Compliance Frameworks (GDPR/HIPAA in Graph)

Building a Graph RAG system for a "Hobby Project" is easy. Building one for a Hospital or a Bank is a legal challenge. You must prove to regulators that your AI isn't "Thinking with Secrets." You must align your graph architecture with international frameworks like GDPR (Privacy), HIPAA (Healthcare), and SOC2 (Security).

In this lesson, we will look at the Compliance Checklist for Graph RAG. We will learn how to map the technical features we've discussed (RBAC, Hashing, Auditing) to the legal requirements of these frameworks. We will see how a well-structured Knowledge Graph actually makes compliance Easier than text-only systems because of its inherent "Traceability."

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1. GDPR: The "Privacy by Design" Graph

Requirement: Data Minimization (Collect only what you need).

• Graph Alignment: Instead of ingesting a whole file, your Extraction Pipeline (Module 6) should be instructed to ignore "Phone Numbers" or "Birthdays" unless they are critical for the reasoning task.
• The Result: A "Lean" Graph that contains only the structural knowledge, not the PII.

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2. HIPAA: Protecting PHI (Personal Health Information)

Requirement: Technical Safeguards for data integrity and access.

• Graph Alignment: Every node representing a patient must be isolated in a High-Security Subgraph (Module 15, Lesson 1).
• The "Need to Know" Logic: The AI agent is restricted by a "Read-Only" role that preventing it from "Exporting" or "Listing" patient data, allowing it only to "Summarize" for an authorized clinician.

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3. SOC2 / ISO 27001: The Audit Trail

Requirement: Logging and Monitoring of access to sensitive data.

• Graph Alignment: Your Traversal Audit Log (Lesson 2) serves as the "Proof of Control." If an auditor asks "Has anyone accessed the Strategy node?", you can show them a graph of every (AuditRecord) connected to that node.

graph TD
    subgraph "The Compliance Shield"
    G[GDPR: Hashing/Minimization]
    H[HIPAA: Subgraph Isolation]
    S[SOC2: Traversal Auditing]
    end
    
    G & H & S --> K[(Compliant Knowledge Graph)]
    
    style K fill:#34A853,color:#fff
    style G fill:#4285F4,color:#fff

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4. Implementation: The Compliance Header in API Requests

Whenever your API makes a request to the graph, it should include a Compliance Context.

def query_with_compliance(user_context, query):
    # 1. Check permissions
    validate_role(user_context.role)
    
    # 2. Add Compliance Metadata to the transaction
    tx_meta = {
        "user": user_context.id,
        "purpose": user_context.reason_for_query, # e.g., 'Medical Treatment'
        "framework": "HIPAA"
    }
    
    # 3. Execute in the database
    return graph.run(query, metadata=tx_meta)

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5. Summary and Exercises

Compliance is the "Final Seal of Approval" for an enterprise AI system.

• Data Minimization happens during the extraction phase.
• Traceability in graphs simplifies the audit process.
• Security Controls (RBAC) must be mapped to specific legal requirements.
• Transparency: If a regulator asks "What facts lead to this answer?", the Graph can show them the exact path.

Exercises

1. Compliance Audit: You are building a bot for a "Law Firm." Which is more important: GDPR Compliance (for client names) or SOC2 Compliance (for server security)?
2. The "Minimal" Extraction: Write an LLM prompt that instructs the model to "Extract only the relationships between Companies, ignoring any mention of individual employees."
3. Visualization: Draw a "Security Boundary" around a cluster of 5 nodes. How do you describe the "Gate" to that cluster?

Congratulations! You have completed Module 15: Security, Governance, and Compliance. You have moved from "Building can I?" to "Building should I, and how?".

In Module 16: Real-World Use Cases, we will see all these principles applied in high-stakes industries.