Implementing Automated PII and PCI Redaction Pipelines for Historical Call Recordings

How-To-Articles
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Implementing Automated PII and PCI Redaction Pipelines for Historical Call Recordings

What This Guide Covers

You are building an automated retroactive redaction pipeline that scans your historical Genesys Cloud recording library - potentially millions of recordings accumulated over years - identifies audio segments containing spoken PII (Social Security Numbers, dates of birth, full names combined with account numbers) and PCI data (payment card numbers, CVV codes, expiry dates), and replaces those segments with silence or a tone before the recordings are processed by speech analytics, accessed by external auditors, or surfaced in response to Right of Access requests. When complete, your speech analytics vendor receives only de-identified audio, your compliance team can produce recordings for auditors without manual review, and your PCI DSS Requirement 3.3 audit documentation is supported by automated evidence of redaction.

Prerequisites, Roles & Licensing

  • Genesys Cloud: Any CX tier with recording access
  • Permissions required:
    • Recording > Recording > View
    • Recording > Recording > Export
  • Processing infrastructure: AWS (Transcribe + Lambda + S3 + DynamoDB) or Google Cloud (Speech-to-Text + Cloud Functions + GCS)
  • Scale: Processing 100,000 recordings (≈ 5 minutes average) at AWS Transcribe rates costs approximately $60,000. Process in priority tiers - billing/payment queues first.
  • Regulatory context: PCI DSS v4.0 Requirement 3.3 - CHD must not be stored unprotected; GDPR Article 25 - data minimization; HIPAA 45 CFR 164.312(a)(2)(iv) - encryption of PHI in audio

The Implementation Deep-Dive

1. Risk-Based Processing Order

With a large historical corpus, you cannot process everything at once. Prioritize by risk:

Priority 1 (Process immediately):

  • Payment/billing queue recordings (highest PCI exposure)
  • Calls where agents asked callers to “read your card number” (detectable via Genesys speech analytics topic: “card number verbalization”)
  • Calls where IVR secure pause was NOT active (DTMF payment capture bypassed)

Priority 2 (Process within 30 days):

  • Account management queues (SSN verification calls)
  • Insurance/healthcare queues (DOB, member ID)
  • Calls longer than 5 minutes (longer calls have more surface area for PII)

Priority 3 (Process within 90 days):

  • General customer service queues
  • Sales queues (occasional credit check and SSN collection)

Priority 4 (Sample only):

  • IVR-only calls (no agent conversation)
  • Internal support calls

2. Recording Download and Preparation

import requests
import boto3
import json
import uuid

s3 = boto3.client("s3")
STAGING_BUCKET = "recording-redaction-staging"

def download_and_stage_recording(
    conversation_id: str,
    recording_id: str,
    priority_tier: int,
    access_token: str,
    base_url: str
) -> dict:
    """
    Download a recording from Genesys Cloud and stage it in S3 for processing.
    Returns staging metadata.
    """
    headers = {"Authorization": f"Bearer {access_token}"}
    
    # Get recording download URL
    meta_resp = requests.get(
        f"{base_url}/api/v2/conversations/{conversation_id}/recordings/{recording_id}",
        headers=headers,
        params={"formatId": "WAV"}
    )
    meta_resp.raise_for_status()
    meta = meta_resp.json()
    
    download_url = None
    for media in meta.get("mediaUris", []):
        if media.get("mediaType") == "AUDIO":
            download_url = media.get("uri")
            break
    
    if not download_url:
        raise ValueError(f"No audio URI for recording {recording_id}")
    
    # Download audio bytes
    audio_resp = requests.get(download_url, timeout=120)
    audio_resp.raise_for_status()
    
    # Stage in S3 with priority tag
    job_id = str(uuid.uuid4())
    object_key = f"pending/{priority_tier}/{job_id}.wav"
    
    s3.put_object(
        Bucket=STAGING_BUCKET,
        Key=object_key,
        Body=audio_resp.content,
        Tagging=f"Priority={priority_tier}&ConversationId={conversation_id}&RecordingId={recording_id}",
        Metadata={
            "conversation-id": conversation_id,
            "recording-id": recording_id,
            "job-id": job_id,
            "priority-tier": str(priority_tier)
        }
    )
    
    return {
        "jobId": job_id,
        "s3Key": object_key,
        "s3Uri": f"s3://{STAGING_BUCKET}/{object_key}",
        "conversationId": conversation_id,
        "recordingId": recording_id
    }

3. Three-Layer PII/PCI Detection

Layer 1: AWS Transcribe with PII Identification

import boto3
import time

transcribe = boto3.client("transcribe", region_name="us-east-1")

def transcribe_with_pii_detection(s3_uri: str, job_name: str) -> list[dict]:
    """
    Run transcription with PCI and PII entity detection.
    Returns list of timed PII segments.
    """
    transcribe.start_transcription_job(
        TranscriptionJobName=job_name,
        Media={"MediaFileUri": s3_uri},
        MediaFormat="wav",
        LanguageCode="en-US",
        ChannelIdentification=True,  # Separate agent and customer channels
        ContentRedaction={
            "RedactionType": "PII",
            "RedactionOutput": "redacted_and_unredacted",
            "PiiEntityTypes": [
                "CREDIT_DEBIT_NUMBER",    # Payment card numbers
                "CREDIT_DEBIT_CVV",       # CVV codes
                "CREDIT_DEBIT_EXPIRY",    # Expiry dates
                "SSN",                    # Social Security Numbers
                "BANK_ACCOUNT_NUMBER",    # Bank account numbers
                "DATE_OF_BIRTH",          # Dates of birth
                "PHONE",                  # Phone numbers (contextual)
                "NAME",                   # Full names
                "ADDRESS"                 # Postal addresses
            ]
        }
    )
    
    # Poll for completion
    max_wait = 7200  # 2 hours max
    start = time.time()
    
    while (time.time() - start) < max_wait:
        job = transcribe.get_transcription_job(TranscriptionJobName=job_name)
        status = job["TranscriptionJob"]["TranscriptionJobStatus"]
        
        if status == "COMPLETED":
            return extract_pii_timestamps_from_job(job)
        elif status == "FAILED":
            raise RuntimeError(f"Transcription failed: {job['TranscriptionJob'].get('FailureReason')}")
        
        time.sleep(30)
    
    raise TimeoutError(f"Transcription job {job_name} timed out after 2 hours")

def extract_pii_timestamps_from_job(job: dict) -> list[dict]:
    """Download full transcript and extract PII segment timestamps."""
    transcript_uri = job["TranscriptionJob"]["Transcript"]["TranscriptFileUri"]
    resp = requests.get(transcript_uri)
    transcript_data = resp.json()
    
    pii_segments = []
    
    for item in transcript_data.get("results", {}).get("items", []):
        if item.get("type") != "pronunciation":
            continue
        
        pii_result = item.get("pii_detection_result", {})
        if pii_result.get("redacted", False):
            pii_segments.append({
                "startTime": float(item.get("start_time", 0)) - 0.1,
                "endTime": float(item.get("end_time", 0)) + 0.2,
                "piiTypes": pii_result.get("entity_types", ["UNKNOWN"]),
                "channel": item.get("channel_label", "ch_0")
            })
    
    return pii_segments

Layer 2: Spoken PAN Detection (16-digit card number sequences)

def detect_spoken_card_numbers_from_transcript(transcript_items: list[dict]) -> list[dict]:
    """Detect 15-16 consecutive spoken digit words (undetected by entity recognition)."""
    NUMBER_WORDS = {
        "zero": True, "oh": True, "one": True, "two": True, "three": True,
        "four": True, "five": True, "six": True, "seven": True, "eight": True, "nine": True
    }
    
    # Extract word items with timestamps
    words = [
        {
            "word": item["alternatives"][0]["content"].lower(),
            "start": float(item.get("start_time", 0)),
            "end": float(item.get("end_time", 0))
        }
        for item in transcript_items
        if item.get("type") == "pronunciation"
    ]
    
    segments = []
    i = 0
    while i < len(words):
        if words[i]["word"] in NUMBER_WORDS:
            j = i + 1
            while j < len(words) and words[j]["word"] in NUMBER_WORDS and (j - i) < 20:
                j += 1
            
            # 15+ consecutive digit words = likely PAN
            if j - i >= 15:
                segments.append({
                    "startTime": words[i]["start"] - 0.2,
                    "endTime": words[j - 1]["end"] + 0.3,
                    "piiTypes": ["SPOKEN_PAN"],
                    "channel": "all"
                })
            i = j
        else:
            i += 1
    
    return segments

Layer 3: Regex on Transcript Text for Custom Identifiers

import re

CUSTOM_PII_PATTERNS = {
    "EMPLOYEE_ID": r"\b[Ee][Mm][Pp][-\s]?\d{6,8}\b",
    "POLICY_NUMBER": r"\bPOL[-\s]?\d{8,12}\b",
    "MEDICAL_RECORD": r"\b[Mm][Rr][Nn][-:\s]?\d{6,10}\b",
    "ROUTING_NUMBER": r"\b0[0-9]{8}\b"  # ABA routing numbers start with 0
}

def find_regex_pii_in_words(words: list[dict]) -> list[dict]:
    segments = []
    for i in range(len(words)):
        # Build 6-word window for context
        window = words[i:i+6]
        window_text = " ".join(w["word"] for w in window)
        
        for pii_type, pattern in CUSTOM_PII_PATTERNS.items():
            if re.search(pattern, window_text, re.IGNORECASE):
                segments.append({
                    "startTime": window[0]["start"] - 0.1,
                    "endTime": window[-1]["end"] + 0.2,
                    "piiTypes": [pii_type],
                    "channel": "all"
                })
    return segments

4. Audio Masking with Silence or Tone

from pydub import AudioSegment
import io

def apply_redaction_to_audio(
    audio_bytes: bytes,
    pii_segments: list[dict],
    audio_format: str = "wav",
    mask_tone_hz: int = 0  # 0 = silence; 1000 = 1kHz tone
) -> bytes:
    """Replace PII segments with silence or tone."""
    audio = AudioSegment.from_file(io.BytesIO(audio_bytes), format=audio_format)
    total_ms = len(audio)
    
    # Merge overlapping segments
    merged = merge_pii_segments(pii_segments, buffer_ms=100)
    
    # Process in reverse order (preserves timestamp accuracy)
    for seg in sorted(merged, key=lambda x: x["startTime"], reverse=True):
        start_ms = max(0, int(seg["startTime"] * 1000))
        end_ms = min(total_ms, int(seg["endTime"] * 1000))
        duration_ms = end_ms - start_ms
        
        if duration_ms <= 0:
            continue
        
        if mask_tone_hz > 0:
            import numpy as np
            sr = audio.frame_rate
            t = np.linspace(0, duration_ms / 1000, int(sr * duration_ms / 1000))
            tone_data = (np.sin(2 * np.pi * mask_tone_hz * t) * 32767 * 0.3).astype(np.int16)
            replacement = AudioSegment(
                tone_data.tobytes(), frame_rate=sr, sample_width=2, channels=1
            )
            if audio.channels == 2:
                replacement = AudioSegment.from_mono_audiosegments(replacement, replacement)
        else:
            replacement = AudioSegment.silent(duration=duration_ms, frame_rate=audio.frame_rate)
        
        audio = audio[:start_ms] + replacement + audio[end_ms:]
    
    output = io.BytesIO()
    audio.export(output, format="wav")
    return output.getvalue()

def merge_pii_segments(segments: list[dict], buffer_ms: int = 100) -> list[dict]:
    if not segments:
        return []
    sorted_segs = sorted(segments, key=lambda x: x["startTime"])
    merged = [sorted_segs[0].copy()]
    for seg in sorted_segs[1:]:
        last = merged[-1]
        if seg["startTime"] * 1000 <= last["endTime"] * 1000 + buffer_ms:
            last["endTime"] = max(last["endTime"], seg["endTime"])
            last["piiTypes"] = list(set(last["piiTypes"] + seg["piiTypes"]))
        else:
            merged.append(seg.copy())
    return merged

5. Redaction Registry and Audit Trail

import boto3
from datetime import datetime, timedelta
import hashlib

dynamodb = boto3.resource("dynamodb").Table("redaction-registry")

def register_redaction_result(
    conversation_id: str,
    recording_id: str,
    original_s3_key: str,
    redacted_s3_key: str,
    pii_segments: list[dict],
    audio_bytes: bytes
):
    content_hash = hashlib.sha256(audio_bytes).hexdigest()
    
    dynamodb.put_item(Item={
        "recordingId": recording_id,
        "conversationId": conversation_id,
        "processedAt": datetime.utcnow().isoformat() + "Z",
        "originalS3Key": original_s3_key,
        "redactedS3Key": redacted_s3_key,
        "piiSegmentsFound": len(pii_segments),
        "piiTypesFound": list(set(t for seg in pii_segments for t in seg["piiTypes"])),
        "totalRedactedMs": int(sum(
            (seg["endTime"] - seg["startTime"]) * 1000 for seg in pii_segments
        )),
        "redactedAudioSha256": content_hash,
        "pipelineVersion": "2.1.0",
        "ttl": int((datetime.utcnow() + timedelta(days=2555)).timestamp())  # 7-year retention
    })

Validation, Edge Cases & Troubleshooting

Edge Case 1: Dual-Channel Recordings with PII on Only One Channel

When the customer reads their card number, it appears only on the customer channel (ch_0). When the agent reads it back (“I have your card ending in…”), it appears only on the agent channel (ch_1). Channel-aware masking (enabled by ChannelIdentification=True in Transcribe) allows you to mask only the affected channel rather than silencing both channels, preserving more of the QA-reviewable agent audio.

Edge Case 2: Transcription Failure on Low-Quality Audio

8kHz G.711 recordings from PSTN calls have lower audio quality than WebRTC/Opus. AWS Transcribe’s accuracy drops on G.711 audio. Run a pre-processing step that upsamples 8kHz recordings to 16kHz using SoX before submitting to Transcribe - Transcribe models are optimized for 16kHz audio, and upsampling produces measurably better transcription accuracy even though no new frequency content is added.

Edge Case 3: Processing Queue Backpressure at Scale

If you submit 50,000 transcription jobs simultaneously, AWS Transcribe queues the excess. Each region has a default concurrency limit (typically 250 concurrent jobs). Implement a controlled submission rate: use a DynamoDB-backed state machine that submits jobs in batches of 200, waits for 80% completion before submitting the next batch, and monitors the SQS DLQ for failed jobs requiring resubmission.

Edge Case 4: Redacted Recording Storage Costs vs. Original

You now have two copies of each recording: original (legal hold, restricted access) and redacted (analytics, auditor access). For a 5TB corpus this doubles storage costs. Apply aggressive lifecycle tiering: original recordings → S3 Glacier after 30 days → S3 Glacier Deep Archive after 1 year. Redacted recordings → S3 Standard for 90 days → S3 Glacier. The redacted copies are frequently accessed for analytics; the originals are accessed only for legal proceedings.

Official References