Dynamic Multi Modal Evidence Extraction with Federated Learning for Real Time Security Questionnaires

Abstract
Security questionnaires and compliance audits have become a bottleneck for fast‑growing SaaS companies. Traditional manual processes are error‑prone, time‑consuming, and struggle to keep up with ever‑changing regulatory standards. This article introduces a groundbreaking solution—Dynamic Multi‑Modal Evidence Extraction (DMEE) powered by Federated Learning (FL)—that integrates tightly with the Procurize AI platform to automate the collection, verification, and presentation of evidentiary artifacts across diverse data modalities (text, images, code snippets, log streams). By keeping learning on‑premise and sharing only model updates, organizations gain privacy‑preserving intelligence while the global model continuously improves, delivering real‑time, context‑aware questionnaire answers with higher accuracy and lower latency.

1. Why Multi‑Modal Evidence Extraction Matters

Security questionnaires request concrete evidence that may live in:

Most AI‑driven assistants excel at single‑modal text generation, leaving gaps when the answer requires a screenshot or a log excerpt. A unified multi‑modal pipeline closes that gap, turning raw artifacts into structured evidence objects that can be plugged directly into responses.

2. Federated Learning: The Privacy‑First Backbone

2.1 Core Principles

• Data Never Leaves the Premises – Raw documents, screenshots, and log files remain on the company’s secure environment. Only model weight deltas are transmitted to a central orchestrator.
• Secure Aggregation – Weight updates are encrypted and aggregated using homomorphic techniques, preventing any individual client from being reverse‑engineered.
• Continuous Improvement – Every new questionnaire answered locally contributes to a global knowledge base without exposing confidential data.

2.2 Federated Learning Workflow in Procurize

  graph LR
    A["Company A\nLocal Evidence Vault"] --> B["Local Extractor\n(LLM + Vision Model)"]
    C["Company B\nLocal Evidence Vault"] --> B
    B --> D["Weight Delta"]
    D --> E["Secure Aggregator"]
    E --> F["Global Model"]
    F --> B
    style A fill:#f9f,stroke:#333,stroke-width:2px
    style C fill:#f9f,stroke:#333,stroke-width:2px
    style D fill:#bbf,stroke:#333,stroke-width:2px
    style E fill:#bbf,stroke:#333,stroke-width:2px
    style F fill:#9f9,stroke:#333,stroke-width:2px

1. Local Extraction – Each tenant runs a multi‑modal extractor that combines a large language model (LLM) with a vision transformer (ViT) to tag and index evidence.
2. Delta Generation – Model updates (gradients) are computed on the local data and encrypted.
3. Secure Aggregation – Encrypted deltas from all participants are aggregated, producing a global model that embodies collective learnings.
4. Model Refresh – The refreshed global model is pushed back to every tenant, instantly improving extraction accuracy across all modalities.

3. Architecture of the DMEE Engine

3.1 Component Overview

3.2 Data Flow Diagram

  flowchart TD
    subgraph Ingestion
        D1[Docs] --> P1[Pre‑Process]
        D2[Images] --> P1
        D3[Logs] --> P1
    end
    P1 --> E1[Multi‑Modal Encoder]
    E1 --> C1[Evidence Classifier]
    C1 --> R1[Vector Store]
    Q[Question] --> G1[Narrative Generator]
    G1 --> R1
    R1 --> G1
    G1 --> V[Validator]
    V --> A[Audit Recorder]
    style Ingestion fill:#e3f2fd,stroke:#90caf9,stroke-width:2px
    style Q fill:#ffcc80,stroke:#fb8c00,stroke-width:2px

4. From Query to Answer: Real‑Time Process Walk‑Through

1. Question Reception – A security analyst opens a questionnaire in Procurize. The question “Provide evidence of MFA for privileged accounts” is sent to the DMEE engine.
2. Intent Extraction – The LLM extracts key intent tokens: MFA, privileged accounts.
3. Cross‑Modal Retrieval – The query vector is matched against the global vector store. The engine pulls:
   • A screenshot of the MFA configuration page (image).
   • An audit log snippet showing successful MFA events (log).
   • The internal MFA policy (text).
4. Evidence Validation – Each object is checked for freshness (< 30 days) and required signatures.
5. Narrative Synthesis – The LLM composes an answer, embedding the evidence objects as secure references that render inline in the questionnaire UI.
6. Instant Delivery – The completed answer appears in the UI within 2–3 seconds, ready for reviewer approval.

5. Benefits for Compliance Teams

6. Implementation Checklist for Enterprises

1. Deploy Local Extractor – Install the Docker‑based extractor on a secure subnet. Connect to your document and log sources.
2. Configure Federated Sync – Provide the central aggregator endpoint and TLS certificates.
3. Define Taxonomy – Map your regulatory framework ( SOC 2, ISO 27001, GDPR ) to the platform’s evidence categories.
4. Set Validation Rules – Specify expiration windows, required attestation signatures, and encryption flags.
5. Pilot Phase – Run the engine on a subset of questionnaires; monitor precision/recall metrics.
6. Roll‑out – Expand to all vendor assessments; enable automated suggestion mode for analysts.

7. Real‑World Case Study: FinTech Corp Reduces Turnaround by 75 %

Background – FinTech Corp handled ~150 vendor questionnaires per quarter, each requiring multiple evidence artifacts. Manual collection averaged 4 hours per questionnaire.

Solution – Implemented Procurize’s DMEE with federated learning across three regional data centers.

Key Takeaways

• The federated approach satisfied strict data residency requirements.
• Multi‑modal retrieval uncovered previously hidden evidence (e.g., UI screenshots) that shortened audit cycles.

8. Challenges & Mitigations

9. Future Roadmap

1. Zero‑Shot Evidence Generation – Use generative diffusion models to synthesize masked screenshots when original assets are unavailable.
2. Explainable AI Confidence Scores – Show per‑evidence confidence bars with counterfactual explanations.
3. Edge‑Federated Nodes – Deploy lightweight extractors on developer laptops for instant on‑the‑fly evidence during code reviews.

10. Conclusion

Dynamic Multi‑Modal Evidence Extraction powered by Federated Learning represents a paradigm shift in security questionnaire automation. By unifying text, visual, and log data while preserving privacy, organizations can respond faster, more accurately, and with full auditability. Procurize’s modular architecture makes adoption straightforward, allowing compliance teams to focus on strategic risk mitigation rather than repetitive data gathering.