Agent skill

implementing-gdpr-data-subject-access-request

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/EDPB guidance compliance, exemption handling, and scalable batch processing. Use when building or auditing DSAR response capabilities under GDPR/UK GDPR.

View SKILL.md on GitHub Repository
Stars 4,300
Forks 470

Install this agent skill to your Project

npx add-skill https://github.com/mukul975/Anthropic-Cybersecurity-Skills/tree/main/skills/implementing-gdpr-data-subject-access-request

SKILL.md

Implementing GDPR Data Subject Access Request (DSAR) Workflow

When to Use

  • When building automated DSAR processing pipelines for GDPR/UK GDPR compliance
  • When implementing PII discovery across structured and unstructured data sources
  • When creating response templates that satisfy Article 15 disclosure requirements
  • When auditing existing DSAR handling for regulatory compliance gaps
  • When scaling DSAR processing from manual to automated workflows

Prerequisites

  • Python 3.8+ with required dependencies (spacy, presidio-analyzer, jinja2)
  • Access to data sources where personal data resides (databases, file shares, logs)
  • Understanding of GDPR Article 15 requirements and ICO/EDPB guidance
  • Appropriate authorization and data protection officer (DPO) approval
  • Test environment with synthetic or anonymized data for validation

Background

GDPR Article 15 - Right of Access

Under GDPR Article 15, data subjects have the right to obtain from the controller:

  1. Confirmation that their personal data is being processed
  2. A copy of all personal data held about them
  3. Supplementary information including:
    • Purposes of processing
    • Categories of personal data
    • Recipients or categories of recipients
    • Retention periods or criteria to determine them
    • Right to rectification, erasure, restriction, or objection
    • Right to lodge a complaint with a supervisory authority
    • Source of the data (if not collected directly from the subject)
    • Existence of automated decision-making, including profiling

Timeline Requirements

  • Standard deadline: 1 calendar month from receipt of valid request
  • Complex extension: Up to 2 additional months (must notify within first month)
  • Clock pause: Permitted when identity verification or clarification is needed
  • Format: Electronic form if request made electronically (unless otherwise requested)
  • Cost: Free of charge (unless manifestly unfounded/excessive)

ICO/EDPB Guidance Key Points

  • No formal format required for DSARs - verbal, written, social media all valid
  • Request need not mention "subject access request" or cite Article 15
  • Identity verification must be proportionate to the risk
  • Exemptions exist for legal privilege, third-party data, trade secrets
  • EDPB coordinated enforcement actions cover right of access compliance

Instructions

Step 1: DSAR Intake and Verification

Implement a request intake system that captures the request through any channel, verifies the requester's identity, and starts the compliance clock.

python
from agent import DSARWorkflowEngine

engine = DSARWorkflowEngine(config_path="dsar_config.json")

# Register a new DSAR
request = engine.register_dsar(
    requester_name="Jane Smith",
    requester_email="jane.smith@example.com",
    request_channel="email",
    request_text="I would like a copy of all personal data you hold about me.",
    identity_docs=["passport_verified"],
)
print(f"DSAR ID: {request['dsar_id']}, Deadline: {request['deadline']}")

Step 2: PII Discovery Across Data Sources

Scan databases, files, and logs using regex patterns and NER to find all personal data associated with the data subject.

python
from agent import PIIDiscoveryEngine

pii_engine = PIIDiscoveryEngine()

# Scan structured data (database)
db_results = pii_engine.scan_database(
    connection_string="postgresql://user:pass@localhost/appdb",
    search_identifiers={"email": "jane.smith@example.com", "name": "Jane Smith"},
)

# Scan unstructured data (files, logs)
file_results = pii_engine.scan_files(
    directories=["/var/log/app", "/data/exports", "/data/documents"],
    search_identifiers={"email": "jane.smith@example.com", "name": "Jane Smith"},
)

# Scan with NER for contextual PII detection
ner_results = pii_engine.scan_with_ner(
    text_corpus=file_results["raw_text_matches"],
    entity_types=["PERSON", "EMAIL", "PHONE_NUMBER", "LOCATION", "DATE_OF_BIRTH"],
)

all_pii = pii_engine.consolidate_results(db_results, file_results, ner_results)
print(f"Found {all_pii['total_records']} PII records across {all_pii['source_count']} sources")

Step 3: Data Mapping and Classification

Map discovered PII to processing purposes, legal bases, and retention periods as required by Article 15.

python
from agent import DataMapper

mapper = DataMapper(data_inventory_path="data_inventory.json")

# Map PII to Article 15 categories
mapped_data = mapper.map_to_article15(
    pii_records=all_pii,
    data_subject_id="jane.smith@example.com",
)

# Output includes processing purposes, recipients, retention for each data category
for category in mapped_data["categories"]:
    print(f"Category: {category['name']}")
    print(f"  Purpose: {category['processing_purpose']}")
    print(f"  Legal basis: {category['legal_basis']}")
    print(f"  Retention: {category['retention_period']}")
    print(f"  Recipients: {', '.join(category['recipients'])}")

Step 4: Exemption Review

Apply exemptions where lawful (third-party data, legal privilege, trade secrets) before compiling the response.

python
from agent import ExemptionReviewer

reviewer = ExemptionReviewer()

# Check for applicable exemptions
review_result = reviewer.review_exemptions(
    mapped_data=mapped_data,
    exemption_checks=[
        "third_party_data",
        "legal_professional_privilege",
        "trade_secrets",
        "crime_prevention",
        "management_forecasting",
    ],
)

# Apply redactions where exemptions apply
redacted_data = reviewer.apply_redactions(mapped_data, review_result["exemptions"])
print(f"Applied {review_result['exemption_count']} exemptions")

Step 5: Response Generation

Generate a compliant DSAR response package with cover letter, data export, and supplementary information document.

python
from agent import DSARResponseGenerator

generator = DSARResponseGenerator(template_dir="templates/")

# Generate complete response package
response = generator.generate_response(
    dsar_id=request["dsar_id"],
    data_subject="Jane Smith",
    mapped_data=redacted_data,
    format="pdf",  # or "json", "csv"
)

# Package includes: cover letter, data export, supplementary info, audit log
for doc in response["documents"]:
    print(f"Generated: {doc['filename']} ({doc['type']})")

Step 6: Audit Trail and Compliance Logging

Maintain complete audit trail of the DSAR lifecycle for accountability.

python
from agent import DSARAuditLogger

logger = DSARAuditLogger(log_path="dsar_audit_logs/")

# Log complete DSAR lifecycle
logger.log_event(request["dsar_id"], "request_received", {
    "channel": "email",
    "identity_verified": True,
})
logger.log_event(request["dsar_id"], "pii_discovery_complete", {
    "records_found": all_pii["total_records"],
    "sources_scanned": all_pii["source_count"],
})
logger.log_event(request["dsar_id"], "response_sent", {
    "format": "pdf",
    "documents_count": len(response["documents"]),
    "exemptions_applied": review_result["exemption_count"],
})

# Generate compliance report
compliance_report = logger.generate_compliance_report(request["dsar_id"])

Examples

Complete DSAR Processing Pipeline

python
from agent import DSARWorkflowEngine, PIIDiscoveryEngine, DSARResponseGenerator

# Full automated pipeline
engine = DSARWorkflowEngine(config_path="dsar_config.json")
pii = PIIDiscoveryEngine()
gen = DSARResponseGenerator(template_dir="templates/")

# 1. Intake
req = engine.register_dsar(
    requester_name="John Doe",
    requester_email="john.doe@example.com",
    request_channel="web_form",
    request_text="Please provide all my data under GDPR Article 15.",
    identity_docs=["email_verified", "account_match"],
)

# 2. Discover
results = pii.full_scan(
    search_identifiers={"email": "john.doe@example.com"},
    sources=["database", "files", "logs"],
)

# 3. Generate response
response = gen.generate_response(
    dsar_id=req["dsar_id"],
    data_subject="John Doe",
    mapped_data=results,
)

# 4. Track deadline
engine.update_status(req["dsar_id"], "response_sent")
print(f"DSAR {req['dsar_id']} completed, {engine.days_remaining(req['dsar_id'])} days remaining")

PII Regex Pattern Testing

python
from agent import PIIPatternMatcher

matcher = PIIPatternMatcher()

# Test individual patterns
test_text = "Contact jane.smith@example.com or call +44 20 7946 0958. SSN: 123-45-6789"
matches = matcher.scan_text(test_text)
for m in matches:
    print(f"  [{m['type']}] '{m['value']}' (confidence: {m['confidence']})")

References

Recommended Agent Skills

Expand your agent's capabilities with these related and highly-rated skills.

mukul975/Anthropic-Cybersecurity-Skills

mapping-mitre-attack-techniques

Maps observed adversary behaviors, security alerts, and detection rules to MITRE ATT&CK techniques and sub-techniques to quantify detection coverage and guide control prioritization. Use when building an ATT&CK-based coverage heatmap, tagging SIEM alerts with technique IDs, aligning security controls to adversary playbooks, or reporting threat exposure to executives. Activates for requests involving ATT&CK Navigator, Sigma rules, MITRE D3FEND, or coverage gap analysis.

4,300 470
Explore
mukul975/Anthropic-Cybersecurity-Skills

hunting-for-spearphishing-indicators

Hunt for spearphishing campaign indicators across email logs, endpoint telemetry, and network data to detect targeted email attacks.

4,300 470
Explore
mukul975/Anthropic-Cybersecurity-Skills

analyzing-malicious-url-with-urlscan

URLScan.io is a free service for scanning and analyzing suspicious URLs. It captures screenshots, DOM content, HTTP transactions, JavaScript behavior, and network connections of web pages in an isolat

4,300 470
Explore
mukul975/Anthropic-Cybersecurity-Skills

implementing-zero-standing-privilege-with-cyberark

Deploy CyberArk Secure Cloud Access to eliminate standing privileges in hybrid and multi-cloud environments using just-in-time access with time, entitlement, and approval controls.

4,300 470
Explore
mukul975/Anthropic-Cybersecurity-Skills

implementing-pam-for-database-access

Deploy privileged access management for database systems including Oracle, SQL Server, PostgreSQL, and MySQL. Covers session proxy configuration, credential vaulting, query auditing, dynamic credentia

4,300 470
Explore
mukul975/Anthropic-Cybersecurity-Skills

detecting-t1003-credential-dumping-with-edr

Detect OS credential dumping techniques targeting LSASS memory, SAM database, NTDS.dit, and cached credentials using EDR telemetry, Sysmon process access monitoring, and Windows security event correlation.

4,300 470
Explore

Didn't find tool you were looking for?

Be as detailed as possible for better results