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GDPR Compliance Guide

This guide helps compliance officers configure Lucid to meet the requirements of the General Data Protection Regulation (GDPR) for AI systems processing personal data of EU residents.

Overview

The GDPR establishes comprehensive data protection requirements for organizations processing personal data of EU residents. When AI systems process personal data, they must comply with principles including lawfulness, purpose limitation, data minimization, accuracy, storage limitation, integrity/confidentiality, and accountability.

Lucid helps organizations meet these requirements through:

  • Automated PII detection to identify personal data in AI inputs and outputs
  • Data sovereignty controls to ensure processing occurs in GDPR-compliant jurisdictions
  • Comprehensive audit logging to demonstrate accountability
  • Bias and fairness evaluation to support automated decision-making safeguards

Key GDPR Articles and Lucid Auditors

GDPR Article Requirement Recommended Auditor
Art. 5 Data processing principles PII Compliance Auditor (data minimization), Observability Auditor (accountability)
Art. 13-14 Information to data subjects Eval Auditor (explainability)
Art. 22 Automated decision-making safeguards Eval Auditor (bias detection, explainability), Fairness Auditor
Art. 25 Data protection by design PII Compliance Auditor (PII detection), Guardrails Auditor
Art. 30 Records of processing activities Observability Auditor
Art. 32 Security of processing Guardrails Auditor, Secrets Auditor (credential detection)
Art. 35 Data protection impact assessment Red Team Auditor
Art. 44-49 International data transfers Sovereignty Auditor

Deploying for GDPR Compliance

Quick Start

Deploy an AI environment with the GDPR compliance profile:

lucid apply --app open-webui --model llama-3.1-8b --profile gdpr

This enables the following auditors: - PII Compliance Auditor - PII detection and data classification - Observability Auditor - Audit logging for accountability - Sovereignty Auditor - EU data residency verification - Eval Auditor - Bias detection and explainability - Fairness Auditor - Bias and discrimination prevention - Guardrails Auditor - Security measures per Art. 32

Custom Configuration

For more control, create a YAML configuration file:

# gdpr-environment.yaml
apiVersion: lucid.io/v1alpha1
kind: LucidEnvironment
metadata:
  name: gdpr-compliant-ai
spec:
  infrastructure:
    provider: gcp
    region: europe-west1  # EU region for data residency
  agents:
    - name: gdpr-agent
      model:
        id: meta-llama/Llama-3.1-8B
      gpu:
        type: L4
        memory: 24GB
      auditChain:
        preRequest:
          - auditorId: lucid-guardrails-auditor
            name: Security Measures (Art. 32)
            env:
              INJECTION_BLOCK_ON_DETECTION: "true"
          - auditorId: lucid-pii-compliance-auditor
            name: PII Detection (Art. 5, 25)
            env:
              PII_DETECTION_ENABLED: "true"
              PII_REDACT_ON_DETECTION: "true"
          - auditorId: lucid-policy-auditor
            name: Credential Detection (Art. 32)
            env:
              CREDENTIAL_DETECTION_ENABLED: "true"
          - auditorId: lucid-sovereignty-auditor
            name: Data Residency (Art. 44-49)
            env:
              ALLOWED_REGIONS: "EU,EEA"
              SOVEREIGNTY_STRICT_MODE: "true"
        postResponse:
          - auditorId: lucid-observability-auditor
            name: Processing Records (Art. 30)
            env:
              LOG_RETENTION_DAYS: "2555"  # 7 years for compliance
              LOG_PII_EVENTS: "true"
          - auditorId: lucid-eval-auditor
            name: Fairness Evaluation (Art. 22)
            env:
              EXPLAINABILITY_ENABLED: "true"
          - auditorId: lucid-fairness-auditor
            name: Bias Detection (Art. 22)
            env:
              BIAS_DETECTION_ENABLED: "true"

Deploy with:

lucid apply -f gdpr-environment.yaml

Article-by-Article Guidance

Article 5: Principles Relating to Processing

Requirement: Personal data must be processed lawfully, fairly, transparently, and with purpose limitation, data minimization, accuracy, storage limitation, and integrity/confidentiality.

Lucid Implementation:

  1. PII Compliance Auditor - Detects personal data to support data minimization
  2. Automatically identifies PII in prompts and responses

  3. Can redact unnecessary personal data before model processing

  4. Observability Auditor - Provides accountability records

  5. Logs all processing activities with timestamps
  6. Creates audit trail demonstrating compliance
env:
  PII_DETECTION_ENABLED: "true"
  PII_CATEGORIES: "name,email,phone,ssn,address,financial"

Article 13-14: Information to Data Subjects

Requirement: Data subjects must receive meaningful information about automated decision-making, including the logic involved.

Lucid Implementation:

  1. Eval Auditor - Provides explainability capabilities
  2. Documents model capabilities and limitations
  3. Generates explanations for AI-assisted decisions

The AI Passport generated for each inference can serve as documentation of the processing logic applied.

Article 22: Automated Decision-Making

Requirement: Data subjects have the right not to be subject to decisions based solely on automated processing that significantly affect them, with safeguards including human intervention.

Lucid Implementation:

  1. Fairness Auditor - Detects bias and supports human oversight
  2. Runs fairness benchmarks to identify potential discrimination

  3. Provides transparency into model behavior

  4. Eval Auditor - Provides explainability support

env:
  BIAS_DETECTION_ENABLED: "true"
  FAIRNESS_METRICS: "demographic_parity,equalized_odds"
  FLAG_HIGH_IMPACT_DECISIONS: "true"

Article 25: Data Protection by Design

Requirement: Implement appropriate technical measures to ensure data protection principles are embedded in processing.

Lucid Implementation:

  1. PII Compliance Auditor - Built-in PII protection
  2. Guardrails Auditor - Security by design against prompt attacks
  3. All auditors execute in hardware-secured enclaves (TEEs), providing technical protection by design

Article 30: Records of Processing Activities

Requirement: Maintain records of processing activities including purposes, data categories, recipients, and security measures.

Lucid Implementation:

  1. Observability Auditor - Comprehensive processing records
  2. Logs all AI system activities
  3. Records include timestamps, user identifiers (pseudonymized), and processing outcomes
# Export processing records for compliance documentation
lucid passport export --from 2024-01-01 --to 2024-03-31 --format json > art30_records.json

Article 32: Security of Processing

Requirement: Implement appropriate technical measures to ensure security appropriate to the risk.

Lucid Implementation:

  1. Guardrails Auditor - Defends against prompt injection attacks
  2. Secrets Auditor - Credential and secret detection
  3. TEE Execution - All processing occurs in hardware-secured enclaves
env:
  INJECTION_THRESHOLD: "0.8"
  INJECTION_BLOCK_ON_DETECTION: "true"
  CREDENTIAL_DETECTION_ENABLED: "true"
  CREDENTIAL_BLOCK_ON_DETECTION: "true"

Article 35: Data Protection Impact Assessment

Requirement: Conduct impact assessments for high-risk processing activities.

Lucid Implementation:

  1. Red Team Auditor - Pre-deployment safety testing
  2. Red team testing identifies potential risks

  3. Adversarial testing documents model vulnerabilities

  4. Eval Auditor - Safety benchmarks

  5. Safety benchmarks document model behavior

The evaluation results from the Red Team and Eval Auditors can be incorporated into your DPIA documentation.

Articles 44-49: International Data Transfers

Requirement: Personal data transfers outside the EU/EEA must have appropriate safeguards.

Lucid Implementation:

  1. Sovereignty Auditor - Data residency verification
  2. Verifies processing location via hardware attestation
  3. Can restrict processing to EU/EEA regions only
env:
  ALLOWED_REGIONS: "EU,EEA"
  SOVEREIGNTY_STRICT_MODE: "true"
  BLOCK_CROSS_BORDER_TRANSFER: "true"

Evidence for Compliance Assessments

Demonstrating Compliance to Supervisory Authorities

Lucid provides verifiable evidence for GDPR compliance:

  1. AI Passports - Cryptographic certificates for each inference showing:
  2. Which auditors were applied
  3. What controls were enforced

  4. Hardware attestation proving secure execution

  5. Audit Logs - Comprehensive records of all processing activities

  6. Policy Documentation - Machine-readable policies that map to GDPR articles

# Generate compliance report
lucid passport list --from 2024-01-01 --to 2024-03-31

# View specific passport details
lucid passport show <passport-id>

# Export for supervisory authority review
lucid passport export --format pdf --compliance-report gdpr

For Your Data Protection Officer

The DPO can use Lucid's outputs to:

  1. Document processing activities (Art. 30) using Observability Auditor logs
  2. Demonstrate security measures (Art. 32) via AI Passports
  3. Support DPIA documentation (Art. 35) with Eval Auditor results
  4. Verify data residency (Art. 44-49) through Sovereignty Auditor attestations

Best Practices

  1. Enable all recommended auditors - The GDPR profile provides comprehensive coverage
  2. Configure PII redaction - Automatically remove unnecessary personal data
  3. Use EU regions - Deploy in europe-west1, europe-west4, or similar EU regions
  4. Retain logs appropriately - Configure retention periods per your legal requirements
  5. Review AI Passports regularly - Monitor for any compliance issues
  6. Document your configuration - Keep your YAML files as part of your compliance documentation