Zero Trust Federated Knowledge Graph for Multi Tenant Questionnaire Automation

Introduction

Security and compliance questionnaires are a persistent bottleneck for SaaS vendors. Each vendor must answer hundreds of questions that span multiple frameworks—SOC 2, ISO 27001, GDPR, and industry‑specific standards. The manual effort required to locate evidence, validate its relevance, and tailor answers for each customer quickly becomes a cost center.

A federated knowledge graph (FKG)—a distributed, schema‑rich representation of evidence, policies, and controls—offers a way to break that bottleneck. When paired with zero‑trust security, the FKG can safely serve many tenants (different business units, subsidiaries, or partner organizations) without ever exposing data that belongs to another tenant. The result is a multi‑tenant, AI‑driven questionnaire automation engine that:

• Aggregates evidence from disparate repositories (Git, cloud storage, CMDBs).
• Enforces strict access policies at the node and edge level (zero‑trust).
• Orchestrates AI‑generated answers via Retrieval‑Augmented Generation (RAG) that draw only from tenant‑allowed knowledge.
• Tracks provenance and auditability through an immutable ledger.

In this article we dive deep into the architecture, data flow, and implementation steps for building such a system on top of the Procurize AI platform.

1. Core Concepts

2. Architectural Overview

Below is a high‑level Mermaid diagram that illustrates the main components and their interactions.

  graph LR
    subgraph Tenant A
        A1[Policy Store] --> A2[Evidence Nodes]
        A2 --> A3[Access Control Engine<br>(Zero Trust)]
    end
    subgraph Tenant B
        B1[Policy Store] --> B2[Evidence Nodes]
        B2 --> B3[Access Control Engine<br>(Zero Trust)]
    end
    subgraph Federated Layer
        A3 <--> FK[Federated Knowledge Graph] <--> B3
        FK --> RAG[Retrieval‑Augmented Generation]
        RAG --> AI[LLM Engine]
        AI --> Resp[Answer Generation Service]
    end
    subgraph Audit Trail
        FK --> Ledger[Immutable Ledger]
        Resp --> Ledger
    end
    User[Questionnaire Request] -->|Auth Token| RAG
    Resp -->|Answer| User

Key takeaways from the diagram

1. Tenant isolation – Each tenant runs its own Policy Store and Evidence Nodes, but the Access Control Engine mediates any cross‑tenant request.
2. Federated Graph – The FK node aggregates schema metadata while keeping raw evidence encrypted and siloed.
3. Zero‑Trust Checks – Every access request passes through the Access Control Engine, which evaluates context (role, device posture, request purpose).
4. AI Integration – The RAG component pulls only those evidence nodes that the tenant is authorized to see, then passes them to an LLM for answer synthesis.
5. Auditability – All retrievals and generated answers are recorded in the Immutable Ledger for compliance auditors.

3. Data Model

3.1 Unified Schema

All entities are stored as property graphs (e.g., Neo4j or JanusGraph) and exposed via a GraphQL‑compatible API.

3.2 Zero‑Trust Policy Language

A lightweight DSL (Domain Specific Language) expresses fine‑grained rules:

allow(user.email =~ "*@tenantA.com")
  where action == "read"
    and entity.type == "Evidence"
    and entity.tenant_id == "tenantA"
    and device.trust_score > 0.8;

These rules are compiled into real‑time policies enforced by the Access Control Engine.

4. Workflow: From Question to Answer

1. Question Ingestion – A security reviewer uploads a questionnaire (PDF, CSV, or API JSON). Procurize parses it into individual questions and maps each to one or more framework controls.

2. Control‑Evidence Mapping – The system queries the FKG for edges that link the target control to evidence nodes belonging to the requesting tenant.

3. Zero‑Trust Authorization – Before any evidence is retrieved, the Access Control Engine validates the request context (user, device, location, time).

4. Evidence Retrieval – Authorized evidence is streamed to the RAG module. The RAG component ranks evidence by relevance using a hybrid TF‑IDF + embedding similarity model.

5. LLM Generation – The LLM receives the question, the retrieved evidence, and a prompt template that enforces tone and compliance language. Example prompt:

   You are a compliance specialist for {tenant_name}. Answer the following security questionnaire item using ONLY the supplied evidence. Do not fabricate details.
   Question: {question_text}
   Evidence: {evidence_snippet}
   

6. Answer Review & Collaboration – The generated answer appears in Procurize’s real‑time collaborative UI where subject‑matter experts can comment, edit, or approve.

7. Audit Logging – Each retrieval, generation, and edit event is appended to the Immutable Ledger with a cryptographic hash linking to the originating evidence version.

5. Security Guarantees

6. Implementation Steps on Procurize

1. Set Up Tenant Graph Nodes

   • Deploy a separate Neo4j instance per tenant (or use a multi‑tenant database with row‑level security).
   • Load existing policy documents and evidence using Procurize’s import pipelines.

2. Define Zero‑Trust Rules

   • Use Procurize’s policy editor to author DSL rules.
   • Enable device posture integration (MDM, endpoint detection) for dynamic risk scores.

3. Configure Federated Sync

   • Install the procurize-fkg-sync micro‑service.
   • Configure it to publish schema updates to a shared schema registry while keeping data encrypted at rest.

4. Integrate RAG Pipeline

   • Deploy the procurize-rag container (includes vector store, Elasticsearch, and a fine‑tuned LLM).
   • Connect the RAG endpoint to the FKG GraphQL API.

5. Activate Immutable Ledger

   • Enable the procurize-ledger module (uses Hyperledger Fabric or a lightweight Append‑Only Log).
   • Set retention policies according to compliance requirements (e.g., 7‑year audit trail).

6. Enable Collaborative UI

   • Turn on the Real‑Time Collaboration feature.
   • Define role‑based view permissions (Reviewer, Approver, Auditor).

7. Run a Pilot

   • Select a high‑volume questionnaire (e.g., SOC 2 Type II) and measure:
     • Turnaround time (baseline vs. AI‑augmented).
     • Accuracy (percentage of answers that pass auditor verification).
     • Compliance cost reduction (FTE hours saved).

7. Benefits Summary

8. Future Enhancements

1. Federated Learning for LLM Fine‑Tuning – Each tenant can contribute anonymized gradient updates to improve the domain‑specific LLM without exposing raw data.
2. Dynamic Policy‑as‑Code Generation – Auto‑generate Terraform or Pulumi modules that enforce the same zero‑trust rules in cloud infrastructure.
3. Explainable AI Overlays – Visualize the reasoning path (evidence → prompt → answer) directly in the UI using Mermaid sequence diagrams.
4. Zero‑Knowledge Proof (ZKP) Integration – Prove to auditors that a particular control is satisfied without revealing the underlying evidence.

9. Conclusion

A Zero‑Trust Federated Knowledge Graph transforms the cumbersome, siloed world of security questionnaire management into a secure, collaborative, and AI‑enhanced workflow. By combining tenant‑isolated graphs, fine‑grained access policies, Retrieval‑Augmented Generation, and an immutable audit trail, organizations can answer compliance questions faster, more accurately, and with full regulatory confidence.

Implementing this architecture on the Procurize AI platform leverages existing ingestion pipelines, collaboration tools, and security primitives—allowing teams to focus on strategic risk management rather than repetitive data gathering.

The future of compliance is federated, trustworthy, and intelligent. Embrace it today to stay ahead of auditors, partners, and regulators.

See Also

• Zero Trust Architecture – NIST SP 800‑207
• Federated Learning: Collaborative Machine Learning without Centralized Data
• Retrieval‑Augmented Generation for Knowledge‑Intensive NLP Tasks (Paper)