Dynamic Evidence Attribution Engine Using Graph Neural Networks

In an era where security questionnaires pile up faster than a development sprint, organizations need a smarter way to find the right piece of evidence at the right moment. Graph Neural Networks (GNNs) provide exactly that – a way to understand the hidden relationships inside your compliance knowledge graph and surface the most relevant artifacts instantly.

1. The Pain Point: Manual Evidence Hunting

Security questionnaires such as SOC 2, ISO 27001, and GDPR request evidence for dozens of controls. Traditional approaches rely on:

• Keyword search across document repositories
• Human‑curated mappings between controls and evidence
• Static rule‑based tagging

These methods are slow, error‑prone, and hard to keep up when policies or regulations change. A single missed evidence item can delay a deal, trigger compliance breaches, or erode customer trust.

2. Why Graph Neural Networks?

A compliance knowledge base is naturally a graph:

• Nodes – policies, controls, evidence documents, regulatory clauses, vendor assets.
• Edges – “covers”, “derived‑from”, “updates”, “related‑to”.

GNNs excel at learning node embeddings that capture both the attribute information (e.g., document text) and the structural context (how a node connects to the rest of the graph). When you query for a control, the GNN can rank evidence nodes that are most semantically and topologically aligned, even if the exact keywords differ.

Key advantages:

3. High‑Level Architecture

Below is a Mermaid diagram that shows how the Dynamic Evidence Attribution Engine slots into the existing Procurize workflow.

  graph LR
    A["Policy Repository"] -->|Parse & Index| B["Knowledge Graph Builder"]
    B --> C["Graph Database (Neo4j)"]
    C --> D["GNN Training Service"]
    D --> E["Node Embedding Store"]
    subgraph Procurize Core
        F["Questionnaire Manager"]
        G["Task Assignment Engine"]
        H["AI Answer Generator"]
    end
    I["User Query: Control ID"] --> H
    H --> J["Embedding Lookup (E)"]
    J --> K["Similarity Search (FAISS)"]
    K --> L["Top‑N Evidence Candidates"]
    L --> G
    G --> F
    style D fill:#f9f,stroke:#333,stroke-width:2px
    style E fill:#ff9,stroke:#333,stroke-width:2px

All node labels are wrapped in double quotes as required by Mermaid syntax.

4. Data Flow in Detail

1. Ingestion

   • Policies, control libraries, and evidence PDFs are ingested via Procurize’s connector framework.
   • Each artifact is stored in a document bucket and its metadata is extracted (title, version, tags).

2. Graph Construction

   • A knowledge‑graph builder creates nodes for each artifact and edges based on:
     • Control ↔️ Regulation mappings (e.g., ISO 27001 A.12.1 → GDPR Article 32)
     • Evidence ↔️ Control citations (parsed from PDFs using Document AI)
     • Version‑history edges (evidence v2 “updates” evidence v1)

3. Feature Generation

   • Textual content of each node is encoded with a pre‑trained LLM (e.g., mistral‑7B‑instruct) to produce a 768‑dimensional vector.
   • Structural features such as degree centrality, betweenness, and edge types are concatenated.

4. GNN Training

   • The GraphSAGE algorithm propagates neighbor information for 3‑hop neighborhoods, learning node embeddings that respect both semantics and graph topology.
   • Supervision comes from historical attribution logs: when a security analyst manually linked evidence to a control, that pair is a positive training sample.

5. Real‑Time Scoring

   • When a questionnaire item is opened, the AI Answer Generator asks the GNN service for the embedding of the target control.
   • A FAISS similarity search retrieves the nearest evidence embeddings, returning a ranked list.

6. Human‑In‑The‑Loop

   • Analysts can accept, reject, or re‑rank the suggestions. Their actions are fed back to the training pipeline, creating a continuous learning loop.

5. Integration Touchpoints with Procurize

6. Security, Privacy, and Governance

• Zero‑Knowledge Proofs (ZKP) for Evidence Retrieval – Sensitive evidence never leaves the encrypted storage; the GNN only receives hashed embeddings.
• Differential Privacy – During model training, noise is added to gradient updates to guarantee that individual evidence contributions cannot be reverse‑engineered.
• Role‑Based Access Control (RBAC) – Only users with the Evidence Analyst role can view raw documents; the UI shows only the GNN‑selected snippet.
• Explainability Dashboard – A heat‑map visualizes which edges (e.g., “covers”, “updates”) contributed most to a recommendation, satisfying audit requirements.

7. Step‑By‑Step Implementation Guide

1. Set Up the Graph Database

   docker run -d -p 7474:7474 -p 7687:7687 \
     --name neo4j \
     -e NEO4J_AUTH=neo4j/securepwd \
     neo4j:5.15
   

2. Install the Knowledge‑Graph Builder (Python package procurize-kg)

   pip install procurize-kg[neo4j,docai]
   

3. Run the Ingestion Pipeline

   kg_builder --source ./policy_repo \
              --docai-token $DOCAI_TOKEN \
              --neo4j-uri bolt://localhost:7687 \
              --neo4j-auth neo4j/securepwd
   

4. Launch the GNN Training Service (Docker‑compose)

   version: "3.8"
   services:
     gnn-trainer:
       image: procurize/gnn-trainer:latest
       environment:
         - NE04J_URI=bolt://neo4j:7687
         - NE04J_AUTH=neo4j/securepwd
         - TRAIN_EPOCHS=30
       ports:
         - "5000:5000"
   

5. Expose the Attribution API

   from fastapi import FastAPI, Query
   from gnns import EmbeddingService, SimilaritySearch
   
   app = FastAPI()
   emb_service = EmbeddingService()
   sim_search = SimilaritySearch()
   
   @app.get("/evidence/attribution")
   async def attribute(control_id: str = Query(...)):
       control_emb = await emb_service.get_embedding(control_id)
       candidates = await sim_search.top_k(control_emb, k=5)
       return {"candidates": candidates}
   

6. Connect to Procurize UI

   • Add a new panel widget that calls /evidence/attribution whenever a control card opens.
   • Display results with acceptance buttons that trigger POST /tasks/create for the selected evidence.

8. Measurable Benefits

The pilot data shows a >75 % reduction in manual effort and a significant boost in confidence for compliance reviewers.

9. Future Roadmap

1. Cross‑Tenant Knowledge Graphs – Federated learning across multiple organizations while preserving data privacy.
2. Multimodal Evidence – Combine textual PDFs with code‑snippets and configuration files via multi‑modal transformers.
3. Adaptive Prompt Marketplace – Auto‑generate LLM prompts based on GNN‑derived evidence, creating a closed‑loop answer generation pipeline.
4. Self‑Healing Graph – Detect orphaned evidence nodes and automatically suggest archiving or re‑linking.

10. Conclusion

The Dynamic Evidence Attribution Engine transforms the tedious “search‑and‑paste” ritual into a data‑driven, AI‑augmented experience. By leveraging Graph Neural Networks, organizations can:

• Accelerate questionnaire completion from minutes to seconds.
• Raise the precision of evidence recommendations, reducing audit findings.
• Maintain full auditability and explainability, satisfying regulator demands.

Integrating this engine with Procurize’s existing collaboration and workflow tools delivers a single source of truth for compliance evidence, empowering security, legal, and product teams to focus on strategy instead of paperwork.

See Also

• ISO 27001:2022 – Controls and Evidence Management Best Practices