{"id":49109062,"url":"https://github.com/jfs2j/data-protection-automation","last_synced_at":"2026-04-21T03:32:45.220Z","repository":{"id":325225587,"uuid":"1100122359","full_name":"jfs2j/data-protection-automation","owner":"jfs2j","description":"Enterprise data protection automation tools: DLP alert enrichment, data classification, and policy enforcement.","archived":false,"fork":false,"pushed_at":"2025-11-20T06:35:53.000Z","size":13,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":0,"default_branch":"main","last_synced_at":"2025-11-20T08:29:05.333Z","etag":null,"topics":["cybersecurity","data-governance","data-protection","dlp","dlpremediation","powershell","privacy","python","python-3","python-script","python3"],"latest_commit_sha":null,"homepage":"https://www.linkedin.com/in/joelsop/","language":"PowerShell","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/jfs2j.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null,"notice":null,"maintainers":null,"copyright":null,"agents":null,"dco":null,"cla":null}},"created_at":"2025-11-19T21:42:02.000Z","updated_at":"2025-11-20T06:42:22.000Z","dependencies_parsed_at":null,"dependency_job_id":null,"html_url":"https://github.com/jfs2j/data-protection-automation","commit_stats":null,"previous_names":["jfs2j/data-protection-automation"],"tags_count":null,"template":false,"template_full_name":null,"purl":"pkg:github/jfs2j/data-protection-automation","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jfs2j%2Fdata-protection-automation","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jfs2j%2Fdata-protection-automation/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jfs2j%2Fdata-protection-automation/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jfs2j%2Fdata-protection-automation/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/jfs2j","download_url":"https://codeload.github.com/jfs2j/data-protection-automation/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jfs2j%2Fdata-protection-automation/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":32075239,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-04-21T02:38:07.213Z","status":"ssl_error","status_checked_at":"2026-04-21T02:38:06.559Z","response_time":128,"last_error":"SSL_read: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["cybersecurity","data-governance","data-protection","dlp","dlpremediation","powershell","privacy","python","python-3","python-script","python3"],"created_at":"2026-04-21T03:32:44.550Z","updated_at":"2026-04-21T03:32:45.211Z","avatar_url":"https://github.com/jfs2j.png","language":"PowerShell","funding_links":[],"categories":[],"sub_categories":[],"readme":"# data-protection-automation\nEnterprise data protection automation tools: DLP alert enrichment, data classification, and policy enforcement.\n\n# Data Protection Automation Portfolio\n\n**Author:** Joel Sop | [LinkedIn](https://linkedin.com/in/JoelSop) | [jfs2j@virginia.edu](mailto:jfs2j@virginia.edu)\n\nEnterprise-scale data protection automation tools built from 8 years architecting DLP and data governance programs at Capital One. These tools demonstrate practical approaches to reducing mean-time-to-respond, improving classification accuracy, and scaling privacy operations without linear headcount growth.\n\n---\n\n## 🎯 Portfolio Overview\n\nThis repository showcases three core automation capabilities I've built in production environments:\n\n| Tool | Purpose | Impact |\n|------|---------|--------|\n| **DLP Alert Enrichment** | Adds user context and risk scoring to raw DLP alerts | Reduced MTTR by 35%, enabled 40% higher case volume |\n| **Data Classification** | Automated sensitive data discovery with false positive reduction | Reduced FP by 25%, expanded coverage by 40% |\n| **Policy Automation** | Dynamic policy enforcement based on data context | Maintained 90% deployment velocity with 98% compliance |\n\n---\n\n## 📁 Repository Structure\n\ndata-protection-automation/\n│\n├── dlp-alert-enrichment/       # Python-based alert enrichment\n├── data-classification/        # PowerShell data classification scanner\n├── policy-automation/          # Policy enforcement engine\n└── README.md                   # This file\n\n\n---\n\n## 🚀 Quick Start\n\n### Prerequisites\n- Python 3.8+ (for Python scripts)\n- PowerShell 5.1+ (for PowerShell scripts)\n\n### Installation\n```bash\n# Clone repository\ngit clone https://github.com/jfs2j/data-protection-automation.git\ncd data-protection-automation\n\n# Install Python dependencies\npip install -r requirements.txt\n\n# Run alert enrichment demo\ncd dlp-alert-enrichment\npython alert_enricher.py\n\n# Run data classification demo (Windows/PowerShell)\ncd ../data-classification\n.\\classify_data.ps1 -Verbose\n```\n\n---\n\n## 💡 Philosophy: Automation as Scale Enabler\n\nAt Capital One, I learned that **effective data protection scales through automation, not headcount**. These tools embody three core principles:\n\n### 1. **Context Over Volume**\nRaw DLP alerts lack actionable context. By enriching alerts with user department, manager info, and historical behavior, analysts can triage faster and more accurately.\n\n### 2. **Precision Over Recall**\nFalse positives erode trust in DLP systems. By implementing validation logic (e.g., Luhn algorithm for credit cards), we reduce noise while maintaining detection coverage.\n\n### 3. **Enablement Over Control**\nData protection should enable business velocity, not slow it down. By automating policy enforcement and providing clear guidance, teams can move fast while managing risk.\n\n---\n\n## 🏆 Real-World Impact\n\nThese automation approaches delivered measurable outcomes at Capital One:\n\n- **35% reduction** in mean-time-to-respond for DLP incidents\n- **60% reduction** in manual alert triage time\n- **25% reduction** in false positive rate\n- **40% increase** in SOC case handling capacity (no headcount growth)\n- **98% policy compliance rate** while maintaining 90% deployment frequency\n\n---\n\n## 🔧 Technical Stack\n\n**Languages:** Python, PowerShell, Bash  \n**Data Protection Platforms:** Netskope, Symantec, Proofpoint, Microsoft Purview  \n**Cloud:** AWS (GuardDuty, CloudTrail, S3), Azure, GCP  \n**SIEM:** Splunk, Chronicle  \n**Automation:** API integration, webhook-based workflows\n\n---\n\n## 📚 Use Cases\n\n### Financial Services\n- PCI-DSS compliance automation\n- Credit card data detection and remediation\n- Cross-border data transfer monitoring\n\n### Healthcare\n- HIPAA-compliant data classification\n- PHI discovery and protection\n- Patient data retention automation\n\n### Entertainment/Streaming\n- User viewing history protection\n- Content licensing data governance\n- Ad-tier consent management\n\n---\n\n## 🤝 Contributing\n\nThis is a portfolio repository demonstrating production-proven automation patterns. While not actively maintained as an open-source project, the code is provided under MIT License for educational and reference purposes.\n\n**Feedback welcome:** If you're implementing similar automation and have questions, feel free to reach out via LinkedIn or email.\n\n---\n\n## 📄 License\n\nMIT License - See [LICENSE](LICENSE) file for details.\n\n---\n\n## 👤 About Me\n\nI'm Joel Sop, a Principal Data Protection Engineer with 8 years building enterprise-scale data governance and privacy infrastructure. I specialize in balancing regulatory compliance with business enablement through pragmatic automation and cross-functional leadership.\n\n**Currently:** Exploring opportunities in consumer privacy and streaming data protection.\n\n**Connect:** [LinkedIn](https://linkedin.com/in/JoelSop) | [GitHub](https://github.com/jfs2j) | [Email](mailto:jfs2j@virginia.edu)\n\n---\n\n## 📊 Repository Stats\n\n![Python](https://img.shields.io/badge/Python-3.8%2B-blue)\n![PowerShell](https://img.shields.io/badge/PowerShell-5.1%2B-blue)\n![License](https://img.shields.io/badge/License-MIT-green)\n![Status](https://img.shields.io/badge/Status-Portfolio-orange)","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjfs2j%2Fdata-protection-automation","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fjfs2j%2Fdata-protection-automation","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjfs2j%2Fdata-protection-automation/lists"}