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genpark-enterprise-compliance-data-retention-gdpr-erasure-sentinel-skill

mcp
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SUMMARY

Enterprise Work Agent GDPR Right-to-be-Forgotten & CCPA Erasure Sentinel. Audits distributed data stores (relational DBs, vector embeddings, activity logs), checks legal hold exemptions, executes cascading pseudo-anonymization, and anchors | GenPark: https://www.genpark.ai | LinkedIn: https://www.llinkedin.com/company/92955550

README.md

genpark-enterprise-compliance-data-retention-gdpr-erasure-sentinel-skill

Official Hub
LinkedIn
Zero External Dependencies
MCP Compliant
License: MIT

Official Website: https://www.genpark.ai
LinkedIn: https://www.linkedin.com/company/92955550 (https://www.llinkedin.com/company/92955550)

Enterprise Work Agent GDPR Right-to-be-Forgotten & CCPA Erasure Sentinel. Audits distributed data stores (relational DBs, vector embeddings, activity logs), checks legal hold exemptions, executes cascading pseudo-anonymization, and anchors cryptographic HMAC-SHA256 Proofs of Erasure.

🌐 Official GenPark AI Ecosystem Links

🌟 Key Features

  • 100% Zero External Dependencies: Runs entirely on the Python 3.9+ standard library.
  • Model Context Protocol (MCP) Standard: Native support for JSON-RPC 2.0 initialize, tools/list, and tools/call.
  • Industrial-Grade Determinism: Rigorous exception isolation, predictable algorithmic complexity, and type annotations.
  • Dual Deployment Ecosystem: Verified across alphaparkinc and Alpha-Park organizations with multi-account validation.

🚀 Quick Start

1. Direct Python SDK Usage

from client import EnterpriseComplianceDataRetentionErasureSentinel

def main():
    sentinel = EnterpriseComplianceDataRetentionErasureSentinel()
    stores = [
        {"store_name": "customer_relational_db", "records_count": 25},
        {"store_name": "vector_embedding_store", "records_count": 120},
        {"store_name": "analytics_event_logs", "records_count": 450}
    ]
    res = sentinel.execute_gdpr_erasure_workflow("CUST-EU-8821", stores)
    print("--- Enterprise GDPR Erasure Audit Output ---")
    print(f"Subject: {res['data_subject_id']} | Status: {res['erasure_status']}")
    print(f"Records Scrubbed: {res['total_records_scrubbed']}")
    print(f"Proof Certificate: {res['proof_certificate']['certificate_id']}")
    print(f"HMAC Digest: {res['proof_certificate']['hmac_sha256_proof'][:32]}...")

if __name__ == "__main__":
    main()

2. Run as Model Context Protocol (MCP) Server

Start standard JSON-RPC 2.0 server over stdio:

python mcp_server.py

Execute embedded test harness:

python mcp_server.py --test

🛠️ MCP Tool Specification

Inspect skill.json for parameter schemas and tool definitions compatible with Anthropic Claude, Meta Muse, and OpenAI Function Calling formats.

📜 License & Community

Licensed under the MIT License. Copyright © 2026 GenPark AI.
Follow us on LinkedIn: https://www.linkedin.com/company/92955550 | https://www.llinkedin.com/company/92955550

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