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/gdpr-data-handling

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by Seth Hobsonwshobson/agents40k stars
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Implement GDPR-compliant data handling with consent management, data subject rights, and privacy by design. Use when building systems that process EU personal data, implementing privacy controls, or conducting GDPR compliance reviews.

Use this Skill: https://skilld.dev/gh/wshobson/agents/gdpr-data-handling

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referencesdetails.md

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gdpr-data-handling — detailed worked examples

Implementation Patterns

Pattern 1: Consent Management

// Consent data model
const consentSchema = {
  userId: String,
  consents: [
    {
      purpose: String, // 'marketing', 'analytics', etc.
      granted: Boolean,
      timestamp: Date,
      source: String, // 'web_form', 'api', etc.
      version: String, // Privacy policy version
      ipAddress: String, // For proof
      userAgent: String, // For proof
    },
  ],
  auditLog: [
    {
      action: String, // 'granted', 'withdrawn', 'updated'
      purpose: String,
      timestamp: Date,
      source: String,
    },
  ],
};

// Consent service
class ConsentManager {
  async recordConsent(userId, purpose, granted, metadata) {
    const consent = {
      purpose,
      granted,
      timestamp: new Date(),
      source: metadata.source,
      version: await this.getCurrentPolicyVersion(),
      ipAddress: metadata.ipAddress,
      userAgent: metadata.userAgent,
    };

    // Store consent
    await this.db.consents.updateOne(
      { userId },
      {
        $push: {
          consents: consent,
          auditLog: {
            action: granted ? "granted" : "withdrawn",
            purpose,
            timestamp: consent.timestamp,
            source: metadata.source,
          },
        },
      },
      { upsert: true },
    );

    // Emit event for downstream systems
    await this.eventBus.emit("consent.changed", {
      userId,
      purpose,
      granted,
      timestamp: consent.timestamp,
    });
  }

  async hasConsent(userId, purpose) {
    const record = await this.db.consents.findOne({ userId });
    if (!record) return false;

    const latestConsent = record.consents
      .filter((c) => c.purpose === purpose)
      .sort((a, b) => b.timestamp - a.timestamp)[0];

    return latestConsent?.granted === true;
  }

  async getConsentHistory(userId) {
    const record = await this.db.consents.findOne({ userId });
    return record?.auditLog || [];
  }
}
<!-- GDPR-compliant consent UI -->
<div class="consent-banner" role="dialog" aria-labelledby="consent-title">
  <h2 id="consent-title">Cookie Preferences</h2>

  <p>
    We use cookies to improve your experience. Select your preferences below.
  </p>

  <form id="consent-form">
    <!-- Necessary - always on, no consent needed -->
    <div class="consent-category">
      <input type="checkbox" id="necessary" checked disabled />
      <label for="necessary">
        <strong>Necessary</strong>
        <span>Required for the website to function. Cannot be disabled.</span>
      </label>
    </div>

    <!-- Analytics - requires consent -->
    <div class="consent-category">
      <input type="checkbox" id="analytics" name="analytics" />
      <label for="analytics">
        <strong>Analytics</strong>
        <span>Help us understand how you use our site.</span>
      </label>
    </div>

    <!-- Marketing - requires consent -->
    <div class="consent-category">
      <input type="checkbox" id="marketing" name="marketing" />
      <label for="marketing">
        <strong>Marketing</strong>
        <span>Personalized ads based on your interests.</span>
      </label>
    </div>

    <div class="consent-actions">
      <button type="button" id="accept-all">Accept All</button>
      <button type="button" id="reject-all">Reject All</button>
      <button type="submit">Save Preferences</button>
    </div>

    <p class="consent-links">
      <a href="/privacy-policy">Privacy Policy</a> |
      <a href="/cookie-policy">Cookie Policy</a>
    </p>
  </form>
</div>

Pattern 2: Data Subject Access Request (DSAR)

from datetime import datetime, timedelta
from typing import Dict, List, Optional
import json

class DSARHandler:
    """Handle Data Subject Access Requests."""

    RESPONSE_DEADLINE_DAYS = 30
    EXTENSION_ALLOWED_DAYS = 60  # For complex requests

    def __init__(self, data_sources: List['DataSource']):
        self.data_sources = data_sources

    async def submit_request(
        self,
        request_type: str,  # 'access', 'erasure', 'rectification', 'portability'
        user_id: str,
        verified: bool,
        details: Optional[Dict] = None
    ) -> str:
        """Submit a new DSAR."""
        request = {
            'id': self.generate_request_id(),
            'type': request_type,
            'user_id': user_id,
            'status': 'pending_verification' if not verified else 'processing',
            'submitted_at': datetime.utcnow(),
            'deadline': datetime.utcnow() + timedelta(days=self.RESPONSE_DEADLINE_DAYS),
            'details': details or {},
            'audit_log': [{
                'action': 'submitted',
                'timestamp': datetime.utcnow(),
                'details': 'Request received'
            }]
        }

        await self.db.dsar_requests.insert_one(request)
        await self.notify_dpo(request)

        return request['id']

    async def process_access_request(self, request_id: str) -> Dict:
        """Process a data access request."""
        request = await self.get_request(request_id)

        if request['type'] != 'access':
            raise ValueError("Not an access request")

        # Collect data from all sources
        user_data = {}
        for source in self.data_sources:
            try:
                data = await source.get_user_data(request['user_id'])
                user_data[source.name] = data
            except Exception as e:
                user_data[source.name] = {'error': str(e)}

        # Format response
        response = {
            'request_id': request_id,
            'generated_at': datetime.utcnow().isoformat(),
            'data_categories': list(user_data.keys()),
            'data': user_data,
            'retention_info': await self.get_retention_info(),
            'processing_purposes': await self.get_processing_purposes(),
            'third_party_recipients': await self.get_recipients()
        }

        # Update request status
        await self.update_request(request_id, 'completed', response)

        return response

    async def process_erasure_request(self, request_id: str) -> Dict:
        """Process a right to erasure request."""
        request = await self.get_request(request_id)

        if request['type'] != 'erasure':
            raise ValueError("Not an erasure request")

        results = {}
        exceptions = []

        for source in self.data_sources:
            try:
                # Check for legal exceptions
                can_delete, reason = await source.can_delete(request['user_id'])

                if can_delete:
                    await source.delete_user_data(request['user_id'])
                    results[source.name] = 'deleted'
                else:
                    exceptions.append({
                        'source': source.name,
                        'reason': reason  # e.g., 'legal retention requirement'
                    })
                    results[source.name] = f'retained: {reason}'
            except Exception as e:
                results[source.name] = f'error: {str(e)}'

        response = {
            'request_id': request_id,
            'completed_at': datetime.utcnow().isoformat(),
            'results': results,
            'exceptions': exceptions
        }

        await self.update_request(request_id, 'completed', response)

        return response

    async def process_portability_request(self, request_id: str) -> bytes:
        """Generate portable data export."""
        request = await self.get_request(request_id)
        user_data = await self.process_access_request(request_id)

        # Convert to machine-readable format (JSON)
        portable_data = {
            'export_date': datetime.utcnow().isoformat(),
            'format_version': '1.0',
            'data': user_data['data']
        }

        return json.dumps(portable_data, indent=2, default=str).encode()

Pattern 3: Data Retention

from datetime import datetime, timedelta
from enum import Enum

class RetentionBasis(Enum):
    CONSENT = "consent"
    CONTRACT = "contract"
    LEGAL_OBLIGATION = "legal_obligation"
    LEGITIMATE_INTEREST = "legitimate_interest"

class DataRetentionPolicy:
    """Define and enforce data retention policies."""

    POLICIES = {
        'user_account': {
            'retention_period_days': 365 * 3,  # 3 years after last activity
            'basis': RetentionBasis.CONTRACT,
            'trigger': 'last_activity_date',
            'archive_before_delete': True
        },
        'transaction_records': {
            'retention_period_days': 365 * 7,  # 7 years for tax
            'basis': RetentionBasis.LEGAL_OBLIGATION,
            'trigger': 'transaction_date',
            'archive_before_delete': True,
            'legal_reference': 'Tax regulations require 7 year retention'
        },
        'marketing_consent': {
            'retention_period_days': 365 * 2,  # 2 years
            'basis': RetentionBasis.CONSENT,
            'trigger': 'consent_date',
            'archive_before_delete': False
        },
        'support_tickets': {
            'retention_period_days': 365 * 2,
            'basis': RetentionBasis.LEGITIMATE_INTEREST,
            'trigger': 'ticket_closed_date',
            'archive_before_delete': True
        },
        'analytics_data': {
            'retention_period_days': 365,  # 1 year
            'basis': RetentionBasis.CONSENT,
            'trigger': 'collection_date',
            'archive_before_delete': False,
            'anonymize_instead': True
        }
    }

    async def apply_retention_policies(self):
        """Run retention policy enforcement."""
        for data_type, policy in self.POLICIES.items():
            cutoff_date = datetime.utcnow() - timedelta(
                days=policy['retention_period_days']
            )

            if policy.get('anonymize_instead'):
                await self.anonymize_old_data(data_type, cutoff_date)
            else:
                if policy.get('archive_before_delete'):
                    await self.archive_data(data_type, cutoff_date)
                await self.delete_old_data(data_type, cutoff_date)

            await self.log_retention_action(data_type, cutoff_date)

    async def anonymize_old_data(self, data_type: str, before_date: datetime):
        """Anonymize data instead of deleting."""
        # Example: Replace identifying fields with hashes
        if data_type == 'analytics_data':
            await self.db.analytics.update_many(
                {'collection_date': {'$lt': before_date}},
                {'$set': {
                    'user_id': None,
                    'ip_address': None,
                    'device_id': None,
                    'anonymized': True,
                    'anonymized_date': datetime.utcnow()
                }}
            )

Pattern 4: Privacy by Design

class PrivacyFirstDataModel:
    """Example of privacy-by-design data model."""

    # Separate PII from behavioral data
    user_profile_schema = {
        'user_id': str,  # UUID, not sequential
        'email_hash': str,  # Hashed for lookups
        'created_at': datetime,
        # Minimal data collection
        'preferences': {
            'language': str,
            'timezone': str
        }
    }

    # Encrypted at rest
    user_pii_schema = {
        'user_id': str,
        'email': str,  # Encrypted
        'name': str,   # Encrypted
        'phone': str,  # Encrypted (optional)
        'address': dict,  # Encrypted (optional)
        'encryption_key_id': str
    }

    # Pseudonymized behavioral data
    analytics_schema = {
        'session_id': str,  # Not linked to user_id
        'pseudonym_id': str,  # Rotating pseudonym
        'events': list,
        'device_category': str,  # Generalized, not specific
        'country': str,  # Not city-level
    }

class DataMinimization:
    """Implement data minimization principles."""

    @staticmethod
    def collect_only_needed(form_data: dict, purpose: str) -> dict:
        """Filter form data to only fields needed for purpose."""
        REQUIRED_FIELDS = {
            'account_creation': ['email', 'password'],
            'newsletter': ['email'],
            'purchase': ['email', 'name', 'address', 'payment'],
            'support': ['email', 'message']
        }

        allowed = REQUIRED_FIELDS.get(purpose, [])
        return {k: v for k, v in form_data.items() if k in allowed}

    @staticmethod
    def generalize_location(ip_address: str) -> str:
        """Generalize IP to country level only."""
        import geoip2.database
        reader = geoip2.database.Reader('GeoLite2-Country.mmdb')
        try:
            response = reader.country(ip_address)
            return response.country.iso_code
        except:
            return 'UNKNOWN'

Pattern 5: Breach Notification

from datetime import datetime
from enum import Enum

class BreachSeverity(Enum):
    LOW = "low"
    MEDIUM = "medium"
    HIGH = "high"
    CRITICAL = "critical"

class BreachNotificationHandler:
    """Handle GDPR breach notification requirements."""

    AUTHORITY_NOTIFICATION_HOURS = 72
    AFFECTED_NOTIFICATION_REQUIRED_SEVERITY = BreachSeverity.HIGH

    async def report_breach(
        self,
        description: str,
        data_types: List[str],
        affected_count: int,
        severity: BreachSeverity
    ) -> dict:
        """Report and handle a data breach."""
        breach = {
            'id': self.generate_breach_id(),
            'reported_at': datetime.utcnow(),
            'description': description,
            'data_types_affected': data_types,
            'affected_individuals_count': affected_count,
            'severity': severity.value,
            'status': 'investigating',
            'timeline': [{
                'event': 'breach_reported',
                'timestamp': datetime.utcnow(),
                'details': description
            }]
        }

        await self.db.breaches.insert_one(breach)

        # Immediate notifications
        await self.notify_dpo(breach)
        await self.notify_security_team(breach)

        # Authority notification required within 72 hours
        if self.requires_authority_notification(severity, data_types):
            breach['authority_notification_deadline'] = (
                datetime.utcnow() + timedelta(hours=self.AUTHORITY_NOTIFICATION_HOURS)
            )
            await self.schedule_authority_notification(breach)

        # Affected individuals notification
        if severity.value in [BreachSeverity.HIGH.value, BreachSeverity.CRITICAL.value]:
            await self.schedule_individual_notifications(breach)

        return breach

    def requires_authority_notification(
        self,
        severity: BreachSeverity,
        data_types: List[str]
    ) -> bool:
        """Determine if supervisory authority must be notified."""
        # Always notify for sensitive data
        sensitive_types = ['health', 'financial', 'credentials', 'biometric']
        if any(t in sensitive_types for t in data_types):
            return True

        # Notify for medium+ severity
        return severity in [BreachSeverity.MEDIUM, BreachSeverity.HIGH, BreachSeverity.CRITICAL]

    async def generate_authority_report(self, breach_id: str) -> dict:
        """Generate report for supervisory authority."""
        breach = await self.get_breach(breach_id)

        return {
            'organization': {
                'name': self.config.org_name,
                'contact': self.config.dpo_contact,
                'registration': self.config.registration_number
            },
            'breach': {
                'nature': breach['description'],
                'categories_affected': breach['data_types_affected'],
                'approximate_number_affected': breach['affected_individuals_count'],
                'likely_consequences': self.assess_consequences(breach),
                'measures_taken': await self.get_remediation_measures(breach_id),
                'measures_proposed': await self.get_proposed_measures(breach_id)
            },
            'timeline': breach['timeline'],
            'submitted_at': datetime.utcnow().isoformat()
        }

Compliance Checklist

## GDPR Implementation Checklist

### Legal Basis

- [ ] Documented legal basis for each processing activity
- [ ] Consent mechanisms meet GDPR requirements
- [ ] Legitimate interest assessments completed

### Transparency

- [ ] Privacy policy is clear and accessible
- [ ] Processing purposes clearly stated
- [ ] Data retention periods documented

### Data Subject Rights

- [ ] Access request process implemented
- [ ] Erasure request process implemented
- [ ] Portability export available
- [ ] Rectification process available
- [ ] Response within 30-day deadline

### Security

- [ ] Encryption at rest implemented
- [ ] Encryption in transit (TLS)
- [ ] Access controls in place
- [ ] Audit logging enabled

### Breach Response

- [ ] Breach detection mechanisms
- [ ] 72-hour notification process
- [ ] Breach documentation system

### Documentation

- [ ] Records of processing activities (Art. 30)
- [ ] Data protection impact assessments
- [ ] Data processing agreements with vendors

Source: SKILL.md on GitHub

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  • Gen Agent Trust Hub16d

    This skill provides safe and instructional documentation for implementing GDPR-compliant systems, including patterns for consent management and data subject request handling. No malicious instructions, obfuscation, or security risks were identified.

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    1 alert: gptAnomaly

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    Risk: LOW · No issues

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    Score: 93/100 · 2 sections analyzed

Signed by skilld at be57c0b. This ties the file your Agent reads to that commit on GitHub. It does not review the instructions.

Last checked against GitHub 3 days ago.

Activeupdated 4 months ago
  • gdpr
  • privacy
  • compliance
  • consent-management
  • data-protection
  • eu-regulation
  • dsar
  • encryption
  • access-control
  • data-retention

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README badge for wshobson/agents/gdpr-data-handling

Implements GDPR-compliant data handling workflows including consent management, data subject rights fulfillment, and privacy controls. Covers legal bases for processing, personal data categories, retention policies, and audit patterns for EU personal data systems.

Generated from the current SKILL.md.

Does this skill cover consent management implementation?
Yes. The skill includes consent management as a core topic, covering how to structure lawful consent under Article 6, how to avoid pre-checked boxes, and how to keep consent separate by purpose.
What data subject rights does this skill address?
It covers the six main rights under GDPR: access, rectification, erasure, restriction, portability, and objection. It notes that responses must be completed within one month.
Does this skill apply to non-EU data processing?
No. The skill is specific to EU personal data and GDPR compliance. It does not address CCPA, LGPD, or other regional privacy regimes.
Does this provide code or just compliance guidance?
It provides practical patterns and best practices for implementing GDPR controls, including documentation of processing activities, encryption, access controls, and audit approaches. Detailed implementation patterns are in a referenced details.md file.

Generated from the current SKILL.md. These answers refresh after source changes.