Data Protection Skill

Purpose

This skill provides comprehensive data protection guidance for the CIA platform, covering data classification, GDPR privacy by design, encryption standards, and data lifecycle management. It ensures political intelligence data is handled according to Hack23 ISMS classification and data protection policies.

When to Use This Skill

Apply this skill when:

• ✅ Handling personal data of politicians or citizens
• ✅ Designing database schemas with sensitive fields
• ✅ Implementing data import/export functionality
• ✅ Configuring data retention or deletion policies
• ✅ Integrating external data sources (Riksdagen, World Bank)
• ✅ Processing election or voting records
• ✅ Implementing caching strategies for sensitive data

Do NOT use for:

• ❌ Public open data with no personal information
• ❌ Static UI component changes
• ❌ Build system or CI/CD pipeline changes

Data Classification Framework

CIA Platform Data Categories

Classification Levels (Hack23 ISMS)
│
├─ PUBLIC
│  ├─ Parliamentary voting records
│  ├─ Published committee documents
│  ├─ Official election results
│  └─ World Bank economic indicators
│
├─ INTERNAL
│  ├─ Aggregated analysis results
│  ├─ Risk scoring algorithms
│  ├─ Platform usage analytics
│  └─ System configuration data
│
├─ CONFIDENTIAL
│  ├─ User account credentials
│  ├─ Session tokens and API keys
│  ├─ Audit logs with user actions
│  └─ Internal security assessments
│
└─ RESTRICTED
   ├─ Database credentials
   ├─ Encryption keys (KMS)
   ├─ AWS access credentials
   └─ Security incident data

Classification Decision Tree

Data Classification Decision
│
├─→ Is it publicly available from official sources?
│   ├─ YES → PUBLIC
│   └─ NO ↓
│
├─→ Does it contain personal identifiers?
│   ├─ YES → CONFIDENTIAL (minimum)
│   └─ NO ↓
│
├─→ Is it a credential, key, or secret?
│   ├─ YES → RESTRICTED
│   └─ NO ↓
│
└─→ Is it internal analysis or configuration?
    ├─ YES → INTERNAL
    └─ NO → Assess case-by-case

GDPR Privacy by Design

Seven Principles Applied to CIA Platform

1. Proactive, not reactive — Embed privacy into political data imports
2. Privacy as default — Minimize personal data collection
3. Privacy embedded in design — Use pseudonymization where possible
4. Full functionality — Privacy without sacrificing analysis quality
5. End-to-end security — Encrypt data at rest and in transit
6. Visibility and transparency — Document all data processing activities
7. Respect for user privacy — Provide data access and deletion mechanisms

Data Processing Patterns

// ✅ SECURE: Minimize personal data in analysis
@Service
public class PoliticianAnalysisService {

    public PoliticianSummary analyzePolitician(String personId) {
        // Only retrieve fields needed for analysis
        PoliticianData data = repository.findPublicDataById(personId);

        // Never include unnecessary personal details
        return PoliticianSummary.builder()
            .personId(personId)
            .votingRecord(data.getVotingRecord())
            .committeeAssignments(data.getCommittees())
            .partyAffiliation(data.getParty())
            .build();
        // Excluded: personal address, phone, private email
    }
}

// ❌ INSECURE: Over-collection of personal data
public PoliticianData getAllPersonalData(String personId) {
    return repository.findById(personId); // Returns ALL fields
}

Data Subject Rights Implementation

Encryption Standards

Data at Rest

Encryption Requirements by Classification
│
├─ PUBLIC → No encryption required
├─ INTERNAL → AES-256 recommended
├─ CONFIDENTIAL → AES-256 required (AWS KMS)
└─ RESTRICTED → AES-256 + envelope encryption (KMS CMK)

Data in Transit

• TLS 1.2+ for all HTTP connections
• Certificate pinning for external API calls
• mTLS for internal service communication
• HSTS headers enforced

Database Encryption

// Column-level encryption for sensitive fields
@Entity
@Table(name = "application_user")
public class ApplicationUser {

    @Column(name = "username")
    private String username; // PUBLIC - no encryption

    @Column(name = "email")
    @Convert(converter = EncryptedStringConverter.class)
    private String email; // CONFIDENTIAL - encrypted

    @Column(name = "password_hash")
    private String passwordHash; // Already hashed (bcrypt)
}

Data Lifecycle Management

Retention Policies

Secure Deletion

// Implement secure deletion for user data
@Service
public class DataDeletionService {

    @Transactional
    public void deleteUserAccount(Long userId) {
        // 1. Remove personal data
        userRepository.anonymizeUser(userId);

        // 2. Delete session data
        sessionRepository.deleteByUserId(userId);

        // 3. Audit the deletion (GDPR compliance)
        auditService.logDeletion(userId, "GDPR erasure request");

        // 4. Remove from caches
        cacheManager.evict("user:" + userId);
    }
}

ISMS Alignment

References

• Hack23 ISMS Classification Policy
• Hack23 ISMS Cryptography Policy
• GDPR Full Text
• NIST SP 800-122 PII Guide