Regulatory Compliance: Building a Rule Graph

Most companies treat "Compliance" as a static PDF document that nobody reads. But in a highly regulated industry (GCP, HIPAA, AML), the rules are Dynamic. A new rule from the SEC might conflict with an old rule from the GDPR. To survive, a company needs a Rule Graph. Graph RAG turns thousands of pages of "Legalese" into a Logic Map that can be queried in real-time.

In this lesson, we will look at how to build a Reg-Graph. We will learn how to extract [:MANDATES], [:FORBIDS], and [:EXEMPTS] relationships from regulatory text. We will see how an AI agent can answer: "According to our 5 overlapping regulatory bodies, is this specific data transfer legal today?"

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1. The Reg-Graph Schema

• (:Regulation) (The Law/Rule book)
• (:Rule) (A specific clause within the regulation)
• (:Business_Activity) (The action we want to take)
• (:Conflict) (A logical contradiction between two rules)

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2. The Logic of "Compliance Checking"

1. Tag the Activity: The business action (e.g., "Transferring European data to a US server") is matched to a node.
2. Traverse for Rules: The AI finds all Rule nodes that [:GOVERN] that specific Activity.
3. Evaluate Mandates: The AI checks for any [:FORBIDS] edges.

The Benefit: Instead of a lawyer spending 40 hours reading, the graph provides an "Instant Risk Assessment" based on the exact path of the law.

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3. Detecting "Regulatory Conflict" (The Overlap Problem)

Sometimes Regulation A says "Keep data for 5 years" and Regulation B says "Delete data after 2 years." In our graph, this is a Conflict Node.

• (Rule_A)-[:CONFLICTS_WITH]->(Rule_B).
• If the AI detects a conflict in its context window, it flags it for a "Human Policy Officer" to resolve, preventing a legal mistake.

graph TD
    R1[Reg: GDPR] -->|Rule 17| RA[Activity: Deletion]
    R2[Reg: Finance Law] -->|Rule 88| RB[Activity: Retention]
    RA ---|CONFLICT| RB
    
    style RA fill:#f4b400,color:#fff
    style RB fill:#f44336,color:#fff
    note[The AI identifies the contradiction before the business acts]

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4. Implementation: Checking for Forbidden Activities in Cypher

MATCH (a:BusinessActivity {name: $activity})-[:GOVERNED_BY]->(r:Rule)
WHERE r.status = 'FORBIDDEN'
RETURN r.description, r.source_regulation;

// This query tells the AI if an activity is explicitly 'Forbidden'
// by any rule in our knowledge base.

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5. Summary and Exercises

Strategic Compliance Graph RAG is the "Automated Lawyer."

• Activity Binding links business reality to the legal rules.
• Mandate Traversal identifies what must be done and what cannot.
• Conflict Detection prevents the business from breaking one law while trying to follow another.
• Traceability: Every "Yes/No" from the AI can be linked back to the specific Clause and Page of the original regulation.

Exercises

1. Rule Mapping: You have a rule: "No employee can take home a company laptop." How would you represent this as a (Person)-[:FORBIDDEN]->(Activity) graph?
2. The "Exemption" Problem: If a rule says "Laptop taking is forbidden" BUT another rule says "Exemption: During a Pandemic," how does the AI resolve this? (Hint: See 'Temporal Reasoning' in Module 15).
3. Visualization: Draw a "Business Activity" node. Surround it with 3 "Regulation" nodes. Draw lines of different colors for "Permitted," "Forbidden," and "Mandated."

In the final lesson of this module, we will look at scientific vertical data: Scientific Discovery: Linking Hypothesis and Data.