implementing-gdpr-data-subject-access-request

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.

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.

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.

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.

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.

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.

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

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

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

• GDPR Article 15: https://gdpr-info.eu/art-15-gdpr/
• ICO Subject Access Request Guidance: https://ico.org.uk/for-organisations/uk-gdpr-guidance-and-resources/subject-access-requests/
• EDPB Guidelines 01/2022 on Right of Access: https://www.edpb.europa.eu/system/files/2023-04/edpb_guidelines_202201_data_subject_rights_access_v2_en.pdf
• GDPR Article 12 (DSAR Modalities): https://gdpr-info.eu/art-12-gdpr/
• Regulation (EU) 2025/2518 (Procedural Rules): Cross-border GDPR enforcement procedural rules