
# 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](https://secureframe.com/hub/soc-2/what-is-soc-2), [ISO 27001](https://www.iso.org/standard/27001), [GDPR](https://gdpr.eu/), 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

| Concept | What it means for questionnaire automation |
|---|---|
| **Zero Trust** | “Never trust, always verify.” Every request to the graph is authenticated, authorized, and continuously evaluated against policies. |
| **Federated Knowledge Graph** | A network of independent graph nodes (each owned by a tenant) that share a common schema but keep their data physically isolated. |
| **RAG (Retrieval‑Augmented Generation)** | LLM‑driven answer generation that fetches relevant evidence from the graph before composing a response. |
| **Immutable Ledger** | Append‑only storage (e.g., blockchain‑style Merkle tree) that records every change to evidence, ensuring tamper‑evidence. |

---

## 2. Architectural Overview

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

```mermaid
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

| Entity | Attributes | Example |
|---|---|---|
| **Policy** | `policy_id`, `framework`, `section`, `control_id`, `text` | `SOC2-CC6.1` |
| **Evidence** | `evidence_id`, `type`, `location`, `checksum`, `tags`, `tenant_id` | `evid-12345`, `log`, `s3://bucket/logs/2024/09/01.log` |
| **Relationship** | `source_id`, `target_id`, `rel_type` | `policy_id -> evidence_id` (evidence_of) |
| **AccessRule** | `entity_id`, `principal`, `action`, `conditions` | `evidence_id`, `user:alice@tenantA.com`, `read`, `device_trusted==true` |

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

| Threat | Mitigation |
|---|---|
| **Data leakage across tenants** | Zero‑Trust Access Control enforces `tenant_id` match; all data transfers are end‑to‑end encrypted (TLS 1.3 + Mutual TLS). |
| **Credential compromise** | Short‑lived JWTs, device attestation, and continuous risk scoring (behavioural analytics) invalidate tokens on anomaly detection. |
| **Tampering of evidence** | Immutable Ledger uses Merkle proofs; any alteration triggers a mismatch alert visible to auditors. |
| **Model hallucination** | RAG constrains the LLM to retrieved evidence only; a post‑generation verifier checks for unsupported statements. |
| **Supply‑chain attacks** | All graph extensions (plugins, connectors) are signed and vetted via a CI/CD gate that runs static analysis and SBOM checks. |

---

## 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

| Business Benefit | Technical Outcome |
|---|---|
| **Speed** – Reduce questionnaire response time from days to minutes. | RAG fetches relevant evidence in < 250 ms; LLM generates answers in < 1 s. |
| **Risk Reduction** – Eliminate human errors and data leakage. | Zero‑trust enforcement and immutable logging guarantee that only authorized evidence is used. |
| **Scalability** – Support hundreds of tenants without replicating data. | Federated graph isolates storage, while the shared schema enables cross‑tenant analytics. |
| **Audit Readiness** – Provide a provable trail for regulators. | Every answer is linked to a cryptographic hash of the exact evidence version. |
| **Cost Efficiency** – Lower compliance OPEX. | Automation cuts manual effort up to 80 %, freeing security teams for strategic work. |

---

## 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.

---

## <span class='highlight-content'>See</span> Also

* [Zero Trust Architecture – NIST SP 800‑207](https://csrc.nist.gov/publications/detail/sp/800-207/final)  
* [Federated Learning: Collaborative Machine Learning without Centralized Data](https://arxiv.org/abs/1902.01046)  
* [Retrieval‑Augmented Generation for Knowledge‑Intensive NLP Tasks (Paper)](https://arxiv.org/abs/2005.11401)