Multi Modal AI Evidence Extraction for Security Questionnaires

Security questionnaires are the gate‑keepers of every B2B SaaS deal. Vendors are asked to provide evidence—policy PDFs, architecture diagrams, code snippets, audit logs, and even screenshots of dashboards. Traditionally, security and compliance teams spend hours combing through repositories, copying files, and manually attaching them to questionnaire fields. The result is a bottleneck that slows down sales cycles, increases human error, and creates audit gaps.

Procurize has already built a powerful unified platform for questionnaire management, task assignment, and AI‑assisted answer generation. The next frontier is to automate evidence collection itself. By leveraging multi‑modal generative AI—models that understand text, images, tables, and code in a single pipeline—organizations can instantly surface the right artifact for any questionnaire item, regardless of format.

In this article we will:

1. Explain why a single‑modality approach (pure text LLMs) falls short for modern compliance workloads.
2. Detail the architecture of a multi‑modal evidence extraction engine built on top of Procurize.
3. Show how to train, evaluate, and continuously improve the system with Generative Engine Optimization (GEO) techniques.
4. Provide a concrete end‑to‑end example, from a security question to the auto‑attached evidence.
5. Discuss governance, security, and auditability concerns.

Key takeaway: Multi‑modal AI transforms evidence retrieval from a manual chore into a repeatable, auditable service, cutting questionnaire turnaround time by up to 80 % while preserving compliance rigor.

1. The Limits of Text‑Only LLMs in Questionnaire Workflows

Most AI‑driven automation today relies on large language models (LLMs) that excel at text generation and semantic search. They can pull policy clauses, summarize audit reports, and even draft narrative answers. However, compliance evidence is rarely pure text:

When a question asks “Provide a network diagram that illustrates data flow between your production and backup environments”, a text‑only model can only respond with a description; it cannot locate, verify, or embed the actual image. This gap forces users to intervene, re‑introducing the manual effort we aim to eliminate.

2. Architecture of a Multi‑Modal Evidence Extraction Engine

Below is a high‑level diagram of the proposed engine, integrated with Procurize’s core questionnaire hub.

  graph TD
    A["User submits questionnaire item"] --> B["Question classification service"]
    B --> C["Multi‑modal retrieval orchestrator"]
    C --> D["Text vector store (FAISS)"]
    C --> E["Image embedding store (CLIP)"]
    C --> F["Code embedding store (CodeBERT)"]
    D --> G["Semantic match (LLM)"]
    E --> G
    F --> G
    G --> H["Evidence ranking engine"]
    H --> I["Compliance metadata enrichment"]
    I --> J["Auto‑attach to Procurize task"]
    J --> K["Human‑in‑the‑loop verification"]
    K --> L["Audit log entry"]

2.1 Core Components

1. Question Classification Service – Uses a fine‑tuned LLM to tag incoming questionnaire items with evidence types (e.g., “network diagram”, “security policy PDF”, “Terraform plan”).
2. Multi‑modal Retrieval Orchestrator – Routes the request to the appropriate embedding stores based on the classification.
3. Embedding Stores
   • Text Store – FAISS index built from all policy docs, audit reports, and markdown files.
   • Image Store – CLIP‑based vectors generated from every diagram, screenshot, and SVG stored in the document repository.
   • Code Store – CodeBERT embeddings for all source files, CI/CD pipeline configs, and IaC templates.
4. Semantic Match Layer – A cross‑modal transformer fuses the query embedding with each modality’s vectors, returning a ranked list of candidate artifacts.
5. Evidence Ranking Engine – Applies Generative Engine Optimization heuristics: freshness, version control status, compliance tag relevance, and confidence score from the LLM.
6. Compliance Metadata Enrichment – Attaches SPDX licences, audit timestamps, and data‑protection tags to each artifact.
7. Human‑in‑the‑Loop (HITL) Verification – UI in Procurize shows the top‑3 suggestions; a reviewer can approve, replace, or reject.
8. Audit Log Entry – Every auto‑attachment is recorded with cryptographic hash, reviewer signature, and AI confidence, satisfying SOX and GDPR audit trails.

2.2 Data Ingestion Pipeline

1. Crawler scans corporate file shares, Git repositories, cloud storage buckets.
2. Pre‑processor runs OCR on scanned PDFs (Tesseract), extracts tables (Camelot), and converts Visio files to SVG.
3. Embedder generates modality‑specific vectors and stores them with metadata (file path, version, owner).
4. Incremental Update – A change‑detection micro‑service (watchdog) re‑embeds only modified assets, keeping the vector stores fresh in near‑real time.

3. Generative Engine Optimization (GEO) for Evidence Retrieval

GEO is a systematic method to tune the entire AI pipeline—not just the language model—so that the end KPI (questionnaire turnaround time) improves while maintaining compliance quality.

3.1 Prompt Example for Multi‑Modal Retrieval

[QUESTION] Provide the most recent [SOC 2] Type II audit report covering data encryption at rest.

[CONTEXT] Retrieve a PDF document that includes the relevant audit section. Return the document ID, page range, and a brief excerpt.

[MODALITY] text

The orchestrator parses the [MODALITY] tag and queries the text store only, dramatically reducing noise from image or code vectors.

3.2 Adaptive Thresholds

Using Bayesian Optimization, the system automatically adjusts the confidence threshold for each modality. When reviewers consistently accept suggestions above 0.78 confidence for diagrams, the threshold rises, reducing unnecessary hits‑to‑review. Conversely, if code snippets receive many rejections, the threshold drops, prompting more candidate artifacts.

4. End‑to‑End Example: From Question to Auto‑Attached Evidence

4.1 The Question

“Attach a diagram that shows the flow of customer data from ingestion to storage, including encryption points.”

4.2 Step‑by‑Step Flow

The total elapsed time from step 1 to step 8 is ≈ 45 seconds, compared to the typical 15–20 minutes for manual retrieval.

5. Governance, Security, and Auditable Trail

Automating evidence handling raises legitimate concerns:

1. Data Leakage – Embedding services must run in a zero‑trust VPC with strict IAM roles. No embeddings leave the corporate network.
2. Version Control – Every artifact is stored with its Git commit hash (or storage object version). If a document is updated, the engine invalidates old embeddings.
3. Explainability – The ranking engine logs the similarity scores and the prompting chain, enabling compliance officers to trace why a particular file was selected.
4. Regulatory Alignment – By attaching SPDX license identifiers and GDPR processing categories to each artifact, the solution satisfies evidence‑origin requirements for ISO 27001 Annex A.
5. Retention Policies – Auto‑purge jobs clean up embeddings for documents older than the organization’s data‑retention window, ensuring no stale evidence persists.

6. Future Directions

6.1 Multi‑Modal Retrieval as a Service (RaaS)

Expose the retrieval orchestrator via a GraphQL API so that other internal tools (e.g., CI/CD compliance checks) can request evidence without going through the full questionnaire UI.

6.2 Real‑Time Regulatory Radar Integration

Combine the multi‑modal engine with Procurize’s Regulatory Change Radar. When a new regulation is detected, automatically re‑classify affected questions and trigger a fresh evidence search, guaranteeing that uploaded artifacts stay compliant.

6.3 Federated Learning Across Enterprises

For SaaS providers serving multiple customers, a federated learning layer can share anonymized embedding updates, improving retrieval quality without exposing proprietary documents.

7. Conclusion

Security questionnaires will remain a cornerstone of vendor risk management, but the manual effort to gather and attach evidence is rapidly becoming untenable. By embracing multi‑modal AI—a blend of text, image, and code understanding—Procurize can turn evidence extraction into an automated, auditable service. Leveraging Generative Engine Optimization ensures that the system continuously improves, aligning AI confidence with human reviewer expectations and compliance mandates.

The result is a dramatic acceleration of questionnaire response times, reduced human error, and a stronger audit trail—empowering security, legal, and sales teams to focus on strategic risk mitigation rather than repetitive document hunting.

See Also

• Multi‑Modal Retrieval with CLIP and FAISS – Towards Scalable Enterprise Search
• Generative Engine Optimization: A Framework for Continuous AI Improvement
• Zero‑Trust Architecture for AI‑Powered Compliance Platforms
• ISO 27001 Annex A – Evidence Management Requirements