  

# マルチ規制質問票向け AI 活用リアルタイム証拠照合  

## はじめに  

Security questionnaires have become the bottleneck of every B2B SaaS deal.  
A single prospective customer may require **10‑15 distinct compliance frameworks**, each asking for overlapping but subtly different evidence. Manual cross‑referencing leads to:

* **Duplicate effort** – security engineers rewrite the same policy snippet for each questionnaire.  
* **Inconsistent answers** – a minor wording change can unintentionally create a compliance gap.  
* **Audit risk** – without a single source of truth, evidence provenance is hard to prove.  

**Procurize’s AI Powered Real Time Evidence Reconciliation Engine (ER‑Engine)** eliminates these pain points. By ingesting all compliance artifacts into a unified Knowledge Graph and applying Retrieval‑Augmented Generation (RAG) with dynamic prompt engineering, the ER‑Engine can:

1. **Identify equivalent evidence** across frameworks in milliseconds.  
2. **Validate provenance** using cryptographic hashing and immutable audit trails.  
3. **Suggest the most up‑to‑date artifact** based on policy drift detection.  

The result is a single, AI‑guided answer that satisfies every framework simultaneously.  

---  

## 解決する主要課題  

| 課題 | 従来の方法 | AI 主導の照合 |
|-----------|----------------------|--------------------------|
| **証拠の重複** | ドキュメント間のコピー＆ペースト、手動での再フォーマット | グラフベースのエンティティリンクにより冗長性が除去される |
| **バージョンドリフト** | スプレッドシートでの手動ログ、手動差分比較 | リアルタイムのポリシー変更レーダーが自動で参照を更新 |
| **規制マッピング** | 手作業のマトリックス、ミスが発生しやすい | LLM 補強推論による自動オントロジーマッピング |
| **監査証跡** | PDF アーカイブ、ハッシュ検証なし | 各回答に Merkle 証明を付与したイミュータブル台帳 |
| **スケーラビリティ** | 質問票ごとに線形の作業量 | 二次的削減: *n* 件の質問票 ↔ *≈ √n* 個のユニーク証拠ノード |  

---  

## アーキテクチャ概要  

The ER‑Engine sits at the heart of Procurize’s platform and comprises four tightly coupled layers:

1. **Ingestion Layer** – Pulls policies, controls, evidence files from Git repositories, cloud storage, or SaaS policy vaults.  
2. **Knowledge Graph Layer** – Stores entities (controls, artifacts, regulations) as nodes, edges encode *satisfies*, *derived‑from*, and *conflicts‑with* relationships.  
3. **AI Reasoning Layer** – Combines a **retrieval engine** (vector similarity on embeddings) with a **generation engine** (instruction‑tuned LLM) to produce draft answers.  
4. **Compliance Ledger Layer** – Writes each generated answer into an append‑only ledger (blockchain‑like) with hash of source evidence, timestamp, and author signature.  

Below is a high‑level Mermaid diagram that captures the data flow.  

```mermaid
graph TD
    A["Policy Repo"] -->|Ingest| B["Document Parser"]
    B --> C["Entity Extractor"]
    C --> D["Knowledge Graph"]
    D --> E["Vector Store"]
    E --> F["RAG Retrieval"]
    F --> G["LLM Prompt Engine"]
    G --> H["Draft Answer"]
    H --> I["Proof & Hash Generation"]
    I --> J["Immutable Ledger"]
    J --> K["Questionnaire UI"]
    K --> L["Vendor Review"]
    style A fill:#f9f,stroke:#333,stroke-width:2px
    style J fill:#bbf,stroke:#333,stroke-width:2px
```  

*All node labels are wrapped in double quotes as required for Mermaid.*  

---  

## ステップバイステップ・ワークフロー  

### 1. 証拠の取り込みと正規化  

* **File Types:** PDFs, DOCX, Markdown, OpenAPI specs, Terraform modules.  
* **Processing:** OCR for scanned PDFs, NLP entity extraction (control IDs, dates, owners).  
* **Normalization:** Converts every artifact into a canonical JSON‑LD record, e.g.:  

```json
{
  "@type": "Evidence",
  "id": "ev-2025-12-13-001",
  "title": "Data Encryption at Rest Policy",
  "frameworks": ["ISO27001","SOC2"],
  "version": "v3.2",
  "hash": "sha256:9a7b..."
}
```  

### 2. Knowledge Graph の構築  

* Nodes are created for **Regulations**, **Controls**, **Artifacts**, and **Roles**.  
* Edge examples:  
  * `Control "A.10.1"` **satisfies** `Regulation "ISO27001"`  
  * `Artifact "ev-2025-12-13-001"` **enforces** `Control "A.10.1"`  

The graph is stored in a **Neo4j** instance with **Apache Lucene** full‑text indexes for rapid traversal.  

### 3. リアルタイム検索  

When a questionnaire asks, *“Describe your data‑at‑rest encryption mechanism.”* the platform:

1. Parses the question into a **semantic query**.  
2. Looks up relevant **Control IDs** (e.g., ISO 27001 A.10.1, SOC 2 CC6.1).  
3. Retrieves top‑k evidence nodes using cosine similarity on **SBERT** embeddings.  

### 4. プロンプトエンジニアリング & 生成  

A **dynamic template** is built on the fly:  

```
You are a compliance analyst. Using the following evidence items (provide citations with IDs), answer the question concisely and in a tone suitable for enterprise security reviewers.
[Evidence List]
Question: {{user_question}}
```  

An **instruction‑tuned LLM** (e.g., Claude‑3.5) returns a draft answer, which is immediately **re‑ranked** based on citation coverage and length constraints.  

### 5. 証跡と台帳への書き込み  

* The answer is concatenated with the **hashes** of all referenced evidence items.  
* A **Merkle tree** is built, its root stored in an **Ethereum‑compatible sidechain** for immutability.  
* The UI displays a **cryptographic receipt** that auditors can verify independently.  

### 6. コラボレーティブレビュ​ー & 公開  

* Teams can **comment inline**, request alternate evidence, or trigger a **re‑run** of the RAG pipeline if policy updates are detected.  
* Once approved, the answer is **published** to the vendor questionnaire module and logged in the ledger.  

---  

## セキュリティ & プライバシー考慮事項  

| Concern | Mitigation |
|---------|------------|
| **Confidential Evidence Exposure** | All evidence is encrypted at rest with AES‑256‑GCM. Retrieval occurs in a **Trusted Execution Environment (TEE)**. |
| **Prompt Injection** | Input sanitization and a sandboxed LLM container restrict system‑level commands. |
| **Ledger Tampering** | Merkle proofs and periodic anchoring to a public blockchain make any alteration statistically impossible. |
| **Cross‑Tenant Data Leakage** | **Federated Knowledge Graphs** isolate tenant sub‑graphs; only shared regulatory ontologies are common. |
| **Regulatory Data Residency** | Deployable in any cloud region; the graph and ledger respect the tenant’s data residency policy. |  

---  

## 企業向け実装ガイドライン  

1. **Run a Pilot on One Framework** – Start with **[SOC 2](https://secureframe.com/hub/soc-2/what-is-soc-2)** to validate ingestion pipelines.  
2. **Map Existing Artifacts** – Use Procurize’s bulk import wizard to tag every policy document with framework IDs (e.g., ISO 27001, GDPR).  
3. **Define Governance Rules** – Set role‑based access (e.g., Security Engineer can approve, Legal can audit).  
4. **Integrate CI/CD** – Hook the ER‑Engine into your **GitOps** pipeline; any policy change automatically triggers a re‑index.  
5. **Train the LLM on Domain Corpus** – Fine‑tune with a few dozen historic questionnaire answers for higher fidelity.  
6. **Monitor Drift** – Enable the **Policy Change Radar**; when a control’s wording changes, the system flags affected answers.  

---  

## 測定可能なビジネス効果  

| Metric | Before ER‑Engine | After ER‑Engine |
|--------|------------------|-----------------|
| **平均回答時間** | 45 min / question | 12 min / question |
| **証拠の重複率** | 30 % of artifacts | < 5 % |
| **監査指摘率** | 2.4 % per audit | 0.6 % |
| **チーム満足度 (NPS)** | 32 | 74 |
| **ベンダー契約完了までの期間** | 6 weeks | 2.5 weeks |

A **2024 case study** at a fintech unicorn reported a **70 % reduction** in questionnaire turnaround and a **30 % cut** in compliance staffing costs after adopting the ER‑Engine.  

---  

## 今後のロードマップ  

* **マルチモーダル証拠抽出** – スクリーンショット、動画、IaC スナップショットの取り込み。  
* **ゼロナレッジ証明統合** – ベンダーが生データを閲覧せずに回答の正当性を検証できるようにする。  
* **予測規制フィード** – AI が今後の規制変更を予測し、事前にポリシー更新を提案。  
* **自己修復テンプレート** – グラフニューラルネットワークがコントロールの廃止を検知し、質問票テンプレートを自動で書き換える。  

---  

## 結論  

The **AI Powered Real Time Evidence Reconciliation Engine** transforms the chaotic landscape of multi‑regulatory questionnaires into a disciplined, traceable, and rapid workflow. By unifying evidence in a knowledge graph, leveraging RAG for instant answer generation, and committing every response to an immutable ledger, Procurize empowers security and compliance teams to focus on risk mitigation rather than repetitive paperwork. As regulations evolve and the volume of vendor assessments skyrockets, such AI‑first reconciliation will become the de‑facto standard for trustworthy, auditable questionnaire automation.