AI 기반 증거 자동 매핑 엔진을 통한 다중 프레임워크 설문조사 통합

소개

Security questionnaires are the gatekeepers of every B2B SaaS deal. Prospects request proof of compliance with frameworks such as SOC 2, ISO 27001, GDPR, PCI‑DSS and emerging data‑localization regulations. While the underlying controls often overlap, each framework defines its own terminology, evidence format, and severity grading. Traditional manual processes force security teams to duplicate effort: they locate a control in one framework, rewrite the answer to match another, and risk inconsistency.

The Evidence Auto‑Mapping Engine (EAME) solves this pain point by automatically translating evidence from a source framework into the language of any target framework. Powered by large language models (LLMs), a dynamic compliance knowledge graph, and a modular retrieval‑augmented generation (RAG) pipeline, EAME delivers accurate, auditable answers in seconds.

In this article we:

• Dissect the architecture of EAME and the data flows that make it reliable.
• Explain how LLM‑driven semantic alignment works without compromising confidentiality.
• Show a step‑by‑step deployment guide for Procurize customers.
• Provide performance benchmarks and best‑practice recommendations.

핵심 문제: 프레임워크 간 증거의 파편화

Even though an Access Control Policy could satisfy both SOC 2 and ISO 27001, each questionnaire asks for it in a different format:

• SOC 2 asks for a policy excerpt with version and last review date.
• ISO 27001 requests a link to the statement of applicability and a risk score.
• GDPR demands a record of processing activities that references the same policy.

Manual teams must locate the policy, copy‑paste it, re‑format the citation, and manually calculate risk scores—an error‑prone workflow that inflates turnaround time by 30‑50 %.

Architectural Overview of the Auto‑Mapping Engine

The engine is built around three pillars:

1. Compliance Knowledge Graph (CKG) – a directed, labeled graph that captures entities (controls, evidence artifacts, frameworks) and relationships (“covers”, “requires”, “equivalent‑to”).
2. LLM‑Enhanced Semantic Mapper – a prompting layer that translates a source evidence node into the target framework’s answer template.
3. Retrieval‑Augmented Generation Loop (RAG‑Loop) – a feedback mechanism that validates generated answers against the CKG and external policy stores.

Below is a high‑level Mermaid diagram illustrating data flow.

  graph LR
  A[사용자가 설문지 제출] --> B[질문 파서]
  B --> C{대상 프레임워크 식별}
  C -->|SOC2| D[CKG 조회: SOC2 노드]
  C -->|ISO27001| E[CKG 조회: ISO 노드]
  D --> F[원본 증거 검색]
  E --> F
  F --> G[LLM 의미 매핑]
  G --> H[생성된 답변]
  H --> I[컴플라이언스 검증기]
  I -->|통과| J[답변이 조달 DB에 저장]
  I -->|실패| K[인간 검토 (HITL)]
  K --> G

1. Compliance Knowledge Graph (CKG)

The CKG is populated from three sources:

• Framework Taxonomies – official control libraries imported as node sets.
• Enterprise Policy Repository – Markdown/Confluence files indexed via embeddings.
• Evidence Metadata Store – files, screenshots, and audit logs tagged with SPDX‑like identifiers.

Each node carries attributes such as framework, control_id, evidence_type, version, and confidence_score. Relationships encode equivalence (equivalent_to), hierarchy (subcontrol_of), and provenance (generated_by).

Graph Example (Mermaid)

  graph TD
  A["접근 제어 정책"]:::evidence -->|covers| B["SOC2 CC6.1"]:::control
  A -->|covers| C["ISO27001 A.9.2.1"]:::control
  A -->|covers| D["GDPR Art.32"]:::control
  classDef control fill:#f9f,stroke:#333,stroke-width:2px;
  classDef evidence fill:#bbf,stroke:#333,stroke-width:2px;

2. LLM‑Enhanced Semantic Mapper

The mapper receives a source evidence payload (e.g., a policy document) and a target framework template (e.g., SOC 2 answer format). Using a few‑shot prompt engineered for compliance context, the LLM produces a structured answer:

{
  "framework": "SOC2",
  "control_id": "CC6.1",
  "answer": "Our Access Control Policy (v3.2, reviewed 2024‑12‑01) restricts system access to authorized personnel based on least‑privilege principles. See attachment for full policy text.",
  "evidence_refs": ["policy_v3.2.pdf"]
}

Key prompt ingredients:

• System Prompt – sets compliance tone and restricts hallucinations.
• Few‑Shot Examples – real answered questionnaires from past audits (anonymized).
• Constraint Tokens – enforce that the answer must reference at least one evidence_refs entry.

The LLM operates behind a private inference endpoint to maintain data confidentiality and compliance with GDPR.

3. Retrieval‑Augmented Generation Loop (RAG‑Loop)

After generation, the answer is passed through a validator that:

1. Cross‑references the answer’s evidence_refs with the CKG to ensure the cited artifact indeed covers the requested control.
2. Checks version consistency (e.g., policy version matches the latest stored version).
3. Runs a similarity score between generated text and the original source evidence; scores below 0.85 trigger a Human‑in‑the‑Loop (HITL) review.

The loop repeats until validation passes, guaranteeing traceability and auditability.

Procurize에 엔진 배포

Prerequisites

Installation Steps

1. Provision CKG Service – Deploy the graph database (Neo4j or Amazon Neptune) using the supplied Helm chart.
2. Ingest Framework Taxonomies – Run the ckg-import CLI with the latest SOC 2, ISO 27001, GDPR JSON schemas.
3. Index Enterprise Policies – Execute policy-indexer which creates dense vector embeddings (SBERT) and stores them in the graph.
4. Deploy LLM Inference – Spin up a secure container (e.g., private-llm) behind a VPC‑isolated load balancer. Set environment variables for LLM_API_KEY.
5. Configure RAG‑Loop – Apply the rag-loop.yaml manifest which defines the validator webhook, HITL queue (Kafka), and Prometheus metrics.
6. Integrate with Procurize UI – Enable the “Auto‑Map” toggle in the questionnaire editor. The UI sends a POST request to /api/auto-map with source_framework, target_framework, and question_id.
7. Run a Smoke Test – Submit a test questionnaire containing a known control (e.g., SOC 2 CC6.1) and verify that the answer includes the correct policy reference.

Monitoring & Observability

• Latency – Target < 2 seconds per answer; alerts fire if > 5 seconds.
• Validation Failure Rate – Aim for < 1 %; spikes indicate drift in policy repository.
• LLM Token Usage – Track cost; enable caching for repeated questions.

Performance Benchmarks

*Evidence re‑use ratio measures how often the same artifact satisfies multiple controls across frameworks.

The engine delivers a ~86 % reduction in manual effort while maintaining an audit‑grade validation pass rate of 97 %.

Best Practices for Sustainable Auto‑Mapping

1. Keep the CKG Fresh – Schedule nightly sync jobs that pull updated control libraries from ISO, SOC, and GDPR portals.
2. Version‑Tag Evidence – Every uploaded artifact should include a semantic version (e.g., policy_v3.2.pdf). The validator will reject outdated references.
3. Fine‑Tune LLM on Domain Data – Use a LoRA adapter trained on 5 k anonymized questionnaire responses to improve compliance tone.
4. Implement Role‑Based Access – Restrict who can approve HITL overrides; log each override with user ID and timestamp.
5. Run Periodic Drift Tests – Randomly select answered questions, compare to a human‑crafted baseline, and compute BLEU/ROUGE scores to detect regression.

Security and Privacy Considerations

• Data Residency – Deploy the LLM endpoint in the same region as your policy bucket to satisfy data‑localization requirements.
• Zero‑Knowledge Proof for Confidential Artifacts – For highly sensitive policies, the system can generate a cryptographic proof of inclusion in the CKG without exposing content, leveraging zk‑SNARKs.
• Differential Privacy – When aggregating usage metrics, add calibrated noise to avoid leaking details about specific policies.

Future Roadmap

• Multi‑Modal Evidence Support – Incorporate OCR for scanned compliance certificates and image embeddings for network diagrams.
• Cross‑Tenant Federated Graph – Allow industry consortia to share anonymized control equivalence mappings while preserving each member’s proprietary evidence.
• Continuous Regulatory Feed – Real‑time ingestion of new regulations (e.g., AI Act) that auto‑creates new graph nodes and triggers re‑training of the LLM mapping prompt.

결론

The AI‑Powered Evidence Auto‑Mapping Engine transforms the compliance landscape from a reactive, manual bottleneck into a proactive, data‑driven service. By unifying evidence across SOC 2, ISO 27001, GDPR, and other frameworks, the engine cuts questionnaire turnaround time by over 95 %, reduces human error, and provides an auditable trail that satisfies auditors and regulators alike.

Implementing EAME within Procurize equips security, legal, and product teams with a single source of truth, frees them to focus on strategic risk mitigation, and ultimately accelerates revenue cycles for SaaS businesses.

참고 Also

• https://www.iso.org/standard/54534.html
• https://www.aicpa.org/interestareas/frc/assuranceadvisory/pages/soc2.aspx
• https://gdpr.eu/
• https://www.nist.gov/cyberframework