AI Security Compliance for Enterprise: SOC 2, GDPR, and the EU AI Act

Enterprise adoption of LLMs is accelerating, but so are regulatory requirements. Organizations must navigate SOC 2, GDPR, HIPAA, and the landmark EU AI Act. This guide helps you build compliant LLM applications without sacrificing innovation.

The AI Compliance Landscape

Existing Frameworks Applied to AI

• SOC 2: Security, availability, and confidentiality controls for AI services
• GDPR: Data protection for AI processing EU citizen data, including automated decision-making rights
• HIPAA: PHI protection for healthcare AI applications
• PCI DSS: Payment data security for AI in financial services
• ISO 27001: Information security management including AI systems

EU AI Act Requirements

The EU AI Act introduces risk-based requirements:

• Prohibited AI: Social scoring, manipulative AI, biometric categorization
• High-Risk AI: Employment, credit, healthcare decisions require extensive documentation
• Limited Risk: Chatbots and deepfakes need transparency measures
• General Purpose AI: Foundation models have specific disclosure requirements

Key Compliance Requirements

Data Protection

• Data Minimization: Only process necessary data in prompts and contexts
• Purpose Limitation: Use data only for stated purposes
• Storage Limitation: Define retention policies for conversation logs
• Access Controls: Restrict who can access LLM interactions
• Data Subject Rights: Enable deletion requests for AI-processed data
• Cross-Border Transfers: Ensure model providers comply with data residency

Security Controls

• Input validation and prompt injection prevention
• Output filtering and content moderation
• Encryption in transit and at rest
• Comprehensive audit logging
• Incident response procedures for AI-related breaches
• Regular security testing and red teaming

Transparency and Explainability

• Disclose AI use to affected individuals
• Explain how AI influences decisions
• Provide mechanisms for human review
• Document system capabilities and limitations
• Maintain records of AI system changes

Building a Compliance Program

1. AI Inventory and Risk Classification

• Document all LLM deployments and their purposes
• Classify risk level under EU AI Act categories
• Identify data processed and decisions influenced
• Map to applicable regulatory frameworks

2. Control Implementation

• Technical: Security tools, monitoring, access controls
• Administrative: Policies, procedures, training
• Human Oversight: Review processes for AI decisions
• Vendor Management: Assess AI service providers

3. Documentation and Evidence

• Maintain technical documentation for high-risk systems
• Keep audit logs for compliance evidence
• Document security testing results
• Record model versions and changes

4. Continuous Monitoring

• Regular security testing and red teaming
• Audit log review and anomaly detection
• Incident tracking and response
• Regulatory update monitoring

Zero Trust for AI

Implement Zero Trust principles for AI systems:

• Never Trust, Always Verify: Validate all inputs and outputs
• Least Privilege: Minimize AI access to data and functions
• Assume Breach: Design for containment of compromised AI
• Continuous Verification: Monitor AI behavior for anomalies

Prompt Guardrails for Compliance

Our platform provides compliance-enabling features:

• Security Controls: Prompt injection prevention, output filtering
• Audit Logging: Comprehensive logs for compliance evidence
• Testing Documentation: Red team results for auditors
• Policy Enforcement: Automated security requirement validation

Conclusion

AI compliance requires a comprehensive approach combining technical controls, governance processes, and continuous monitoring. As regulations like the EU AI Act take full effect, organizations must proactively implement robust security and documentation practices. The investment in compliance infrastructure today positions your organization to scale AI adoption responsibly.