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

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.

  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.:

{
  "@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

企業向け実装ガイドライン

1. Run a Pilot on One Framework – Start with 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

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.