Implementing Data Loss Prevention (DLP) Scanners within Genesys Cloud Web Messaging
What This Guide Covers
You are building a real-time Data Loss Prevention (DLP) scanning layer integrated into Genesys Cloud’s Web Messaging (Messenger) and Open Messaging channels. When complete, your system will inspect every inbound customer message and every outbound agent reply for sensitive data patterns-credit card numbers, Social Security Numbers, healthcare record identifiers (MRNs), and proprietary internal document codes-before the message is committed to the conversation thread. Detected violations are redacted in transit, the agent is coached inline, and all violation events are streamed to your SIEM for compliance reporting.
Prerequisites, Roles & Licensing
- Genesys Cloud: CX 2 or 3 with Web Messaging or Open Messaging.
- Permissions required:
Integrations > Integration > Edit(for Messenger or Open Messaging webhook config)Conversations > Message > Edit(for the DLP service account)
- Infrastructure:
- A middleware DLP service (AWS Lambda behind API Gateway, or a Node.js container).
- A DLP policy engine: either AWS Macie, Google Cloud DLP, Microsoft Purview, or a custom regex/ML-based engine.
- A SIEM (Splunk, Azure Sentinel) for violation event streaming.
The Implementation Deep-Dive
1. The Two DLP Interception Points
There are two distinct moments to intercept messages in a digital conversation:
Point A - Pre-Delivery (Inbound from Customer):
A customer types “My SSN is 123-45-6789” into the chat window. Before the message is displayed to the agent, the DLP layer must detect and redact the SSN.
Point B - Pre-Send (Outbound from Agent):
An agent is about to reply and accidentally includes a full policy number or internal system credential. Before the message is delivered to the customer, it must be caught.
Both interception points use the same DLP policy engine but are triggered from different architectural locations.
2. The DLP Service API Design
Your DLP middleware exposes a single scan endpoint that both interception points use:
from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
import re
import boto3
import json
app = FastAPI()
MACIE = boto3.client('macie2', region_name='us-east-1')
class ScanRequest(BaseModel):
message_text: str
conversation_id: str
direction: str # "INBOUND" or "OUTBOUND"
agent_id: str | None = None
class ScanResult(BaseModel):
is_clean: bool
redacted_text: str
violations: list[dict]
# Priority-ordered DLP rules (most specific first)
DLP_RULES = [
{"name": "CreditCard", "pattern": r"\b(?:\d[ -]*?){13,16}\b", "replacement": "[CREDIT CARD REDACTED]", "severity": "CRITICAL"},
{"name": "SSN", "pattern": r"\b\d{3}-\d{2}-\d{4}\b", "replacement": "[SSN REDACTED]", "severity": "CRITICAL"},
{"name": "MRN", "pattern": r"\bMRN[-:]?\s*\d{6,10}\b", "replacement": "[MRN REDACTED]", "severity": "HIGH"},
{"name": "InternalDocCode", "pattern": r"\bINT-[A-Z]{2}-\d{6}\b", "replacement": "[INTERNAL REF REDACTED]", "severity": "MEDIUM"},
]
@app.post("/dlp/scan", response_model=ScanResult)
async def scan_message(request: ScanRequest):
text = request.message_text
violations = []
for rule in DLP_RULES:
matches = re.findall(rule["pattern"], text, re.IGNORECASE)
if matches:
violations.append({
"rule": rule["name"],
"severity": rule["severity"],
"matchCount": len(matches),
"conversationId": request.conversation_id,
"direction": request.direction,
"agentId": request.agent_id
})
# Redact the match
text = re.sub(rule["pattern"], rule["replacement"], text, flags=re.IGNORECASE)
return ScanResult(
is_clean=len(violations) == 0,
redacted_text=text,
violations=violations
)
3. Intercepting Inbound Messages (Open Messaging Webhook)
For Genesys Cloud Open Messaging, all inbound messages arrive at your webhook endpoint first. Your DLP layer intercepts here.
Modified Webhook Handler:
import requests
def handle_inbound_message(webhook_payload: dict, genesys_token: str) -> dict:
"""
Scans inbound customer messages before creating the Genesys conversation event.
"""
message_text = webhook_payload.get("text", "")
conversation_id = webhook_payload.get("id")
# 1. Scan the message
scan_response = requests.post(
"http://localhost:8000/dlp/scan",
json={"message_text": message_text, "conversation_id": conversation_id, "direction": "INBOUND"}
).json()
if not scan_response["is_clean"]:
# 2a. Replace the message text with the redacted version
webhook_payload["text"] = scan_response["redacted_text"]
# 2b. Stream violation events to SIEM
stream_violations_to_siem(scan_response["violations"])
# 2c. Log violation note to the conversation
add_system_note_to_conversation(
conversation_id=conversation_id,
note=f"[DLP ALERT] {len(scan_response['violations'])} sensitive data pattern(s) automatically redacted from customer message.",
genesys_token=genesys_token
)
# 3. Forward the (potentially redacted) message to Genesys Cloud
return create_genesys_inbound_message(webhook_payload, genesys_token)
4. Intercepting Outbound Agent Replies
Outbound interception requires a custom agent desktop widget. The widget intercepts the agent’s message text before the POST /api/v2/conversations/messages/{conversationId}/communications/{communicationId}/messages API call is made.
// Custom MAX Widget JavaScript (conceptual)
async function onAgentSendClick(draftText, conversationId) {
// 1. Pre-scan the draft
const scanResult = await fetch('/dlp/scan', {
method: 'POST',
headers: {'Content-Type': 'application/json'},
body: JSON.stringify({
message_text: draftText,
conversation_id: conversationId,
direction: 'OUTBOUND',
agent_id: currentAgentId
})
}).then(r => r.json());
if (!scanResult.is_clean) {
// 2. Show a blocking alert to the agent
showDlpViolationAlert({
violations: scanResult.violations,
redactedText: scanResult.redacted_text,
onConfirmRedacted: () => sendMessage(scanResult.redacted_text),
onCancel: () => { /* Agent rewrites the message */ }
});
return; // Block the original send
}
// 3. If clean, send as normal
sendMessage(draftText);
}
5. SIEM Integration for Compliance Reporting
All violations stream to your SIEM for audit purposes.
import requests
def stream_violations_to_siem(violations: list[dict]):
"""Sends DLP violation events to Splunk HEC."""
splunk_url = "https://splunk.yourcompany.com:8088/services/collector"
splunk_token = "SPLUNK_TOKEN"
for v in violations:
requests.post(
splunk_url,
headers={"Authorization": f"Splunk {splunk_token}"},
json={
"event": v,
"source": "genesys_dlp",
"sourcetype": "dlp_violation"
}
)
Validation, Edge Cases & Troubleshooting
Edge Case 1: DLP Latency Breaking the Chat UX
If the DLP scan takes 400ms, every message the agent sends has a 400ms delay. For fast-paced chats, this creates a noticeable lag that frustrates agents.
Solution: Run the DLP scan asynchronously for outbound messages in low-risk scenarios. Only enforce a synchronous blocking scan for CRITICAL severity rule categories (Credit Cards, SSNs). For MEDIUM severity rules (internal reference codes), scan asynchronously and alert the supervisor after the fact without blocking the agent’s workflow.
Edge Case 2: False Positives on Legitimate Data
A customer provides their 16-digit loyalty card number. The DLP scanner identifies it as a credit card and redacts it. The agent cannot see the number and cannot help the customer.
Solution: Implement a Luhn algorithm validation step after the regex match. A loyalty card number that fails the Luhn check is almost certainly not a credit card and should be excluded from the CreditCard rule matches.
Edge Case 3: Multi-Language Content Bypassing Regex
A customer writes their SSN in a non-standard format (common for non-US customers using dashes or spaces differently). Standard regex may miss the pattern.
Solution: For multi-language or multi-format deployments, augment your regex rules with a cloud-native DLP service (AWS Macie or Google Cloud DLP) which uses ML-based entity detection that is more tolerant of formatting variations. Use the regex layer as a fast, cheap first pass, and the cloud DLP service as a slower, more comprehensive second pass for HIGH/CRITICAL messages only.