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Covers ICO\u002FEDPB guidance compliance, exemption handling, and scalable batch processing. Use when building or auditing DSAR response capabilities under GDPR\u002FUK GDPR.","mukul975\u002FAnthropic-Cybersecurity-Skills","mukul975","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F42860185?v=4","01M6ADEQR8T5M4EEG6C67BQCWF","Mahipal","01M6ADJPMYWG1E5GM1FWBE3128",35884,"Apache-2.0","https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fimplementing-gdpr-data-subject-access-request\u002FSKILL.md",[18,23,26,31,34],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000018","写作与文案","SCENARIO","使用场景",{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发",{"categoryNo":27,"categoryName":28,"taxonomyType":29,"taxonomyTypeDesc":30},"01JBSEEDCAT000000000000033","数据分析师","PROFESSION","适用职业",{"categoryNo":32,"categoryName":33,"taxonomyType":29,"taxonomyTypeDesc":30},"01JBSEEDCAT000000000000036","内容创作者",{"categoryNo":35,"categoryName":36,"taxonomyType":29,"taxonomyTypeDesc":30},"01JBSEEDCAT000000000000037","律师与法务",[38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57],"data","implementing gdpr data subject access request","gdpr","data-subject-rights","workflows","article-15","pii-discovery","dsar","pii","t1059","t1078","ico","pr","cybersecurity","identity","audit","batch","logging","verification","capabilities","2026-11-10 10:12:30",false,"---\nname: implementing-gdpr-data-subject-access-request\ndescription: 'Automates GDPR Data Subject Access Request (DSAR) workflows including\n  identity verification, PII discovery across databases and files using regex and\n  NER, data mapping, response templating per Article 15 requirements, deadline tracking,\n  and audit logging. Covers ICO\u002FEDPB guidance compliance, exemption handling, and\n  scalable batch processing. Use when building or auditing DSAR response capabilities\n  under GDPR\u002FUK GDPR.\n\n  '\ndomain: cybersecurity\nsubdomain: privacy-compliance\ntags:\n- gdpr\n- dsar\n- privacy\n- pii-discovery\n- data-subject-rights\n- compliance\n- article-15\nversion: '1.0'\nauthor: mukul975\nlicense: Apache-2.0\nnist_csf:\n- GV.PO-01\n- PR.DS-01\n- GV.OC-05\nmitre_attack:\n- T1078\n- T1190\n- T1059\n---\n\n# Implementing GDPR Data Subject Access Request (DSAR) Workflow\n\n## When to Use\n\n- When building automated DSAR processing pipelines for GDPR\u002FUK GDPR compliance\n- When implementing PII discovery across structured and unstructured data sources\n- When creating response templates that satisfy Article 15 disclosure requirements\n- When auditing existing DSAR handling for regulatory compliance gaps\n- When scaling DSAR processing from manual to automated workflows\n\n## Prerequisites\n\n- Python 3.8+ with required dependencies (spacy, presidio-analyzer, jinja2)\n- Access to data sources where personal data resides (databases, file shares, logs)\n- Understanding of GDPR Article 15 requirements and ICO\u002FEDPB guidance\n- Appropriate authorization and data protection officer (DPO) approval\n- Test environment with synthetic or anonymized data for validation\n\n## Background\n\n### GDPR Article 15 - Right of Access\n\nUnder GDPR Article 15, data subjects have the right to obtain from the controller:\n\n1. **Confirmation** that their personal data is being processed\n2. **A copy** of all personal data held about them\n3. **Supplementary information** including:\n   - Purposes of processing\n   - Categories of personal data\n   - Recipients or categories of recipients\n   - Retention periods or criteria to determine them\n   - Right to rectification, erasure, restriction, or objection\n   - Right to lodge a complaint with a supervisory authority\n   - Source of the data (if not collected directly from the subject)\n   - Existence of automated decision-making, including profiling\n\n### Timeline Requirements\n\n- **Standard deadline**: 1 calendar month from receipt of valid request\n- **Complex extension**: Up to 2 additional months (must notify within first month)\n- **Clock pause**: Permitted when identity verification or clarification is needed\n- **Format**: Electronic form if request made electronically (unless otherwise requested)\n- **Cost**: Free of charge (unless manifestly unfounded\u002Fexcessive)\n\n### ICO\u002FEDPB Guidance Key Points\n\n- No formal format required for DSARs - verbal, written, social media all valid\n- Request need not mention \"subject access request\" or cite Article 15\n- Identity verification must be proportionate to the risk\n- Exemptions exist for legal privilege, third-party data, trade secrets\n- EDPB coordinated enforcement actions cover right of access compliance\n\n## Instructions\n\n### Step 1: DSAR Intake and Verification\n\nImplement a request intake system that captures the request through any channel,\nverifies the requester's identity, and starts the compliance clock.\n\n```python\nfrom agent import DSARWorkflowEngine\n\nengine = DSARWorkflowEngine(config_path=\"dsar_config.json\")\n\n# Register a new DSAR\nrequest = engine.register_dsar(\n    requester_name=\"Jane Smith\",\n    requester_email=\"jane.smith@example.com\",\n    request_channel=\"email\",\n    request_text=\"I would like a copy of all personal data you hold about me.\",\n    identity_docs=[\"passport_verified\"],\n)\nprint(f\"DSAR ID: {request['dsar_id']}, Deadline: {request['deadline']}\")\n```\n\n### Step 2: PII Discovery Across Data Sources\n\nScan databases, files, and logs using regex patterns and NER to find all\npersonal data associated with the data subject.\n\n```python\nfrom agent import PIIDiscoveryEngine\n\npii_engine = PIIDiscoveryEngine()\n\n# Scan structured data (database)\ndb_results = pii_engine.scan_database(\n    connection_string=\"postgresql:\u002F\u002Fuser:pass@localhost\u002Fappdb\",\n    search_identifiers={\"email\": \"jane.smith@example.com\", \"name\": \"Jane Smith\"},\n)\n\n# Scan unstructured data (files, logs)\nfile_results = pii_engine.scan_files(\n    directories=[\"\u002Fvar\u002Flog\u002Fapp\", \"\u002Fdata\u002Fexports\", \"\u002Fdata\u002Fdocuments\"],\n    search_identifiers={\"email\": \"jane.smith@example.com\", \"name\": \"Jane Smith\"},\n)\n\n# Scan with NER for contextual PII detection\nner_results = pii_engine.scan_with_ner(\n    text_corpus=file_results[\"raw_text_matches\"],\n    entity_types=[\"PERSON\", \"EMAIL\", \"PHONE_NUMBER\", \"LOCATION\", \"DATE_OF_BIRTH\"],\n)\n\nall_pii = pii_engine.consolidate_results(db_results, file_results, ner_results)\nprint(f\"Found {all_pii['total_records']} PII records across {all_pii['source_count']} sources\")\n```\n\n### Step 3: Data Mapping and Classification\n\nMap discovered PII to processing purposes, legal bases, and retention periods\nas required by Article 15.\n\n```python\nfrom agent import DataMapper\n\nmapper = DataMapper(data_inventory_path=\"data_inventory.json\")\n\n# Map PII to Article 15 categories\nmapped_data = mapper.map_to_article15(\n    pii_records=all_pii,\n    data_subject_id=\"jane.smith@example.com\",\n)\n\n# Output includes processing purposes, recipients, retention for each data category\nfor category in mapped_data[\"categories\"]:\n    print(f\"Category: {category['name']}\")\n    print(f\"  Purpose: {category['processing_purpose']}\")\n    print(f\"  Legal basis: {category['legal_basis']}\")\n    print(f\"  Retention: {category['retention_period']}\")\n    print(f\"  Recipients: {', '.join(category['recipients'])}\")\n```\n\n### Step 4: Exemption Review\n\nApply exemptions where lawful (third-party data, legal privilege, trade secrets)\nbefore compiling the response.\n\n```python\nfrom agent import ExemptionReviewer\n\nreviewer = ExemptionReviewer()\n\n# Check for applicable exemptions\nreview_result = reviewer.review_exemptions(\n    mapped_data=mapped_data,\n    exemption_checks=[\n        \"third_party_data\",\n        \"legal_professional_privilege\",\n        \"trade_secrets\",\n        \"crime_prevention\",\n        \"management_forecasting\",\n    ],\n)\n\n# Apply redactions where exemptions apply\nredacted_data = reviewer.apply_redactions(mapped_data, review_result[\"exemptions\"])\nprint(f\"Applied {review_result['exemption_count']} exemptions\")\n```\n\n### Step 5: Response Generation\n\nGenerate a compliant DSAR response package with cover letter, data export,\nand supplementary information document.\n\n```python\nfrom agent import DSARResponseGenerator\n\ngenerator = DSARResponseGenerator(template_dir=\"templates\u002F\")\n\n# Generate complete response package\nresponse = generator.generate_response(\n    dsar_id=request[\"dsar_id\"],\n    data_subject=\"Jane Smith\",\n    mapped_data=redacted_data,\n    format=\"pdf\",  # or \"json\", \"csv\"\n)\n\n# Package includes: cover letter, data export, supplementary info, audit log\nfor doc in response[\"documents\"]:\n    print(f\"Generated: {doc['filename']} ({doc['type']})\")\n```\n\n### Step 6: Audit Trail and Compliance Logging\n\nMaintain complete audit trail of the DSAR lifecycle for accountability.\n\n```python\nfrom agent import DSARAuditLogger\n\nlogger = DSARAuditLogger(log_path=\"dsar_audit_logs\u002F\")\n\n# Log complete DSAR lifecycle\nlogger.log_event(request[\"dsar_id\"], \"request_received\", {\n    \"channel\": \"email\",\n    \"identity_verified\": True,\n})\nlogger.log_event(request[\"dsar_id\"], \"pii_discovery_complete\", {\n    \"records_found\": all_pii[\"total_records\"],\n    \"sources_scanned\": all_pii[\"source_count\"],\n})\nlogger.log_event(request[\"dsar_id\"], \"response_sent\", {\n    \"format\": \"pdf\",\n    \"documents_count\": len(response[\"documents\"]),\n    \"exemptions_applied\": review_result[\"exemption_count\"],\n})\n\n# Generate compliance report\ncompliance_report = logger.generate_compliance_report(request[\"dsar_id\"])\n```\n\n## Examples\n\n### Complete DSAR Processing Pipeline\n\n```python\nfrom agent import DSARWorkflowEngine, PIIDiscoveryEngine, DSARResponseGenerator\n\n# Full automated pipeline\nengine = DSARWorkflowEngine(config_path=\"dsar_config.json\")\npii = PIIDiscoveryEngine()\ngen = DSARResponseGenerator(template_dir=\"templates\u002F\")\n\n# 1. Intake\nreq = engine.register_dsar(\n    requester_name=\"John Doe\",\n    requester_email=\"john.doe@example.com\",\n    request_channel=\"web_form\",\n    request_text=\"Please provide all my data under GDPR Article 15.\",\n    identity_docs=[\"email_verified\", \"account_match\"],\n)\n\n# 2. Discover\nresults = pii.full_scan(\n    search_identifiers={\"email\": \"john.doe@example.com\"},\n    sources=[\"database\", \"files\", \"logs\"],\n)\n\n# 3. Generate response\nresponse = gen.generate_response(\n    dsar_id=req[\"dsar_id\"],\n    data_subject=\"John Doe\",\n    mapped_data=results,\n)\n\n# 4. Track deadline\nengine.update_status(req[\"dsar_id\"], \"response_sent\")\nprint(f\"DSAR {req['dsar_id']} completed, {engine.days_remaining(req['dsar_id'])} days remaining\")\n```\n\n### PII Regex Pattern Testing\n\n```python\nfrom agent import PIIPatternMatcher\n\nmatcher = PIIPatternMatcher()\n\n# Test individual patterns\ntest_text = \"Contact jane.smith@example.com or call +44 20 7946 0958. SSN: 123-45-6789\"\nmatches = matcher.scan_text(test_text)\nfor m in matches:\n    print(f\"  [{m['type']}] '{m['value']}' (confidence: {m['confidence']})\")\n```\n\n## References\n\n- GDPR Article 15: https:\u002F\u002Fgdpr-info.eu\u002Fart-15-gdpr\u002F\n- ICO Subject Access Request Guidance: https:\u002F\u002Fico.org.uk\u002Ffor-organisations\u002Fuk-gdpr-guidance-and-resources\u002Fsubject-access-requests\u002F\n- EDPB Guidelines 01\u002F2022 on Right of Access: https:\u002F\u002Fwww.edpb.europa.eu\u002Fsystem\u002Ffiles\u002F2023-04\u002Fedpb_guidelines_202201_data_subject_rights_access_v2_en.pdf\n- GDPR Article 12 (DSAR Modalities): https:\u002F\u002Fgdpr-info.eu\u002Fart-12-gdpr\u002F\n- Regulation (EU) 2025\u002F2518 (Procedural Rules): Cross-border GDPR enforcement procedural rules\n","# Implementing GDPR Data Subject Access Request (DSAR) Workflow\n\n## When to Use\n\n- When building automated DSAR processing pipelines for GDPR\u002FUK GDPR compliance\n- When implementing PII discovery across structured and unstructured data sources\n- When creating response templates that satisfy Article 15 disclosure requirements\n- When auditing existing DSAR handling for regulatory compliance gaps\n- When scaling DSAR processing from manual to automated workflows\n\n## Prerequisites\n\n- Python 3.8+ with required dependencies (spacy, presidio-analyzer, jinja2)\n- Access to data sources where personal data resides (databases, file shares, logs)\n- Understanding of GDPR Article 15 requirements and ICO\u002FEDPB guidance\n- Appropriate authorization and data protection officer (DPO) approval\n- Test environment with synthetic or anonymized data for validation\n\n## Background\n\n### GDPR Article 15 - Right of Access\n\nUnder GDPR Article 15, data subjects have the right to obtain from the controller:\n\n1. **Confirmation** that their personal data is being processed\n2. **A copy** of all personal data held about them\n3. **Supplementary information** including:\n   - Purposes of processing\n   - Categories of personal data\n   - Recipients or categories of recipients\n   - Retention periods or criteria to determine them\n   - Right to rectification, erasure, restriction, or objection\n   - Right to lodge a complaint with a supervisory authority\n   - Source of the data (if not collected directly from the subject)\n   - Existence of automated decision-making, including profiling\n\n### Timeline Requirements\n\n- **Standard deadline**: 1 calendar month from receipt of valid request\n- **Complex extension**: Up to 2 additional months (must notify within first month)\n- **Clock pause**: Permitted when identity verification or clarification is needed\n- **Format**: Electronic form if request made electronically (unless otherwise requested)\n- **Cost**: Free of charge (unless manifestly unfounded\u002Fexcessive)\n\n### ICO\u002FEDPB Guidance Key Points\n\n- No formal format required for DSARs - verbal, written, social media all valid\n- Request need not mention \"subject access request\" or cite Article 15\n- Identity verification must be proportionate to the risk\n- Exemptions exist for legal privilege, third-party data, trade secrets\n- EDPB coordinated enforcement actions cover right of access compliance\n\n## Instructions\n\n### Step 1: DSAR Intake and Verification\n\nImplement a request intake system that captures the request through any channel,\nverifies the requester's identity, and starts the compliance clock.\n\n```python\nfrom agent import DSARWorkflowEngine\n\nengine = DSARWorkflowEngine(config_path=\"dsar_config.json\")\n\n# Register a new DSAR\nrequest = engine.register_dsar(\n    requester_name=\"Jane Smith\",\n    requester_email=\"jane.smith@example.com\",\n    request_channel=\"email\",\n    request_text=\"I would like a copy of all personal data you hold about me.\",\n    identity_docs=[\"passport_verified\"],\n)\nprint(f\"DSAR ID: {request['dsar_id']}, Deadline: {request['deadline']}\")\n```\n\n### Step 2: PII Discovery Across Data Sources\n\nScan databases, files, and logs using regex patterns and NER to find all\npersonal data associated with the data subject.\n\n```python\nfrom agent import PIIDiscoveryEngine\n\npii_engine = PIIDiscoveryEngine()\n\n# Scan structured data (database)\ndb_results = pii_engine.scan_database(\n    connection_string=\"postgresql:\u002F\u002Fuser:pass@localhost\u002Fappdb\",\n    search_identifiers={\"email\": \"jane.smith@example.com\", \"name\": \"Jane Smith\"},\n)\n\n# Scan unstructured data (files, logs)\nfile_results = pii_engine.scan_files(\n    directories=[\"\u002Fvar\u002Flog\u002Fapp\", \"\u002Fdata\u002Fexports\", \"\u002Fdata\u002Fdocuments\"],\n    search_identifiers={\"email\": \"jane.smith@example.com\", \"name\": \"Jane Smith\"},\n)\n\n# Scan with NER for contextual PII detection\nner_results = pii_engine.scan_with_ner(\n    text_corpus=file_results[\"raw_text_matches\"],\n    entity_types=[\"PERSON\", \"EMAIL\", \"PHONE_NUMBER\", \"LOCATION\", \"DATE_OF_BIRTH\"],\n)\n\nall_pii = pii_engine.consolidate_results(db_results, file_results, ner_results)\nprint(f\"Found {all_pii['total_records']} PII records across {all_pii['source_count']} sources\")\n```\n\n### Step 3: Data Mapping and Classification\n\nMap discovered PII to processing purposes, legal bases, and retention periods\nas required by Article 15.\n\n```python\nfrom agent import DataMapper\n\nmapper = DataMapper(data_inventory_path=\"data_inventory.json\")\n\n# Map PII to Article 15 categories\nmapped_data = mapper.map_to_article15(\n    pii_records=all_pii,\n    data_subject_id=\"jane.smith@example.com\",\n)\n\n# Output includes processing purposes, recipients, retention for each data category\nfor category in mapped_data[\"categories\"]:\n    print(f\"Category: {category['name']}\")\n    print(f\"  Purpose: {category['processing_purpose']}\")\n    print(f\"  Legal basis: {category['legal_basis']}\")\n    print(f\"  Retention: {category['retention_period']}\")\n    print(f\"  Recipients: {', '.join(category['recipients'])}\")\n```\n\n### Step 4: Exemption Review\n\nApply exemptions where lawful (third-party data, legal privilege, trade secrets)\nbefore compiling the response.\n\n```python\nfrom agent import ExemptionReviewer\n\nreviewer = ExemptionReviewer()\n\n# Check for applicable exemptions\nreview_result = reviewer.review_exemptions(\n    mapped_data=mapped_data,\n    exemption_checks=[\n        \"third_party_data\",\n        \"legal_professional_privilege\",\n        \"trade_secrets\",\n        \"crime_prevention\",\n        \"management_forecasting\",\n    ],\n)\n\n# Apply redactions where exemptions apply\nredacted_data = reviewer.apply_redactions(mapped_data, review_result[\"exemptions\"])\nprint(f\"Applied {review_result['exemption_count']} exemptions\")\n```\n\n### Step 5: Response Generation\n\nGenerate a compliant DSAR response package with cover letter, data export,\nand supplementary information document.\n\n```python\nfrom agent import DSARResponseGenerator\n\ngenerator = DSARResponseGenerator(template_dir=\"templates\u002F\")\n\n# Generate complete response package\nresponse = generator.generate_response(\n    dsar_id=request[\"dsar_id\"],\n    data_subject=\"Jane Smith\",\n    mapped_data=redacted_data,\n    format=\"pdf\",  # or \"json\", \"csv\"\n)\n\n# Package includes: cover letter, data export, supplementary info, audit log\nfor doc in response[\"documents\"]:\n    print(f\"Generated: {doc['filename']} ({doc['type']})\")\n```\n\n### Step 6: Audit Trail and Compliance Logging\n\nMaintain complete audit trail of the DSAR lifecycle for accountability.\n\n```python\nfrom agent import DSARAuditLogger\n\nlogger = DSARAuditLogger(log_path=\"dsar_audit_logs\u002F\")\n\n# Log complete DSAR lifecycle\nlogger.log_event(request[\"dsar_id\"], \"request_received\", {\n    \"channel\": \"email\",\n    \"identity_verified\": True,\n})\nlogger.log_event(request[\"dsar_id\"], \"pii_discovery_complete\", {\n    \"records_found\": all_pii[\"total_records\"],\n    \"sources_scanned\": all_pii[\"source_count\"],\n})\nlogger.log_event(request[\"dsar_id\"], \"response_sent\", {\n    \"format\": \"pdf\",\n    \"documents_count\": len(response[\"documents\"]),\n    \"exemptions_applied\": review_result[\"exemption_count\"],\n})\n\n# Generate compliance report\ncompliance_report = logger.generate_compliance_report(request[\"dsar_id\"])\n```\n\n## Examples\n\n### Complete DSAR Processing Pipeline\n\n```python\nfrom agent import DSARWorkflowEngine, PIIDiscoveryEngine, DSARResponseGenerator\n\n# Full automated pipeline\nengine = DSARWorkflowEngine(config_path=\"dsar_config.json\")\npii = PIIDiscoveryEngine()\ngen = DSARResponseGenerator(template_dir=\"templates\u002F\")\n\n# 1. Intake\nreq = engine.register_dsar(\n    requester_name=\"John Doe\",\n    requester_email=\"john.doe@example.com\",\n    request_channel=\"web_form\",\n    request_text=\"Please provide all my data under GDPR Article 15.\",\n    identity_docs=[\"email_verified\", \"account_match\"],\n)\n\n# 2. Discover\nresults = pii.full_scan(\n    search_identifiers={\"email\": \"john.doe@example.com\"},\n    sources=[\"database\", \"files\", \"logs\"],\n)\n\n# 3. Generate response\nresponse = gen.generate_response(\n    dsar_id=req[\"dsar_id\"],\n    data_subject=\"John Doe\",\n    mapped_data=results,\n)\n\n# 4. Track deadline\nengine.update_status(req[\"dsar_id\"], \"response_sent\")\nprint(f\"DSAR {req['dsar_id']} completed, {engine.days_remaining(req['dsar_id'])} days remaining\")\n```\n\n### PII Regex Pattern Testing\n\n```python\nfrom agent import PIIPatternMatcher\n\nmatcher = PIIPatternMatcher()\n\n# Test individual patterns\ntest_text = \"Contact jane.smith@example.com or call +44 20 7946 0958. SSN: 123-45-6789\"\nmatches = matcher.scan_text(test_text)\nfor m in matches:\n    print(f\"  [{m['type']}] '{m['value']}' (confidence: {m['confidence']})\")\n```\n\n## References\n\n- GDPR Article 15: https:\u002F\u002Fgdpr-info.eu\u002Fart-15-gdpr\u002F\n- ICO Subject Access Request Guidance: https:\u002F\u002Fico.org.uk\u002Ffor-organisations\u002Fuk-gdpr-guidance-and-resources\u002Fsubject-access-requests\u002F\n- EDPB Guidelines 01\u002F2022 on Right of Access: https:\u002F\u002Fwww.edpb.europa.eu\u002Fsystem\u002Ffiles\u002F2023-04\u002Fedpb_guidelines_202201_data_subject_rights_access_v2_en.pdf\n- GDPR Article 12 (DSAR Modalities): https:\u002F\u002Fgdpr-info.eu\u002Fart-12-gdpr\u002F\n- Regulation (EU) 2025\u002F2518 (Procedural Rules): Cross-border GDPR enforcement procedural rules\n",{"name":5,"tags":63,"author":9,"domain":51,"license":15,"version":66,"nist_csf":67,"subdomain":71,"description":72,"mitre_attack":73},[40,45,64,44,41,65,43],"privacy","compliance","1.0",[68,69,70],"GV.PO-01","PR.DS-01","GV.OC-05","privacy-compliance","Automates GDPR Data Subject Access Request (DSAR) workflows including identity verification, PII discovery across databases and files using regex and NER, data mapping, response templating per Article 15 requirements, deadline tracking, and audit logging. Covers ICO\u002FEDPB guidance compliance, exemption handling, and scalable batch processing. Use when building or auditing DSAR response capabilities under GDPR\u002FUK GDPR.\n",[74,75,76],"T1078","T1190","T1059","main","54a798831d2266a3ca61ce68a7acb80b81160d57","skills\u002Fimplementing-gdpr-data-subject-access-request",4379,"https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills","https:\u002F\u002Fmahipal.engineer\u002FAnthropic-Cybersecurity-Skills\u002F","https:\u002F\u002Fgithub.com\u002Fmukul975","Mahipal.engineer","🔐 Cybersecurity | 💻 Dev | 📷 Street Photographer\r\n🎓 MSc | 📚 Research & AI Security","2026-08-31 12:32:44",{"source":8,"skillName":5,"commands":88,"prompt":98,"zipAvailable":99},[89,92,95],{"tool":90,"command":91},"npx","npx skills add mukul975\u002FAnthropic-Cybersecurity-Skills --skill implementing-gdpr-data-subject-access-request",{"tool":93,"command":94},"bunx","bunx skills add mukul975\u002FAnthropic-Cybersecurity-Skills --skill implementing-gdpr-data-subject-access-request",{"tool":96,"command":97},"pnpm","pnpm dlx skills add mukul975\u002FAnthropic-Cybersecurity-Skills --skill implementing-gdpr-data-subject-access-request","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fimplementing-gdpr-data-subject-access-request\u002FSKILL.md",true,[101,106,112],{"path":102,"resourceType":102,"resourceTypeDesc":103,"sizeBytes":104,"sourceHtmlUrl":105},"LICENSE","许可证",11255,"https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fimplementing-gdpr-data-subject-access-request\u002FLICENSE",{"path":107,"resourceType":108,"resourceTypeDesc":109,"sizeBytes":110,"sourceHtmlUrl":111},"references\u002Fapi-reference.md","REFERENCE","参考文档",11411,"https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fimplementing-gdpr-data-subject-access-request\u002Freferences\u002Fapi-reference.md",{"path":113,"resourceType":114,"resourceTypeDesc":115,"sizeBytes":116,"sourceHtmlUrl":117},"scripts\u002Fagent.py","SCRIPT","脚本",59845,"https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fimplementing-gdpr-data-subject-access-request\u002Fscripts\u002Fagent.py",[119,144,167,193,218,245],{"skillNo":120,"name":121,"nameZh":6,"description":122,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":123,"categories":124,"keywords":126,"publishedAt":58},"01M6AGDM3HJXTKXX15ZZ0ZVCJ3","verifying-build-provenance-with-slsa-sigstore","Verifies artifact signatures and SLSA provenance using Sigstore's cosign (verify, verify-attestation, verify-blob-attestation) and slsa-verifier (verify-artifact), enforcing keyless OIDC builder identity and source repo against SLSA Build levels. Use in CI\u002FCD before deploying artifacts, when consuming third-party attestations, establishing a SLSA Build L3 pipeline, or confirming provenance during incident response or admission control.","https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fverifying-build-provenance-with-slsa-sigstore\u002FSKILL.md",[125],{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},[127,128,129,130,131,51,132,52,50,133,134,135,136,137,138,139,140,141,142,143],"sigstore","provenance","slsa","verifying build provenance with slsa sigstore","security","incident response","code","artifacts","ci","supply-chain","pipeline","supply-chain-security","t1195","attestation","code-signing","cosign","keyless",{"skillNo":145,"name":146,"nameZh":6,"description":147,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":148,"categories":149,"keywords":151,"publishedAt":58},"01M6AGDGBPWYVZ2F11GFKJ4HZ9","validating-tpm-measured-boot-attestation","Verifies TPM 2.0 measured-boot integrity and remote attestation with tpm2-tools -- reading PCRs (tpm2_pcrread), replaying the boot event log, generating and checking signed quotes (tpm2_quote\u002Ftpm2_checkquote), and sealing secrets to a PCR policy. Use to confirm a system booted trusted firmware\u002Fkernel for Zero Trust device posture, detect boot-chain tampering via PCR drift, or build a golden-value baseline for fleet attestation.","https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fvalidating-tpm-measured-boot-attestation\u002FSKILL.md",[150],{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},[152,140,153,154,131,155,51,50,56,156,157,158,159,160,161,162,163,164,165,166],"boot","TPM","validating tpm measured boot attestation","policy","ZeRO","firmware","Tools","measured-boot","PCR","integrity","secrets","remote-attestation","tpm2-tools","integrity-verification","trusted-computing",{"skillNo":168,"name":169,"nameZh":6,"description":170,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":171,"categories":172,"keywords":177,"publishedAt":58},"01M6AGDCW0Y4FXZ3362GT1941M","validating-backup-integrity-for-recovery","Validates backup integrity through cryptographic hash verification, automated restore testing, corruption detection, and recoverability checks to confirm backups are reliable for disaster recovery and ransomware response. Use before relying on backups for recovery, when building post-backup validation pipelines, auditing backup infrastructure for compliance, or checking immutable\u002Fair-gapped backups for silent corruption or tampering.","https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fvalidating-backup-integrity-for-recovery\u002FSKILL.md",[173,176],{"categoryNo":174,"categoryName":175,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000008","测试与质量",{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},[161,178,179,180,181,51,182,183,56,65,184,185,186,187,188,48,189,190,191,192],"backup","recovery","validating backup integrity for recovery","testing","infrastructure","validation","hash-verification","incident-response","ransomware","cryptocurrency","detection","t1490","immutable","disaster-recovery","restore-testing",{"skillNo":194,"name":195,"nameZh":6,"description":196,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":197,"categories":198,"keywords":200,"publishedAt":58},"01M6AGD9GBG2KZ336AE763J3WH","triaging-windows-with-kape","Runs KAPE (Kroll Artifact Parser and Extractor) to collect targeted forensic artifacts (registry hives, $MFT, event logs, prefetch, browser data) via Targets and parse them with Modules wrapping Eric Zimmerman's EZ Tools (PECmd, MFTECmd, RECmd). Use during early incident containment\u002Ftriage when full disk imaging is impractical but a defensible, parseable Windows artifact set is needed quickly, including at-scale remote collection.","https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Ftriaging-windows-with-kape\u002FSKILL.md",[199],{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},[201,202,203,51,204,205,206,207,208,209,210,38,211,212,213,214,158,215,216,217],"windows","kape","triaging windows with kape","registry","browser","logs","windows-forensics","dfir","mft","mftecmd","prefetch","eric-zimmerman","digital-forensics","SET","collection","pecmd","artifact-collection",{"skillNo":219,"name":220,"nameZh":6,"description":221,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":222,"categories":223,"keywords":225,"publishedAt":58},"01M6AGD5PW5J4EVZM958WD74ZQ","triaging-vulnerabilities-with-ssvc-framework","Triages and prioritizes vulnerabilities with CISA's Stakeholder-Specific Vulnerability Categorization (SSVC) decision tree, weighing exploitation status (via the CISA KEV catalog and FIRST EPSS API), technical impact, automatability, and mission prevalence to output Track\u002FTrack*\u002FAttend\u002FAct decisions. Use when prioritizing vulnerability scan results (OpenVAS, Nessus, Qualys) for remediation planning beyond raw CVSS scores.","https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Ftriaging-vulnerabilities-with-ssvc-framework\u002FSKILL.md",[224],{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},[226,227,228,51,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244],"vulnerabilities","framework","triaging vulnerabilities with ssvc framework","status","planning","CISA","vulnerability-management","risk-management","nessus","qualys","openvas","cvss","remediation","risk","vulnerability","api","prioritization","ssvc","decision-tree",{"skillNo":246,"name":247,"nameZh":6,"description":248,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":249,"categories":250,"keywords":252,"publishedAt":58},"01M6AGD24TTQQM4CXG69E3B9TJ","triaging-security-incident","Performs initial triage of security incidents using the NIST SP 800-61r3 and SANS PICERL frameworks, classifying incident type, assigning priority by business impact, and routing to the appropriate response team. Use when a SIEM\u002FEDR alert needs human classification, concurrent alerts must be prioritized, or a user report or threat-intel IOC match requires initial incident categorization.","https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Ftriaging-security-incident\u002FSKILL.md",[251],{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},[131,253,51,254,255,256,257,258,259,260,261,48,262,189,263,264,265,266,267,268],"triaging security incident","siem","file","ioc","soc","NIST-800-61","NIST","severity","edr","execution","analysis","classification","severity-classification","incident-triage","SANS-PICERL","SOC-operations"]