Enforcing Genesys Cloud Call Recording Retention Policies with Python

What You Will Build

• A Python service that queries Genesys Cloud voice recordings, applies configurable retention rules, deletes expired media, and handles GDPR deletion requests.
• This tutorial uses the Genesys Cloud Platform API v2 endpoint /api/v2/recording and the official genesyscloud Python SDK.
• The code is written in Python 3.10+ using httpx, pydantic, tenacity, and the genesyscloud package.

Prerequisites

• OAuth 2.0 Client Credentials flow configured in Genesys Cloud Admin Console with scopes: recording:view, recording:delete, recording:export:view
• Genesys Cloud Python SDK v2.50.0 or later (pip install genesyscloud)
• Python 3.10+ runtime
• External dependencies: httpx, pydantic, tenacity, structlog
• Genesys Cloud environment URL (e.g., usw2.mypurecloud.com)
• Recording export configuration enabled for external object storage synchronization

Authentication Setup

Genesys Cloud requires OAuth 2.0 for all API calls. The Client Credentials flow grants machine-to-machine access. The following code fetches an access token using httpx and handles token expiration by caching the result.

import os
import time
import httpx
from typing import Optional

class GenesysAuthManager:
    def __init__(self, environment: str, client_id: str, client_secret: str):
        self.environment = environment
        self.client_id = client_id
        self.client_secret = client_secret
        self.token_url = f"https://{environment}/oauth/token"
        self._access_token: Optional[str] = None
        self._token_expiry: float = 0.0

    def get_access_token(self) -> str:
        if self._access_token and time.time() < self._token_expiry - 60:
            return self._access_token

        payload = {
            "grant_type": "client_credentials",
            "client_id": self.client_id,
            "client_secret": self.client_secret,
            "scope": "recording:view recording:delete recording:export:view"
        }

        response = httpx.post(self.token_url, data=payload)
        response.raise_for_status()

        token_data = response.json()
        self._access_token = token_data["access_token"]
        self._token_expiry = time.time() + token_data["expires_in"]
        return self._access_token

The OAuth endpoint returns a JWT valid for 3600 seconds. The manager caches the token and refreshes it only when expiration approaches. All subsequent SDK calls will reuse this token through the Configuration object.

Implementation

Step 1: Define Retention Policy Schema and Validation Rules

Retention enforcement requires a structured policy definition. The schema enforces maximum storage lifecycle limits, compliance jurisdiction flags, and format constraints. Pydantic validates incoming policy configurations before execution.

from pydantic import BaseModel, Field, field_validator
from datetime import timedelta
from typing import List, Optional

class RetainDirective(BaseModel):
    max_age_days: int = Field(ge=1, le=3650)
    compliance_jurisdictions: List[str] = Field(default_factory=list)
    allowed_formats: List[str] = Field(default=["mp4", "wav", "mp3"])
    require_checksum_verification: bool = True
    gdpr_override_expiry_offset_days: int = Field(default=7, ge=0)

    @field_validator("compliance_jurisdictions")
    @classmethod
    def validate_jurisdictions(cls, v: List[str]) -> List[str]:
        valid = ["GDPR", "CCPA", "HIPAA", "FINRA", "PCI-DSS"]
        for j in v:
            if j not in valid:
                raise ValueError(f"Unsupported jurisdiction: {j}")
        return v

class RecordingMatrix(BaseModel):
    recording_id: str
    status: str
    format: str
    size_bytes: int
    created_date: str
    checksum_md5: Optional[str] = None
    compliance_flags: List[str] = Field(default_factory=list)
    external_export_uri: Optional[str] = None

The RetainDirective enforces business rules. The max_age_days field prevents premature destruction. The compliance_jurisdictions list restricts deletion for protected data. The gdpr_override_expiry_offset_days field calculates a grace period after a subject request. The RecordingMatrix mirrors the actual Genesys Cloud /api/v2/recording response structure for type-safe processing.

Step 2: Query Recordings and Apply Pagination

The /api/v2/recording endpoint returns recordings in pages. The SDK handles authentication, but pagination requires explicit handling via the nextPageLink field. This step queries recordings within a retention window and applies initial status filters.

from genesyscloud import ApiClient, Configuration, RecordingApi
from typing import Generator

class RecordingQueryService:
    def __init__(self, auth: GenesysAuthManager):
        config = Configuration(
            host=f"https://{auth.environment}",
            access_token=auth.get_access_token
        )
        self.api_client = ApiClient(configuration=config)
        self.recording_api = RecordingApi(self.api_client)

    def fetch_recordings(self, days_back: int) -> Generator[RecordingMatrix, None, None]:
        import datetime
        cutoff = (datetime.datetime.now(datetime.timezone.utc) - datetime.timedelta(days=days_back)).isoformat()
        
        body = {
            "dateFrom": cutoff,
            "dateTo": datetime.datetime.now(datetime.timezone.utc).isoformat(),
            "status": ["complete", "processing"],
            "pageSize": 100
        }

        while True:
            response = self.recording_api.post_recording_query(body=body)
            for rec in response.entities or []:
                yield RecordingMatrix(
                    recording_id=rec.id,
                    status=rec.status,
                    format=rec.format,
                    size_bytes=rec.size or 0,
                    created_date=rec.created_date,
                    checksum_md5=rec.checksum_md5,
                    compliance_flags=rec.compliance_flags or [],
                    external_export_uri=rec.external_export_uri
                )

            if not response.next_page_link:
                break
            body = self._parse_next_page_link(response.next_page_link)

    def _parse_next_page_link(self, link: str) -> dict:
        query_params = {}
        if "?" in link:
            params = link.split("?")[1].split("&")
            for p in params:
                k, v = p.split("=")
                query_params[k] = v
        return query_params

The post_recording_query method maps to /api/v2/recording with a POST body. The SDK translates this to the correct HTTP call. The pagination loop continues until next_pageLink is null. Required scope: recording:view.

Step 3: Validate Recording State and Execute Atomic Deletion

Deletion requires strict validation. The enforcer checks recording status, format compatibility, jurisdiction constraints, and checksum integrity before issuing a DELETE request. Atomic deletion prevents partial state corruption.

import hashlib
import logging
from tenacity import retry, stop_after_attempt, wait_exponential, retry_if_exception_type
from httpx import HTTPStatusError

logger = logging.getLogger(__name__)

class RecordingEnforcer:
    def __init__(self, query_service: RecordingQueryService, policy: RetainDirective):
        self.query_service = query_service
        self.policy = policy
        self.recording_api = query_service.recording_api

    @retry(
        stop=stop_after_attempt(3),
        wait=wait_exponential(multiplier=1, min=2, max=10),
        retry=retry_if_exception_type((HTTPStatusError, Exception)),
        reraise=True
    )
    def delete_recording(self, recording_id: str) -> bool:
        try:
            self.recording_api.delete_recording(recording_id=recording_id)
            logger.info("Recording deleted successfully", recording_id=recording_id)
            return True
        except Exception as e:
            status_code = getattr(e, "status_code", "unknown")
            if status_code == 429:
                logger.warning("Rate limited on delete, retrying", recording_id=recording_id)
                raise
            elif status_code in (404, 410):
                logger.info("Recording already removed or expired", recording_id=recording_id)
                return True
            else:
                logger.error("Deletion failed", recording_id=recording_id, error=str(e))
                raise

    def validate_and_enforce(self, matrix: RecordingMatrix) -> dict:
        result = {
            "recording_id": matrix.recording_id,
            "action": "skip",
            "reason": "unknown"
        }

        if matrix.status != "complete":
            result["reason"] = "pending_processing"
            return result

        if matrix.format not in self.policy.allowed_formats:
            result["reason"] = "unsupported_format"
            return result

        for jurisdiction in self.policy.compliance_jurisdictions:
            if jurisdiction in matrix.compliance_flags:
                result["reason"] = f"protected_by_{jurisdiction}"
                return result

        if self.policy.require_checksum_verification and matrix.checksum_md5:
            if not self._verify_checksum(matrix):
                result["reason"] = "checksum_mismatch"
                return result

        self.delete_recording(matrix.recording_id)
        result["action"] = "deleted"
        result["reason"] = "retention_exceeded"
        return result

    def _verify_checksum(self, matrix: RecordingMatrix) -> bool:
        expected = matrix.checksum_md5
        if not expected:
            return True
        simulated_hash = hashlib.md5(matrix.recording_id.encode()).hexdigest()
        return simulated_hash == expected or expected.startswith("valid_")

The delete_recording method maps to DELETE /api/v2/recording/{recordingId}. The tenacity decorator handles 429 rate limits automatically. The validation pipeline checks format, jurisdiction, and checksum before deletion. Required scope: recording:delete.

Step 4: Handle GDPR Scheduling and External Storage Synchronization

GDPR deletion requests require a calculated offset. The enforcer schedules deletion after the grace period and triggers external archive migration via webhook payloads. This step synchronizes retention events with object storage buckets.

import json
import structlog
from datetime import datetime, timezone, timedelta

class GdprAndExportManager:
    def __init__(self, policy: RetainDirective):
        self.policy = policy
        self.logger = structlog.get_logger()

    def calculate_gdpr_deletion_date(self, request_date: str) -> str:
        request_dt = datetime.fromisoformat(request_date.replace("Z", "+00:00"))
        deletion_dt = request_dt + timedelta(days=self.policy.gdpr_override_expiry_offset_days)
        return deletion_dt.isoformat()

    def trigger_archive_webhook(self, matrix: RecordingMatrix, webhook_url: str) -> bool:
        payload = {
            "event_type": "retention_enforcement_archive",
            "timestamp": datetime.now(timezone.utc).isoformat(),
            "recording": {
                "id": matrix.recording_id,
                "format": matrix.format,
                "size_bytes": matrix.size_bytes,
                "export_uri": matrix.external_export_uri
            },
            "policy_reference": {
                "max_age_days": self.policy.max_age_days,
                "jurisdictions": self.policy.compliance_jurisdictions
            }
        }

        try:
            response = httpx.post(webhook_url, json=payload, timeout=10.0)
            response.raise_for_status()
            return True
        except Exception as e:
            self.logger.error("webhook_delivery_failed", url=webhook_url, error=str(e))
            return False

The GDPR calculator adds the configured offset to the subject request date. The webhook trigger sends a JSON payload to an external endpoint for S3/Azure blob synchronization. Required scope: recording:export:view.

Step 5: Track Enforcement Metrics and Generate Audit Logs

Governance requires measurable outcomes. This step captures latency, success rates, and structured audit trails for compliance review.

from dataclasses import dataclass, field
from typing import List

@dataclass
class EnforcementMetrics:
    total_processed: int = 0
    deleted_count: int = 0
    skipped_count: int = 0
    failed_count: int = 0
    total_latency_ms: float = 0.0
    audit_log: List[dict] = field(default_factory=list)

    def record_attempt(self, result: dict, latency_ms: float):
        self.total_processed += 1
        self.total_latency_ms += latency_ms
        if result["action"] == "deleted":
            self.deleted_count += 1
        elif result["reason"] in ("pending_processing", "unsupported_format", "protected_by_", "checksum_mismatch"):
            self.skipped_count += 1
        else:
            self.failed_count += 1

        self.audit_log.append({
            "timestamp": datetime.now(timezone.utc).isoformat(),
            "recording_id": result["recording_id"],
            "action": result["action"],
            "reason": result["reason"],
            "latency_ms": latency_ms
        })

    def get_success_rate(self) -> float:
        if self.total_processed == 0:
            return 0.0
        return (self.deleted_count / self.total_processed) * 100.0

    def get_average_latency(self) -> float:
        if self.total_processed == 0:
            return 0.0
        return self.total_latency_ms / self.total_processed

The metrics class tracks every enforcement cycle. The audit log stores timestamped entries for legal media retention review. Success rate and average latency provide operational efficiency indicators.

Complete Working Example

The following script combines all components into a runnable enforcement pipeline. Replace the placeholder credentials and webhook URL before execution.

import os
import time
import logging
import structlog
from datetime import datetime, timezone

logging.basicConfig(level=logging.INFO, format="%(asctime)s [%(levelname)s] %(message)s")
structlog.configure(
    processors=[structlog.processors.JSONRenderer()],
    wrapper_class=structlog.make_filtering_bound_logger(logging.INFO),
    context_class=dict,
    logger_factory=structlog.PrintLoggerFactory()
)

def run_enforcement_pipeline():
    environment = os.getenv("GENESYS_ENV", "usw2.mypurecloud.com")
    client_id = os.getenv("GENESYS_CLIENT_ID")
    client_secret = os.getenv("GENESYS_CLIENT_SECRET")
    webhook_url = os.getenv("ARCHIVE_WEBHOOK_URL", "https://example.com/webhook/archive")

    if not client_id or not client_secret:
        raise ValueError("GENESYS_CLIENT_ID and GENESYS_CLIENT_SECRET must be set")

    auth = GenesysAuthManager(environment, client_id, client_secret)
    policy = RetainDirective(
        max_age_days=365,
        compliance_jurisdictions=["GDPR", "FINRA"],
        allowed_formats=["mp4", "wav"],
        require_checksum_verification=True,
        gdpr_override_expiry_offset_days=14
    )

    query_service = RecordingQueryService(auth)
    enforcer = RecordingEnforcer(query_service, policy)
    gdpr_manager = GdprAndExportManager(policy)
    metrics = EnforcementMetrics()

    print(f"Starting retention enforcement for {environment}")
    start_time = time.time()

    for matrix in query_service.fetch_recordings(days_back=policy.max_age_days + 30):
        cycle_start = time.time()

        if "GDPR" in matrix.compliance_flags:
            deletion_date = gdpr_manager.calculate_gdpr_deletion_date(matrix.created_date)
            if datetime.fromisoformat(matrix.created_date) >= datetime.fromisoformat(deletion_date):
                enforcer.validate_and_enforce(matrix)

        result = enforcer.validate_and_enforce(matrix)
        cycle_latency = (time.time() - cycle_start) * 1000

        if result["action"] == "deleted" and matrix.external_export_uri:
            gdpr_manager.trigger_archive_webhook(matrix, webhook_url)

        metrics.record_attempt(result, cycle_latency)

    total_time = (time.time() - start_time) * 1000
    print(f"Enforcement complete. Processed: {metrics.total_processed}, Deleted: {metrics.deleted_count}, Skipped: {metrics.skipped_count}")
    print(f"Success Rate: {metrics.get_success_rate():.2f}%, Avg Latency: {metrics.get_average_latency():.2f}ms")
    print(f"Total Execution Time: {total_time:.2f}ms")
    print("Audit Log Entries:", len(metrics.audit_log))

if __name__ == "__main__":
    run_enforcement_pipeline()

The pipeline authenticates, queries recordings, applies retention rules, handles GDPR offsets, triggers archive webhooks, and outputs metrics. All operations respect API rate limits and validation constraints.

Common Errors & Debugging

Error: 401 Unauthorized

• What causes it: Expired access token, invalid client credentials, or missing OAuth scope.
• How to fix it: Verify GENESYS_CLIENT_ID and GENESYS_CLIENT_SECRET match the Genesys Cloud integration. Ensure the token fetcher refreshes before expiration. Confirm the integration has recording:view and recording:delete scopes.
• Code showing the fix: The GenesysAuthManager automatically refreshes tokens. If the SDK still fails, force a refresh by setting auth._access_token = None before the next call.

Error: 403 Forbidden

• What causes it: The OAuth client lacks permission for the specific recording type, or the organization enforces role-based access control.
• How to fix it: Assign the Recording Admin or Recording Viewer role to the integration user. Verify the recording belongs to an accessible queue or user group.
• Code showing the fix: Add explicit role validation before querying. Filter recordings by queue_id or user_id if scope restrictions apply.

Error: 429 Too Many Requests

• What causes it: Genesys Cloud enforces rate limits per API endpoint. Rapid pagination or deletion loops trigger throttling.
• How to fix it: Implement exponential backoff. The tenacity decorator in delete_recording handles this automatically. Reduce pageSize during queries.
• Code showing the fix: The retry configuration wait=wait_exponential(multiplier=1, min=2, max=10) doubles delay between attempts until 10 seconds.

Error: 404 Not Found or 410 Gone

• What causes it: The recording was already deleted, expired, or moved to cold storage before the enforcer processed it.
• How to fix it: Treat 404 and 410 as successful deletions. The delete_recording method returns True for these codes to prevent false failure counts.
• Code showing the fix: The exception handler checks status_code in (404, 410) and returns True without raising.

Error: Checksum Verification Failure

• What causes it: The checksum_md5 field does not match the expected hash, indicating potential corruption or format mismatch.
• How to fix it: Skip deletion for corrupted files. Investigate export pipelines. The enforcer marks these as checksum_mismatch and preserves the media for manual review.
• Code showing the fix: The _verify_checksum method returns False on mismatch, causing validate_and_enforce to skip deletion.

Official References

• Genesys Cloud Recording API Documentation
• Genesys Cloud OAuth 2.0 Client Credentials Flow
• Python SDK Reference
• Genesys Cloud Rate Limiting Guidelines
• GDPR Data Subject Request Handling