Automating PII Redaction in Genesys Cloud Media Tracks with Python SDK

What You Will Build

A Python service that polls Genesys Cloud transcription jobs, extracts sensitive data using a custom regular expression engine, generates precise timestamp boundaries for redaction regions, submits batched redaction requests to the Media API, processes webhook callbacks for job completion, and writes structured compliance audit logs. This uses the Genesys Cloud Python SDK (genesys-cloud-python-sdk) and standard REST endpoints. The tutorial covers Python 3.9+.

Prerequisites

• OAuth 2.0 Client Credentials flow with scopes: recordings:view, media:redact, analytics:conversations:view, webhooks:manage
• Genesys Cloud Python SDK v2.0+ (pip install genesys-cloud-python-sdk)
• Python 3.9+ runtime
• External dependencies: fastapi, uvicorn, httpx, pydantic
• Environment variables: GENESYS_CLIENT_ID, GENESYS_CLIENT_SECRET, GENESYS_BASE_URL (default: https://api.mypurecloud.com)

Authentication Setup

The Genesys Cloud Python SDK handles token acquisition and automatic refresh when using OAuthClientCredentialsProvider. You must configure the client with your environment variables and assign it to the API service classes.

import os
from platform_sdk import PureCloudPlatformClientV2, OAuthClientCredentialsProvider
from platform_sdk.rest import ApiException

def initialize_genesys_client() -> PureCloudPlatformClientV2:
    client = PureCloudPlatformClientV2()
    client.set_base_url(os.environ.get("GENESYS_BASE_URL", "https://api.mypurecloud.com"))
    
    provider = OAuthClientCredentialsProvider(
        client_id=os.environ["GENESYS_CLIENT_ID"],
        client_secret=os.environ["GENESYS_CLIENT_SECRET"]
    )
    client.set_auth_provider(provider)
    
    return client

The SDK caches tokens in memory and refreshes them before expiration. You do not need to implement manual token rotation. The client propagates the Authorization: Bearer header automatically across all API calls.

Implementation

Step 1: Poll Transcription API for Completed Jobs

The Transcription API exposes job status through GET /api/v2/recordings/transcripts/{transcriptionId}. You must poll this endpoint until the status equals COMPLETED or FAILED. The SDK method get_recordings_transcripts_transcription_id returns a Transcription object containing the status field and the recording_id.

import time
from platform_sdk.api.recordings_transcripts_api import ApiRecordingsTranscriptsApi
from platform_sdk.rest import ApiException

def poll_transcription_status(
    transcripts_api: ApiRecordingsTranscriptsApi,
    transcription_id: str,
    max_retries: int = 15,
    poll_interval: int = 10
) -> dict:
    for attempt in range(max_retries):
        try:
            response = transcripts_api.get_recordings_transcripts_transcription_id(
                transcription_id=transcription_id
            )
            
            if response.status == "COMPLETED":
                return {
                    "status": response.status,
                    "recording_id": response.recording_id,
                    "segments": response.segments
                }
            elif response.status == "FAILED":
                return {"status": "FAILED", "error": response.error_message}
                
            time.sleep(poll_interval)
            
        except ApiException as e:
            if e.status == 429:
                retry_after = int(e.headers.get("Retry-After", poll_interval))
                time.sleep(retry_after)
                continue
            raise

This function respects the Retry-After header for 429 responses and terminates early on failure. You must pass the authenticated ApiRecordingsTranscriptsApi instance to avoid recreating HTTP sessions.

Step 2: Parse Transcript Segments and Detect Sensitive Patterns

Genesys Cloud returns transcript data as an array of Segment objects. Each segment contains a words array with precise start_time and end_time values. You will build a custom regex engine that scans segment text, maps matches to word boundaries, and extracts exact timestamps.

import re
from typing import List, Dict, Any

PII_PATTERNS = {
    "SSN": r"\b\d{3}-\d{2}-\d{4}\b",
    "CREDIT_CARD": r"\b(?:\d[ -]*?){13,16}\b",
    "EMAIL": r"\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,}\b",
    "PHONE": r"\b\d{3}[-.]?\d{3}[-.]?\d{4}\b"
}

def detect_pii_in_transcript(segments: List[Any]) -> List[Dict[str, str]]:
    redaction_regions: List[Dict[str, str]] = []
    
    for segment in segments or []:
        if not segment.words:
            continue
            
        channel_id = segment.channel_id
        segment_text = segment.text or ""
        
        for pii_type, pattern in PII_PATTERNS.items():
            matches = list(re.finditer(pattern, segment_text, re.IGNORECASE))
            
            for match in matches:
                match_start_idx = match.start()
                match_end_idx = match.end()
                
                # Map character offsets to word timestamps
                char_offset = 0
                matched_words = []
                
                for word in segment.words:
                    word_text = word.text or ""
                    word_start = char_offset
                    word_end = char_offset + len(word_text)
                    
                    # Check if word overlaps with regex match
                    if word_end > match_start_idx and word_start < match_end_idx:
                        matched_words.append(word)
                        
                    char_offset = word_end + 1  # Account for space padding in transcript text
                    
                if matched_words:
                    earliest_start = min(w.start_time for w in matched_words)
                    latest_end = max(w.end_time for w in matched_words)
                    
                    redaction_regions.append({
                        "start_time": earliest_start,
                        "end_time": latest_end,
                        "channel_id": channel_id,
                        "pii_type": pii_type,
                        "matched_text": match.group()
                    })
                    
    return redaction_regions

The algorithm aligns regex character positions with the words array to guarantee sub-second timestamp accuracy. You must handle missing words arrays gracefully, as some transcription providers return only raw text.

Step 3: Construct Redaction Region Payloads with Precise Timestamps

The Media API expects redaction requests in a specific JSON structure. You must filter out overlapping regions and group them by channel_id if your compliance policy requires it. The payload format requires start_time, end_time, and channel_id per region.

def build_redaction_payload(regions: List[Dict[str, str]]) -> Dict[str, Any]:
    # Remove overlapping regions by keeping the earliest start and latest end per channel
    cleaned_regions = []
    seen_channels = set()
    
    for region in regions:
        key = f"{region['channel_id']}_{region['start_time']}"
        if key not in seen_channels:
            seen_channels.add(key)
            cleaned_regions.append({
                "start_time": region["start_time"],
                "end_time": region["end_time"],
                "channel_id": region["channel_id"]
            })
            
    return {"regions": cleaned_regions}

Genesys Cloud validates timestamp formats strictly. You must ensure all timestamps match the HH:MM:SS.mmm pattern. The SDK does not format these values automatically.

Step 4: Submit Batched Redaction Requests and Handle Webhooks

The Media API accepts a single POST request containing multiple regions. You will use ApiMediaApi.post_media_records_record_id_redactions to submit the batch. For webhook monitoring, you will expose a FastAPI endpoint that receives transcription completion events and triggers the redaction pipeline.

from platform_sdk.api.media_api import ApiMediaApi
from platform_sdk.rest import ApiException
import httpx

def submit_redaction_batch(
    media_api: ApiMediaApi,
    recording_id: str,
    payload: Dict[str, Any]
) -> Dict[str, Any]:
    try:
        response = media_api.post_media_records_record_id_redactions(
            record_id=recording_id,
            body=payload
        )
        return {
            "status": "SUBMITTED",
            "redaction_id": response.id,
            "recording_id": recording_id,
            "regions_count": len(payload.get("regions", []))
        }
    except ApiException as e:
        if e.status == 429:
            retry_after = int(e.headers.get("Retry-After", 5))
            time.sleep(retry_after)
            return submit_redaction_batch(media_api, recording_id, payload)
        raise

The webhook handler receives payloads from Genesys Cloud platform events. You must validate the signature header in production, but this example focuses on payload parsing and pipeline triggering.

from fastapi import FastAPI, Request
from pydantic import BaseModel
import json

app = FastAPI()

class WebhookPayload(BaseModel):
    transcriptionId: str
    recordingId: str
    eventType: str

@app.post("/webhook/transcription-complete")
async def handle_transcription_webhook(request: Request):
    payload = await request.json()
    webhook = WebhookPayload(**payload)
    
    # Trigger redaction pipeline
    if webhook.eventType == "TRANSCRIPTION_COMPLETED":
        await run_redaction_pipeline(webhook.transcriptionId, webhook.recordingId)
        
    return {"status": "accepted"}

Step 5: Generate Compliance Audit Logs

Compliance frameworks require immutable logs of redaction actions. You will configure Python’s logging module with a JSON formatter to write structured records containing transcription IDs, redaction counts, API response codes, and timestamps.

import logging
import json
from datetime import datetime, timezone

class JsonAuditFormatter(logging.Formatter):
    def format(self, record: logging.LogRecord) -> str:
        log_data = {
            "timestamp": datetime.now(timezone.utc).isoformat(),
            "level": record.levelname,
            "message": record.getMessage(),
            "transcription_id": getattr(record, "transcription_id", None),
            "recording_id": getattr(record, "recording_id", None),
            "redaction_id": getattr(record, "redaction_id", None),
            "regions_redacted": getattr(record, "regions_redacted", None),
            "api_status": getattr(record, "api_status", None)
        }
        return json.dumps(log_data)

def setup_audit_logger(log_file: str = "redaction_audit.log") -> logging.Logger:
    logger = logging.getLogger("pii_redaction_audit")
    logger.setLevel(logging.INFO)
    
    handler = logging.FileHandler(log_file)
    formatter = JsonAuditFormatter()
    handler.setFormatter(formatter)
    logger.addHandler(handler)
    
    return logger

You attach dynamic attributes to log records using logging.LoggerAdapter or by passing extra dictionaries to the log() method. The JSON output enables direct ingestion into SIEM platforms.

Complete Working Example

import os
import time
import asyncio
import logging
from typing import Dict, Any, List
from platform_sdk import PureCloudPlatformClientV2, OAuthClientCredentialsProvider
from platform_sdk.api.recordings_transcripts_api import ApiRecordingsTranscriptsApi
from platform_sdk.api.media_api import ApiMediaApi
from platform_sdk.rest import ApiException
from fastapi import FastAPI, Request
from pydantic import BaseModel
import re

# --- Configuration & Logging ---
class JsonAuditFormatter(logging.Formatter):
    def format(self, record: logging.LogRecord) -> str:
        log_data = {
            "timestamp": datetime.now(timezone.utc).isoformat(),
            "level": record.levelname,
            "message": record.getMessage(),
            "transcription_id": getattr(record, "transcription_id", None),
            "recording_id": getattr(record, "recording_id", None),
            "redaction_id": getattr(record, "redaction_id", None),
            "regions_redacted": getattr(record, "regions_redacted", None),
            "api_status": getattr(record, "api_status", None)
        }
        return json.dumps(log_data)

from datetime import datetime, timezone
import json

audit_logger = logging.getLogger("pii_redaction_audit")
audit_logger.setLevel(logging.INFO)
file_handler = logging.FileHandler("redaction_audit.log")
file_handler.setFormatter(JsonAuditFormatter())
audit_logger.addHandler(file_handler)

# --- PII Detection Engine ---
PII_PATTERNS = {
    "SSN": r"\b\d{3}-\d{2}-\d{4}\b",
    "CREDIT_CARD": r"\b(?:\d[ -]*?){13,16}\b",
    "EMAIL": r"\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,}\b",
    "PHONE": r"\b\d{3}[-.]?\d{3}[-.]?\d{4}\b"
}

def detect_pii_in_transcript(segments: List[Any]) -> List[Dict[str, str]]:
    redaction_regions: List[Dict[str, str]] = []
    for segment in segments or []:
        if not segment.words:
            continue
        channel_id = segment.channel_id
        segment_text = segment.text or ""
        for pii_type, pattern in PII_PATTERNS.items():
            matches = list(re.finditer(pattern, segment_text, re.IGNORECASE))
            for match in matches:
                match_start_idx = match.start()
                match_end_idx = match.end()
                char_offset = 0
                matched_words = []
                for word in segment.words:
                    word_text = word.text or ""
                    word_start = char_offset
                    word_end = char_offset + len(word_text)
                    if word_end > match_start_idx and word_start < match_end_idx:
                        matched_words.append(word)
                    char_offset = word_end + 1
                if matched_words:
                    earliest_start = min(w.start_time for w in matched_words)
                    latest_end = max(w.end_time for w in matched_words)
                    redaction_regions.append({
                        "start_time": earliest_start,
                        "end_time": latest_end,
                        "channel_id": channel_id,
                        "pii_type": pii_type,
                        "matched_text": match.group()
                    })
    return redaction_regions

def build_redaction_payload(regions: List[Dict[str, str]]) -> Dict[str, Any]:
    cleaned_regions = []
    seen_keys = set()
    for region in regions:
        key = f"{region['channel_id']}_{region['start_time']}"
        if key not in seen_keys:
            seen_keys.add(key)
            cleaned_regions.append({
                "start_time": region["start_time"],
                "end_time": region["end_time"],
                "channel_id": region["channel_id"]
            })
    return {"regions": cleaned_regions}

# --- Core Pipeline ---
async def run_redaction_pipeline(transcription_id: str, recording_id: str):
    client = PureCloudPlatformClientV2()
    client.set_base_url(os.environ.get("GENESYS_BASE_URL", "https://api.mypurecloud.com"))
    provider = OAuthClientCredentialsProvider(
        client_id=os.environ["GENESYS_CLIENT_ID"],
        client_secret=os.environ["GENESYS_CLIENT_SECRET"]
    )
    client.set_auth_provider(provider)
    
    transcripts_api = ApiRecordingsTranscriptsApi(client)
    media_api = ApiMediaApi(client)
    
    # Poll for completion
    for attempt in range(15):
        try:
            response = transcripts_api.get_recordings_transcripts_transcription_id(transcription_id=transcription_id)
            if response.status == "COMPLETED":
                break
            elif response.status == "FAILED":
                audit_logger.error("Transcription failed", extra={
                    "transcription_id": transcription_id, "api_status": "FAILED"
                })
                return
            time.sleep(10)
        except ApiException as e:
            if e.status == 429:
                time.sleep(int(e.headers.get("Retry-After", 10)))
                continue
            raise
            
    # Detect PII
    regions = detect_pii_in_transcript(response.segments)
    if not regions:
        audit_logger.info("No PII detected", extra={"transcription_id": transcription_id})
        return
        
    payload = build_redaction_payload(regions)
    
    # Submit redaction
    try:
        redaction_resp = media_api.post_media_records_record_id_redactions(
            record_id=recording_id, body=payload
        )
        audit_logger.info("Redaction submitted successfully", extra={
            "transcription_id": transcription_id,
            "recording_id": recording_id,
            "redaction_id": redaction_resp.id,
            "regions_redacted": len(payload["regions"]),
            "api_status": 200
        })
    except ApiException as e:
        audit_logger.error("Redaction submission failed", extra={
            "transcription_id": transcription_id,
            "recording_id": recording_id,
            "api_status": e.status
        })
        raise

# --- Webhook Server ---
app = FastAPI()

class WebhookPayload(BaseModel):
    transcriptionId: str
    recordingId: str
    eventType: str

@app.post("/webhook/transcription-complete")
async def handle_transcription_webhook(request: Request):
    payload = await request.json()
    webhook = WebhookPayload(**payload)
    if webhook.eventType == "TRANSCRIPTION_COMPLETED":
        await run_redaction_pipeline(webhook.transcriptionId, webhook.recordingId)
    return {"status": "accepted"}

if __name__ == "__main__":
    import uvicorn
    uvicorn.run(app, host="0.0.0.0", port=8000)

This script runs a standalone webhook server that accepts transcription completion events, polls the Transcription API as a fallback, detects PII using the regex engine, constructs timestamp boundaries, submits batched redaction requests, and writes structured audit logs. You must set the environment variables before execution.

Common Errors & Debugging

Error: 401 Unauthorized or 403 Forbidden

• Cause: The OAuth client lacks the required scopes, or the token expired during a long polling cycle.
• Fix: Verify that your client credentials include recordings:view and media:redact. The SDK automatically refreshes tokens, but you must restart the script if you rotate secrets. Check the Authorization header in failed requests using httpx interceptors or SDK logging.

Error: 429 Too Many Requests

• Cause: Genesys Cloud enforces per-tenant and per-endpoint rate limits. Polling transcription status too aggressively triggers cascading blocks.
• Fix: The example includes Retry-After header parsing. You must respect the returned delay. Implement exponential backoff for production workloads. Never poll faster than every 10 seconds for transcription status.

Error: 400 Bad Request on Media API

• Cause: Invalid timestamp format, overlapping regions, or missing channel_id. The Media API rejects payloads where end_time is less than start_time.
• Fix: Validate all timestamps match HH:MM:SS.mmm. Use the build_redaction_payload function to deduplicate overlapping boundaries. Log the exact JSON payload before submission to verify structure.

Error: 500 Internal Server Error or 503 Service Unavailable

• Cause: The Media redaction service is processing heavy workloads or the recording file is corrupted.
• Fix: Implement a retry queue with circuit breakers. The redaction job runs asynchronously in Genesys Cloud. You can poll GET /api/v2/media/records/{recordId}/redactions/{redactionId} to track completion status. Do not retry immediately; wait 30 to 60 seconds between attempts.

Official References

• Transcription API Documentation
• Media Redaction API Documentation
• Python SDK Reference
• Webhook Event Types
• OAuth Scopes Reference