement the retry and exponential backoff logic. Do not assume the infrastructure will absorb 429s for you.

The Managed MCP Approach

Instead of forcing your engineering team to build a custom translation layer for Strac's binary endpoints and proxy architectures, Truto provides a managed MCP server.

Truto dynamically generates MCP tools based on Strac's API documentation. There is no manual schema mapping required. When you connect an integrated Strac account, Truto's getTools() engine iterates through available endpoints, applying a documentation gate - if an endpoint is documented, it becomes an AI tool. The query and body parameters are automatically compiled into flat JSON schemas that ChatGPT can ingest natively.

Method 1: Creating the MCP Server via the Truto UI

For IT admins or product managers who need immediate access, you can generate a secure server URL directly from the dashboard.

1. Navigate to the integrated account page for your connected Strac instance.
2. Click the MCP Servers tab.
3. Click Create MCP Server.
4. Select your desired configuration. You can filter tools by methods (e.g., only allow read operations) or apply specific tag filters.
5. Copy the generated MCP server URL. This URL contains a cryptographically hashed token scoped explicitly to this tenant's connection.

Method 2: Creating the MCP Server via the API

For engineering teams orchestrating AI access programmatically, you can generate MCP servers on the fly via the Truto API. This is crucial for multi-tenant architectures where every end-user needs a tightly scoped server.

Execute a POST request to /integrated-account/:id/mcp:

POST https://api.truto.one/integrated-account/<strac_account_id>/mcp
Authorization: Bearer <TRUTO_API_TOKEN>
Content-Type: application/json
 
{
  "name": "Strac Audit and Redaction Agent",
  "config": {
    "methods": ["read", "create"],
    "tags": ["vault", "audit"]
  },
  "expires_at": "2026-12-31T23:59:59Z"
}

The response returns the secure endpoint URL:

{
  "id": "mcp_token_abc123",
  "name": "Strac Audit and Redaction Agent",
  "config": {
    "methods": ["read", "create"],
    "tags": ["vault", "audit"]
  },
  "expires_at": "2026-12-31T23:59:59Z",
  "url": "https://api.truto.one/mcp/t_xyz987securetoken"
}

This URL acts as the definitive authentication gateway. The underlying token is hashed with an HMAC key before storage in Cloudflare KV, ensuring that even internal database access does not expose the raw connection string.

Connecting the Strac MCP Server to ChatGPT

Once you have your Truto MCP URL, you must connect it to your LLM framework. ChatGPT supports remote MCP connections, allowing it to natively discover the Strac tools derived from the integration.

Method A: Via the ChatGPT UI (Custom Connectors)

If your organization uses ChatGPT Enterprise, Pro, or Team tiers, you can add the server directly through the interface.

1. Copy the MCP server URL from the Truto API or UI.
2. In ChatGPT, navigate to Settings -> Apps -> Advanced settings.
3. Toggle Developer mode to ON.
4. Under MCP servers / Custom connectors, click add a new server.
5. Enter a logical name (e.g., "Strac Vault Operations").
6. Paste the Truto MCP URL into the Server URL field.
7. Click Save. ChatGPT will immediately initialize the connection and fetch the tools/list.

Method B: Via Manual SSE Configuration

If you are building a custom agent wrapper around the OpenAI API, you connect using a Server-Sent Events (SSE) transport layer. You use the @modelcontextprotocol/server-sse package to connect to the Truto router.

{
  "mcpServers": {
    "strac_vault": {
      "command": "npx",
      "args": [
        "-y",
        "@modelcontextprotocol/server-sse",
        "--url",
        "https://api.truto.one/mcp/t_xyz987securetoken"
      ]
    }
  }
}

If your MCP token was created with require_api_token_auth: true, you must configure the client to send your Truto API token in the Authorization header to satisfy the secondary authentication middleware.

Hero Tools for Strac

Once connected, ChatGPT has access to the curated endpoints defined in the integration resources. The MCP router handles the complex task of flattening the LLM's arguments into query and body parameters. Here are the highest-leverage tools available for Strac.

create_a_strac_document

Uploads a file directly to the Strac vault and returns a reference documentId. This tool is strictly immutable. If the LLM repeats the call with the exact same binary content, it generates a completely new stored document and a new reference ID. The maximum file size limit is 10 MB.

"I need to securely vault this CSV file containing customer records. Upload it to Strac and give me the reference document ID so I can process it later."

create_a_strac_detect

Submits a payload or text segment to the Strac detection engine to identify sensitive data. It returns a structured output defining exactly what PII or PCI data was found, and where it resides within the submitted structure.

"Scan this customer support transcript for any sensitive data. Identify the exact locations of any SSNs, credit card numbers, or phone numbers using the Strac detect tool."

create_a_strac_redact

This tool processes inline text content, replacing detected sensitive fields with tokenized Strac vault links or mask characters according to your configured redaction mode. It returns the newly sanitized string, ensuring no PII leaks into your downstream analytics or logs.

"Take the following meeting notes and run them through Strac redaction. Replace all names and phone numbers with tokens, and return the cleaned text."

create_a_strac_tokens_batch

Creates a batch of up to 200 Strac tokens by sending raw sensitive data elements to the vault in a single atomic request. It returns an array of reference identifiers. Remember that if one element fails validation, the whole batch is rejected.

"I have a list of 50 employee passport numbers. Send them to Strac in a single batch tokenization request and map the returned token IDs back to the employee names."

create_a_strac_proxy_redact

Sends an HTTP request through Strac's outbound proxy to a third-party API, redacting sensitive data in transit. Because the response shape is determined by the target URL, the LLM must handle parsing the dynamic JSON returned by the destination service.

"Execute a POST request to the vendor API through the Strac proxy redact tool. Pass the customer payload, but ensure the SSN field is vaulted before the payload hits the vendor endpoint."

list_all_strac_data_security_risks

Queries the Strac engine for discovered security risks across your integrated applications. You can filter by date range, application type, and sensitive data types to pinpoint vulnerabilities.

"Pull all data security risks detected in our Slack integration over the last 7 days. Group them by severity and summarize the findings."

For the complete tool inventory, request schemas, and pagination logic, consult the Strac integration page.

Workflows in Action

Connecting Strac to ChatGPT enables sophisticated, agentic automation. Here are two concrete scenarios demonstrating how these tools chain together to execute real-world security operations.

Scenario 1: Automated PII Remediation in Support Logs

An IT administrator wants to identify if a recent customer support ticket contains leaked PII, vault it securely, and replace the original text with tokens.

"Analyze this raw support ticket text. Detect any sensitive data. If you find any, execute an inline redaction and give me the sanitized output so I can update the ticketing system."

1. create_a_strac_detect: ChatGPT sends the raw text to Strac to verify the presence of PII (e.g., identifying a stray credit card number in the notes).
2. create_a_strac_redact: The agent submits the text for inline redaction. Strac replaces the credit card number with a vaulted token.
3. ChatGPT outputs the sanitized string, confirming that the original data is securely vaulted. (For a broader look, see our technical deep dive on implementing PII redaction via MCP).

sequenceDiagram
    participant Admin as IT Admin
    participant ChatGPT as ChatGPT
    participant TrutoMCP as Truto MCP Server
    participant StracAPI as Strac API

    Admin->>ChatGPT: "Analyze and redact this ticket..."
    ChatGPT->>TrutoMCP: Call create_a_strac_detect
    TrutoMCP->>StracAPI: POST /detect
    StracAPI-->>TrutoMCP: Returns PII locations
    TrutoMCP-->>ChatGPT: Parsed schema
    ChatGPT->>TrutoMCP: Call create_a_strac_redact
    TrutoMCP->>StracAPI: POST /redact (inline)
    StracAPI-->>TrutoMCP: Returns sanitized text
    TrutoMCP-->>ChatGPT: Standardized tool response
    ChatGPT-->>Admin: Provides clean, tokenized ticket data

Scenario 2: Secure Outbound Proxying

A DevOps engineer needs to call an external analytics vendor, but company policy dictates that no raw email addresses can leave the network. They use the LLM to write the proxy logic.

"I need to send this user event payload to our analytics vendor at https://api.vendor.com/events. Route it through the Strac redaction proxy so the email addresses are tokenized before leaving our infrastructure."

1. create_a_strac_proxy_redact: ChatGPT formulates the proxy payload, specifying the target URL and the raw JSON body.
2. Strac intercepts the payload, tokenizes the emails based on vault policies, and forwards the scrubbed data to the vendor.
3. The vendor's API responds, Strac relays the dynamic response through Truto, and ChatGPT confirms the successful transmission to the engineer.

flowchart TD
    A["DevOps Engineer"] -->|"Proxy this payload..."| B["ChatGPT"]
    B -->|"Call create_a_strac_proxy_redact"| C["Truto MCP Server"]
    C -->|"POST /proxy/redact"| D["Strac Outbound Proxy"]
    D -->|"Scrub data & forward"| E["Vendor API"]
    E -->|"Dynamic 200 OK"| D
    D -->|"Relay response"| C
    C -->|"Return tool result"| B

Security and Access Control

Exposing an enterprise vault to an LLM requires strict boundary controls. Truto enforces security at the protocol level (learn more about our zero-data-retention architecture for compliance), ensuring your Strac configuration remains isolated and tamper-proof.

• Method Filtering: You can explicitly deny ChatGPT access to destructive endpoints. By setting config.methods: ["read"] during token generation, Truto will silently skip building tools like create_a_strac_document or delete_a_strac_document_by_id, leaving only safe audit tools.
• Tag Filtering: Limit the server scope to specific resource groups. If you only want the LLM to access proxy features, apply config.tags: ["proxy"]. The intersection of method and tag filters creates a tightly constrained toolset.
• Expiration (TTL): Avoid leaving permanent credentials in developer environments. Use the expires_at property to create ephemeral MCP servers. Cloudflare KV handles the immediate TTL expiration, and a Durable Object alarm ensures the underlying database records are aggressively purged.
• Require API Token Auth: For Zero Trust environments, possessing the MCP URL is not enough. Enabling require_api_token_auth enforces a secondary middleware check. The ChatGPT client must present a valid Truto API bearer token to complete the JSON-RPC handshake.

Wrapping Up

Connecting Strac to ChatGPT shouldn't require your engineering team to absorb the maintenance burden of handling binary buffers, dynamic proxy schemas, or atomic tokenization batches. By utilizing a dynamically generated MCP server, you abstract the infrastructure overhead while retaining strict control over what the LLM can read and write.

Whether you are automating PII redaction from support logs or orchestrating complex security audits, Truto's documentation-driven routing ensures your AI agents have the exact schemas they need, instantly.