Implementing Automated GDPR “Right to Erasure” Workflows via the GDPR API
What This Guide Covers
You are building an automated pipeline that processes GDPR Article 17 “Right to Erasure” (right to be forgotten) requests by invoking the Genesys Cloud GDPR API to locate, review, and delete a data subject’s personal data across conversations, recordings, and system records - with a documented audit trail that satisfies regulatory accountability requirements. When complete, your Data Protection Officer can trigger erasure for a verified subject and receive a confirmation report within the 30-day regulatory deadline, with zero manual platform intervention from engineering.
Prerequisites, Roles & Licensing
- Licensing: Any Genesys Cloud CX tier (GDPR API is available on all tiers for EU and UK-region tenants; available on request for other regions where GDPR applies)
- Permissions required (service account):
GDPR > Subject > Add(to create GDPR subject records)GDPR > Request > Add(to submit deletion requests)GDPR > Request > View(to monitor request status)Recording > Recording > Delete(if manual recording deletion is required for edge cases)
- OAuth scopes:
gdpr - Legal prerequisites:
- A verified Data Subject Access Request (DSAR) intake process - the API deletes data; you must verify identity before triggering deletion
- Legal basis review - confirm the erasure is not blocked by a legal hold (see Designing a Compliant Recording Retrieval Pipeline for Legal Discovery and Litigation Hold), a statutory retention requirement, or a legitimate interest that overrides erasure
- External dependencies: A DSAR management tool (OneTrust, TrustArc, or a custom ticketing system) to receive and triage incoming erasure requests before API submission
The Implementation Deep-Dive
1. Understanding the Genesys Cloud GDPR Data Model
The Genesys Cloud GDPR API operates on two primary objects:
GDPR Subject: Represents the data subject (the individual whose data you are deleting). A subject record stores the identifying information used to locate their data within Genesys Cloud.
GDPR Request: Represents a specific action (delete, export) applied to a subject. A deletion request triggers the platform to locate all personal data associated with the subject and queue it for removal.
Genesys Cloud identifies a data subject by one or more of:
- Phone number (ANI/DNIS): Locates conversations where this number appeared as calling or called party
- Email address: Locates digital interactions (email channel)
- Name: Fuzzy-matches against conversation metadata and recording transcripts (less reliable - use in combination with other identifiers)
- User ID: For agents who are also data subjects (former employees)
The GDPR API searches across: conversation records, recording metadata, audio recordings, chat transcripts, voicemail transcriptions, and participant data attributes.
The Trap - GDPR API does not delete CRM or external system data: The Genesys Cloud GDPR API erases data within Genesys Cloud’s systems only. Any data replicated to Salesforce, ServiceNow, your data warehouse, or a third-party analytics tool is not touched. Your erasure workflow must include steps to notify and trigger deletion in each downstream system. Document all downstream systems in your DSAR response procedure.
2. Creating the GDPR Subject Record
Before submitting a deletion request, create a GDPR Subject record:
import requests
import json
def create_gdpr_subject(
first_name: str,
last_name: str,
email: str,
phone_numbers: list[str],
access_token: str,
base_url: str = "https://api.mypurecloud.com"
) -> dict:
headers = {
"Authorization": f"Bearer {access_token}",
"Content-Type": "application/json"
}
payload = {
"name": {
"firstName": first_name,
"lastName": last_name
},
"addresses": [
{"name": email, "type": "EMAIL"}
] + [
{"name": phone, "type": "PHONE"} for phone in phone_numbers
]
}
resp = requests.post(
f"{base_url}/api/v2/gdpr/subjects",
headers=headers,
json=payload
)
resp.raise_for_status()
return resp.json()
Response includes the subjectId - store this in your DSAR ticket as the linkage between the regulatory request and the platform action.
Identity verification before subject creation: The API has no identity verification - it accepts whatever identifiers you provide. Your verification process must occur outside the API (matching government ID against account records, requiring authentication via existing account credentials) before the system account calls this endpoint. Implement a mandatory approval gate in your DSAR workflow: no API call is made until a DPO or designated Privacy Officer has signed off on the verified request.
3. Searching for Personal Data (Pre-Deletion Review)
Before deleting, you can request a data export to understand what records exist for the subject. This is also required when responding to a Subject Access Request (Article 15) prior to erasure:
def submit_gdpr_export(subject_id: str, access_token: str, base_url: str) -> dict:
headers = {
"Authorization": f"Bearer {access_token}",
"Content-Type": "application/json"
}
payload = {
"operations": [
{
"type": "GDPR_REQUEST",
"requestType": "GDPR_EXPORT",
"gdprSubjectId": subject_id
}
]
}
resp = requests.post(
f"{base_url}/api/v2/gdpr/requests",
headers=headers,
json=payload
)
resp.raise_for_status()
return resp.json()
Poll the request status:
def poll_gdpr_request(request_id: str, access_token: str, base_url: str) -> dict:
headers = {"Authorization": f"Bearer {access_token}"}
resp = requests.get(f"{base_url}/api/v2/gdpr/requests/{request_id}", headers=headers)
resp.raise_for_status()
return resp.json()
# Poll until complete (max 30 minutes for large data sets)
import time
def wait_for_completion(request_id: str, access_token: str, base_url: str, timeout_seconds: int = 1800) -> dict:
start = time.time()
while (time.time() - start) < timeout_seconds:
result = poll_gdpr_request(request_id, access_token, base_url)
status = result.get("status")
if status == "COMPLETE":
return result
elif status == "ERROR":
raise RuntimeError(f"GDPR request failed: {result.get('errorCode')} - {result.get('errorMessage')}")
time.sleep(30) # Poll every 30 seconds
raise TimeoutError(f"GDPR request {request_id} did not complete within {timeout_seconds} seconds")
The export result contains a list of matched records (conversation IDs, recording IDs, timestamp ranges) that will be deleted if the deletion request is submitted. Review this list before proceeding.
4. Submitting the Deletion Request
After reviewing the export (or as part of an automated pipeline for straightforward cases), submit the deletion:
def submit_gdpr_deletion(
subject_id: str,
delete_recordings: bool,
access_token: str,
base_url: str
) -> dict:
headers = {
"Authorization": f"Bearer {access_token}",
"Content-Type": "application/json"
}
payload = {
"operations": [
{
"type": "GDPR_REQUEST",
"requestType": "GDPR_DELETE",
"gdprSubjectId": subject_id,
"deleteConfirmed": True
}
],
# Explicitly include recordings in the deletion scope
"deleteRecordings": delete_recordings
}
resp = requests.post(
f"{base_url}/api/v2/gdpr/requests",
headers=headers,
json=payload
)
resp.raise_for_status()
deletion_request = resp.json()
return {
"requestId": deletion_request["id"],
"status": deletion_request["status"],
"subjectId": subject_id,
"submittedAt": deletion_request["createdDate"]
}
The deleteRecordings: true flag: By default, GDPR deletion removes conversation metadata and transcript text but may not delete the audio recording binary. Set deleteRecordings: true to include recording audio files in the deletion scope. For voice-heavy contact centers where recordings are the primary PII risk, this flag is mandatory.
The Trap - treating deletion as synchronous: Genesys Cloud GDPR deletion is asynchronous. The API returns a request ID immediately, but the actual deletion occurs as a background process that can take hours for subjects with large conversation histories (hundreds of calls, years of records). Do not send the compliance confirmation to the data subject until you have polled the request to COMPLETE status. Build your workflow to wait for completion before issuing the regulatory response letter.
5. Handling Recordings Under Legal Hold
Before submitting a deletion request, check whether any recordings are subject to a litigation hold. Deleting a record under legal hold is a potential spoliation of evidence - a serious legal liability.
def check_legal_hold_before_erasure(subject_conversations: list[str], legal_hold_register: dict) -> list[str]:
"""
Returns a list of conversation IDs that cannot be deleted due to active legal holds.
legal_hold_register: dict mapping conversationId → hold expiry date
"""
blocked = []
for conv_id in subject_conversations:
if conv_id in legal_hold_register:
hold_expiry = legal_hold_register[conv_id]
blocked.append({
"conversationId": conv_id,
"holdExpiry": hold_expiry,
"reason": "Active litigation hold - cannot delete until hold expires"
})
return blocked
If held records are found, the GDPR erasure must be deferred for those records until the legal hold expires. Document this in your response to the data subject: “We have located records subject to a legal hold that overrides the right to erasure under Article 17(3)(e). These records will be deleted upon expiry of the legal hold on [date].”
This is not a GDPR violation - Article 17(3) explicitly allows retention for the establishment, exercise, or defence of legal claims.
6. Documenting the Erasure for Accountability (Article 5(2))
GDPR Article 5(2) requires controllers to be able to demonstrate compliance with GDPR principles (“accountability”). Your erasure workflow must generate a structured compliance record:
import json
from datetime import datetime
def generate_erasure_compliance_record(
dsar_ticket_id: str,
subject_id: str,
deletion_request_id: str,
deletion_result: dict,
held_records: list,
downstream_systems_notified: list
) -> dict:
record = {
"erasureComplianceRecord": {
"dsarTicketId": dsar_ticket_id,
"gdprSubjectId": subject_id,
"requestSubmittedAt": deletion_result.get("submittedAt"),
"requestCompletedAt": datetime.utcnow().isoformat() + "Z",
"genesysCloudDeletionRequestId": deletion_request_id,
"deletionStatus": deletion_result.get("status"),
"recordingsDeleted": deletion_result.get("deleteRecordings", False),
"recordsWithheldForLegalHold": held_records,
"downstreamSystemsNotified": downstream_systems_notified,
"compliantWithDeadline": True, # Set based on request date vs. completion date
"processorAccountability": {
"processedBy": "automated-gdpr-pipeline-v2",
"authorizedBy": "DPO",
"legalBasisForRetainedRecords": "Article 17(3)(e) - Legal claims defence"
}
}
}
# Store in your immutable compliance archive (S3 with Object Lock, Azure Immutable Blob Storage)
filename = f"gdpr_erasure_{dsar_ticket_id}_{datetime.utcnow().strftime('%Y%m%d')}.json"
with open(f"/compliance_archive/{filename}", "w") as f:
json.dump(record, f, indent=2)
return record
Store these records with immutable storage (WORM - Write Once Read Many). Compliance records must themselves not be deleteable - they are the proof that the erasure occurred, and are exempt from erasure requests under Article 17(3)(e).
Validation, Edge Cases & Troubleshooting
Edge Case 1: Data Subject is a Former Agent
If the erasure request is for a former employee who was also a Genesys Cloud agent (not just a customer), their agent user account contains PII (name, email, profile photo). Use the Users API to deactivate and anonymize the account (see Building Automated Offboarding Scripts for Agent Termination using the Users API) in addition to the GDPR API for conversation records. The GDPR API’s subject matching by email will cover conversations where the agent participated, but the user profile must be handled separately.
Edge Case 2: Subject Phone Number Reused by a New Customer
Telco number recycling means a phone number that belonged to Subject A in 2022 may now belong to unrelated Subject B in 2024. If you submit a GDPR deletion using Subject A’s phone number, the GDPR API will match all conversations involving that number - including post-recycling calls from Subject B. Pre-filter the conversation export by date range (limit to the period the subject was a customer) before confirming deletion of conversation records.
Edge Case 3: Deletion Request Times Out (Large Data Volume)
For subjects with 10+ years of interaction history, the deletion job may exceed the API’s processing window and return an ERROR status with a timeout code. In this case, the request is partially processed - some records may be deleted and others not. Contact Genesys Cloud support to complete the deletion; document the partial completion and the support ticket reference in your compliance record. Do not resubmit the deletion without confirming which records were already deleted, to avoid double-processing errors.
Edge Case 4: Erasure Confirmation Letter Timing
The 30-day GDPR deadline runs from receipt of the verified request, not from API submission. If your intake process takes 5 days to verify the subject’s identity, the API must complete within 25 remaining days. Build your pipeline SLA with buffer: trigger the GDPR API within 3 business days of verification, leaving 27 days for the asynchronous job to complete and the confirmation letter to be issued.