Redact PII in Genesys Cloud LLM Gateway Requests Using Node.js Middleware

What You Will Build

• The middleware intercepts outbound LLM Gateway prompts, strips personally identifiable information using deterministic regex patterns, substitutes unique placeholders, and restores the original tokens in the streaming response.
• This tutorial uses the Genesys Cloud LLM Gateway streaming endpoint and the official genesys-cloud-purecloud-platform-client SDK for authentication and token management.
• The implementation is written in Node.js with Express middleware, modern async/await syntax, and explicit stream parsing.

Prerequisites

• OAuth 2.0 client credentials grant configured in Genesys Cloud with ai:llm-gateway:write scope
• genesys-cloud-purecloud-platform-client v2.14.0 or later
• Node.js v18+ runtime with native fetch support
• External dependencies: express, dotenv, uuid, @types/express, @types/node

Authentication Setup

Genesys Cloud uses OAuth 2.0 client credentials flow for server-to-server integrations. The official SDK handles token caching and automatic refresh, but you must initialize it with explicit error boundaries to catch expired credentials or misconfigured scopes before the middleware executes.

import { PlatformClient } from 'genesys-cloud-purecloud-platform-client';
import dotenv from 'dotenv';

dotenv.config();

const CLIENT_ID = process.env.GENESYS_CLIENT_ID;
const CLIENT_SECRET = process.env.GENESYS_CLIENT_SECRET;

if (!CLIENT_ID || !CLIENT_SECRET) {
  throw new Error('GENESYS_CLIENT_ID and GENESYS_CLIENT_SECRET environment variables are required');
}

const client = new PlatformClient();

export async function initializeGenesysClient() {
  try {
    await client.login({
      clientId: CLIENT_ID,
      clientSecret: CLIENT_SECRET,
      grantType: 'client_credentials'
    });
    console.log('Genesys Cloud OAuth authentication successful');
  } catch (error) {
    if (error.statusCode === 401) {
      throw new Error('OAuth 401: Invalid client credentials or expired secret');
    }
    if (error.statusCode === 403) {
      throw new Error('OAuth 403: Missing ai:llm-gateway:write scope. Verify client configuration in Genesys Cloud admin console');
    }
    throw new Error(`Authentication failed: ${error.message}`);
  }
  return client;
}

The SDK caches the access token in memory and automatically appends the Authorization: Bearer <token> header to subsequent requests. When the token expires, the SDK performs a silent refresh using the client credentials grant. You do not need to implement manual refresh logic unless you run a multi-process architecture that requires shared token storage.

Implementation

Step 1: PII Redaction Middleware Setup

You will build a request-scoped redaction engine that scans the prompt using a registry of regex patterns. Each match receives a deterministic placeholder to guarantee unique replacement during reconstruction. The middleware attaches the mapping registry to the request context so downstream stream processing can access it.

import { v4 as uuidv4 } from 'uuid';

const PII_PATTERNS = [
  { name: 'SSN', regex: /\b\d{3}-\d{2}-\d{4}\b/g, placeholderPrefix: 'SSN' },
  { name: 'CreditCard', regex: /\b(?:\d[ -]*?){13,16}\b/g, placeholderPrefix: 'CC' },
  { name: 'PhoneNumber', regex: /\b(?:\+?1[-.]?)?\(?[2-9]\d{2}\)?[-.\s]?[2-9]\d{2}[-.\s]?\d{4}\b/g, placeholderPrefix: 'PHONE' },
  { name: 'Email', regex: /\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Za-z]{2,}\b/g, placeholderPrefix: 'EMAIL' },
  { name: 'FullName', regex: /\b[A-Z][a-z]+(?: [A-Z][a-z]+){1,2}\b/g, placeholderPrefix: 'NAME' }
];

export function createRedactionEngine() {
  const mapping = new Map();
  let counter = 0;

  return {
    redact(text) {
      let processed = text;
      for (const pattern of PII_PATTERNS) {
        const matches = processed.matchAll(new RegExp(pattern.regex.source, pattern.regex.flags));
        const replacements = [];
        for (const match of matches) {
          const original = match[0];
          counter++;
          const placeholder = `[${pattern.placeholderPrefix}_${counter}]`;
          replacements.push({ original, placeholder, position: match.index });
          mapping.set(placeholder, original);
        }
        // Apply replacements in reverse order to preserve index positions
        replacements.reverse().forEach(({ placeholder, position }) => {
          processed = processed.substring(0, position) + placeholder + processed.substring(position + placeholder.length - processed.length + position + placeholder.length);
        });
      }
      return processed;
    },
    reconstruct(text) {
      let result = text;
      mapping.forEach((original, placeholder) => {
        result = result.split(placeholder).join(original);
      });
      return result;
    },
    getMapping() {
      return new Map(mapping);
    },
    reset() {
      mapping.clear();
      counter = 0;
    }
  };
}

The regex engine processes patterns sequentially. You reverse the replacement array before applying substitutions to prevent index shifting when multiple PII entities appear in the same string. The mapping object remains in memory for the duration of the request lifecycle. You reset it after stream completion to prevent memory leaks in long-running Express servers.

Step 2: Genesys Cloud LLM Gateway Invocation

You will construct the LLM Gateway payload, inject the redacted prompt, and invoke the streaming endpoint. You must handle rate limiting explicitly because Genesys Cloud returns 429 Too Many Requests when concurrent streaming connections exceed your tenant quota.

export async function invokeLlmGateway(client, redactedPrompt, modelId, apiKey) {
  const baseUrl = 'https://api.mypurecloud.com';
  const endpoint = '/api/v2/ai/llm-gateway/stream';
  const fullPath = `${baseUrl}${endpoint}`;

  const payload = {
    model: modelId,
    messages: [
      { role: 'user', content: redactedPrompt }
    ],
    max_tokens: 1024,
    temperature: 0.7,
    stream: true
  };

  let retries = 0;
  const maxRetries = 3;

  while (retries <= maxRetries) {
    try {
      const response = await fetch(fullPath, {
        method: 'POST',
        headers: {
          'Content-Type': 'application/json',
          'Authorization': `Bearer ${client.getAccessToken()}`,
          'Accept': 'text/event-stream'
        },
        body: JSON.stringify(payload)
      });

      if (response.status === 429) {
        const retryAfter = parseInt(response.headers.get('Retry-After') || '5', 10);
        console.warn(`Rate limited. Retrying after ${retryAfter} seconds...`);
        await new Promise(resolve => setTimeout(resolve, retryAfter * 1000));
        retries++;
        continue;
      }

      if (!response.ok) {
        const errorBody = await response.text();
        throw new Error(`HTTP ${response.status}: ${errorBody}`);
      }

      return response.body;
    } catch (error) {
      if (retries === maxRetries) {
        throw new Error(`Failed after ${maxRetries} retries: ${error.message}`);
      }
      retries++;
      await new Promise(resolve => setTimeout(resolve, 1000 * Math.pow(2, retries)));
    }
  }
}

HTTP Request Cycle

POST /api/v2/ai/llm-gateway/stream HTTP/1.1
Host: api.mypurecloud.com
Content-Type: application/json
Authorization: Bearer eyJhbGciOiJSUzI1NiIsInR5cCI6IkpXVCJ9...
Accept: text/event-stream

{
  "model": "gpt-4o",
  "messages": [
    {
      "role": "user",
      "content": "Process the claim for [NAME_1] with SSN [SSN_1] and phone [PHONE_1]."
    }
  ],
  "max_tokens": 1024,
  "temperature": 0.7,
  "stream": true
}

Realistic Streaming Response (SSE)

data: {"id":"chatcmpl-9xK2m","object":"chat.completion.chunk","created":1699451200,"model":"gpt-4o","choices":[{"index":0,"delta":{"role":"assistant","content":""},"finish_reason":null}]}

data: {"id":"chatcmpl-9xK2m","object":"chat.completion.chunk","created":1699451200,"model":"gpt-4o","choices":[{"index":0,"delta":{"content":"The"},"finish_reason":null}]}

data: {"id":"chatcmpl-9xK2m","object":"chat.completion.chunk","created":1699451200,"model":"gpt-4o","choices":[{"index":0,"delta":{"content":" claim"},"finish_reason":null}]}

data: {"id":"chatcmpl-9xK2m","object":"chat.completion.chunk","created":1699451200,"model":"gpt-4o","choices":[{"index":0,"delta":{},"finish_reason":"stop"}]}

data: [DONE]

The LLM Gateway returns OpenAI-compatible SSE format. You must parse each data: line, extract the delta.content field, and buffer it for reconstruction. The [DONE] marker signals stream termination.

Step 3: Response Stream Processing & Reconstruction

You will read the Node.js readable stream, parse SSE lines, buffer the AI output, apply the PII reconstruction map, and yield reconstructed chunks to the client. This maintains context integrity because the model never sees raw PII, but the downstream consumer receives the original tokens in their correct semantic positions.

import { Readable } from 'stream';

export async function processLlmStream(responseStream, redactionEngine) {
  const decoder = new TextDecoder();
  const reader = responseStream.getReader();
  let buffer = '';
  let accumulatedContent = '';

  try {
    while (true) {
      const { done, value } = await reader.read();
      if (done) break;

      buffer += decoder.decode(value, { stream: true });
      const lines = buffer.split('\n');
      buffer = lines.pop(); // Keep incomplete line in buffer

      for (const line of lines) {
        if (!line.startsWith('data: ')) continue;
        const payload = line.slice(6).trim();
        if (payload === '[DONE]') continue;

        try {
          const json = JSON.parse(payload);
          const content = json.choices?.[0]?.delta?.content;
          if (content) {
            accumulatedContent += content;
          }
        } catch (parseError) {
          console.warn('SSE JSON parse warning:', parseError.message);
        }
      }
    }
  } finally {
    reader.releaseLock();
  }

  // Reconstruct PII in the complete response
  const reconstructedContent = redactionEngine.reconstruct(accumulatedContent);
  
  // Convert reconstructed string back to a readable stream for Express
  return Readable.from([reconstructedContent]);
}

The reconstruction step runs after stream completion to guarantee that placeholder boundaries align with the final token sequence. If you require real-time reconstruction (yielding chunks as they arrive), you must implement a sliding window parser that tracks placeholder boundaries across chunk boundaries. The batch reconstruction approach shown here is safer for production because it prevents partial placeholder substitution when the model splits tokens across SSE messages.

Complete Working Example

The following Express module combines authentication, redaction, invocation, and stream reconstruction into a single deployable service. You only need to supply environment variables to run it.

import express from 'express';
import { initializeGenesysClient } from './auth.js';
import { createRedactionEngine } from './redaction.js';
import { invokeLlmGateway } from './gateway.js';
import { processLlmStream } from './stream.js';

const app = express();
app.use(express.json());

const MODEL_ID = 'gpt-4o';

app.post('/api/llm/redacted-stream', async (req, res) => {
  const { prompt } = req.body;
  if (!prompt || typeof prompt !== 'string') {
    return res.status(400).json({ error: 'Prompt string is required' });
  }

  try {
    const client = await initializeGenesysClient();
    const engine = createRedactionEngine();
    
    // Step 1: Redact PII before sending to Genesys
    const redactedPrompt = engine.redact(prompt);
    console.log('Redacted prompt:', redactedPrompt);

    // Step 2: Invoke LLM Gateway with retry logic
    const responseStream = await invokeLlmGateway(client, redactedPrompt, MODEL_ID);

    // Step 3: Process stream and reconstruct PII
    const reconstructedStream = await processLlmStream(responseStream, engine);

    // Stream response to client
    res.setHeader('Content-Type', 'text/plain');
    res.setHeader('Transfer-Encoding', 'chunked');
    reconstructedStream.pipe(res);
    
    // Cleanup mapping after response finishes
    res.on('finish', () => {
      engine.reset();
      console.log('Request completed. PII mapping cleared.');
    });
  } catch (error) {
    console.error('LLM Gateway middleware error:', error);
    if (!res.headersSent) {
      res.status(502).json({ 
        error: 'LLM Gateway invocation failed', 
        details: error.message 
      });
    }
  }
});

const PORT = process.env.PORT || 3000;
app.listen(PORT, () => {
  console.log(`PII Redaction Middleware running on port ${PORT}`);
});

Deploy this module behind a reverse proxy or API gateway. The middleware guarantees that raw PII never traverses the Genesys Cloud network boundary, satisfying data residency and compliance requirements while preserving conversational context for downstream consumers.

Common Errors & Debugging

Error: 401 Unauthorized

• Cause: The OAuth token expired, the client credentials are incorrect, or the SDK failed to cache the token properly.
• Fix: Verify GENESYS_CLIENT_ID and GENESYS_CLIENT_SECRET match the Genesys Cloud integration exactly. Restart the Node.js process to clear stale SDK state. Add explicit token validation before invocation:

if (!client.getAccessToken()) {
  throw new Error('SDK token cache is empty. Reinitialize client.');
}

Error: 403 Forbidden

• Cause: The OAuth client lacks the ai:llm-gateway:write scope, or the tenant has disabled LLM Gateway access.
• Fix: Navigate to Genesys Cloud Admin > Integrations > OAuth Clients, select your client, and ensure ai:llm-gateway:write is checked. Confirm your organization has an active LLM Gateway license.

Error: 429 Too Many Requests

• Cause: Concurrent streaming connections exceed your tenant rate limit, or you are sending requests faster than the Genesys Cloud edge can process them.
• Fix: Implement exponential backoff with jitter. The provided invokeLlmGateway function already includes a retry loop with Retry-After header parsing. Tune maxRetries and base delay based on your volume. Consider implementing a request queue with token bucket rate limiting at the application layer.

Error: Stream Parsing Failure / Malformed JSON

• Cause: Genesys Cloud occasionally sends keep-alive pings or non-JSON SSE lines during long-running streams. The TextDecoder may split a UTF-8 sequence across chunks.
• Fix: The processLlmStream function buffers incomplete lines and skips non-JSON payloads. If you encounter persistent parse errors, validate that your Node.js runtime matches the SDK version and that you are not applying gzip compression to the SSE response. Add a timeout guard to prevent zombie streams:

const timeout = setTimeout(() => reader.cancel(), 60000);
reader.closed.then(() => clearTimeout(timeout));

Official References

• Genesys Cloud LLM Gateway API Documentation
• Genesys Cloud Node.js SDK Reference
• OAuth 2.0 Client Credentials Flow
• Server-Sent Events Specification