Secure Multiparty Computation Powered AI for Confidential Vendor Questionnaire Responses

Introduction

Security questionnaires are the gatekeepers of B2B SaaS contracts. They solicit detailed information about infrastructure, data handling, incident response, and compliance controls. Vendors often need to answer dozens of such questionnaires per quarter, each demanding evidence that may contain sensitive internal data—architecture diagrams, privileged credentials, or proprietary process descriptions.

Traditional AI‑driven automation, like the Procurize AI Engine, dramatically speeds up answer generation but typically requires centralized access to the raw source material. That centralization introduces two major risks:

1. Data Leakage – If the AI model or the underlying storage is compromised, confidential company information may be exposed.
2. Regulatory Non‑Compliance – Regulations such as GDPR, CCPA, and emerging data‑sovereignty laws limit where and how personal or proprietary data can be processed.

Enter Secure Multiparty Computation (SMPC)—a cryptographic protocol that allows multiple parties to jointly compute a function over their inputs while keeping those inputs private. By fusing SMPC with generative AI, we can produce accurate, auditable questionnaire answers without ever revealing raw data to the AI model or any single processing node.

This article explores the technical underpinnings, practical implementation steps, and business benefits of a Secure‑SMPC‑AI pipeline, tailored for the Procurize platform.

Key takeaway: SMPC‑augmented AI delivers the speed of automation and the privacy guarantees of zero‑knowledge, reshaping how SaaS firms respond to security questionnaires.

1. Fundamentals of Secure Multiparty Computation

Secure Multiparty Computation enables a set of participants, each holding a private input, to compute a joint function f such that:

• Correctness – All parties receive the correct output f(x₁, x₂, …, xₙ).
• Privacy – No party learns anything about the inputs of the others beyond what can be inferred from the output.

SMPC protocols come in two major families:

For our scenario—text extraction, semantic similarity, and evidence synthesis—the additive secret sharing approach scales best because it handles high‑dimensional vector operations efficiently using modern MPC frameworks such as MP-SPDZ, CrypTen, or Scale‑MPC.

2. Architecture Overview

Below is a high‑level Mermaid diagram illustrating the end‑to‑end flow of SMPC‑augmented AI inside Procurize.

  graph TD
    A["Data Owner (Company)"] -->|Encrypt & Share| B["SMPC Node 1 (AI Compute)"]
    A -->|Encrypt & Share| C["SMPC Node 2 (Policy Store)"]
    A -->|Encrypt & Share| D["SMPC Node 3 (Audit Ledger)"]
    B -->|Secure Vector Ops| E["LLM Inference (Encrypted)"]
    C -->|Policy Retrieval| E
    D -->|Proof Generation| F["Zero‑Knowledge Audit Proof"]
    E -->|Encrypted Answer| G["Answer Aggregator"]
    G -->|Revealed Answer| H["Vendor Questionnaire UI"]
    F -->|Audit Trail| H

Explanation of components

• Data Owner (Company) – Holds proprietary documents (e.g., SOC 2 reports, architecture diagrams). Before any processing, the owner secret‑shares each document into three encrypted shards and distributes them to the SMPC nodes.
• SMPC Nodes – Independently compute on shares. Node 1 runs the LLM inference engine (e.g., a fine‑tuned Llama‑2 model) under encryption. Node 2 holds policy knowledge graphs (e.g., ISO 27001 controls) also secret‑shared. Node 3 maintains an immutable audit ledger (blockchain or append‑only log) that records request metadata without exposing raw data.
• LLM Inference (Encrypted) – The model receives encrypted embeddings derived from the shredded documents, produces encrypted answer vectors, and returns them to the aggregator.
• Answer Aggregator – Reconstructs the plaintext answer only after the entire computation finishes, ensuring no intermediate leak.
• Zero‑Knowledge Audit Proof – Generated by Node 3 to prove that the answer was derived from the designated policy sources without revealing the sources themselves.

3. Detailed Workflow

3.1 Ingestion & Secret Sharing

1. Document Normalization – PDFs, Word files, and source‑code snippets are converted into plain‑text and tokenized.
2. Embedding Generation – A lightweight encoder (e.g., MiniLM) creates dense vectors for each paragraph.
3. Additive Secret Splitting – For each vector v, generate random shares v₁, v₂, v₃ such that v = v₁ + v₂ + v₃ (mod p).
4. Distribution – Shares are sent over TLS to the three SMPC nodes.

3.2 Secure Retrieval of Policy Context

• The policy knowledge graph (controls, mappings to standards) resides encrypted across the nodes.
• When a questionnaire item arrives (e.g., “Describe your data‑at‑rest encryption”), the system queries the graph using secure set‑intersection to locate relevant policy clauses without releasing the entire graph.

3.3 Encrypted LLM Inference

• The encrypted embeddings and retrieved policy vectors are fed into a privacy‑preserving transformer that operates on secret shares.
• Techniques such as Fully Homomorphic Encryption (FHE) friendly attention or MPC‑optimized softmax compute the most probable answer token sequence in the encrypted domain.

3.4 Reconstruction & Auditable Proof

• Once the encrypted answer tokens are ready, the Answer Aggregator reconstructs the plaintext answer by summing the shares.
• Simultaneously, Node 3 produces a Zero‑Knowledge Succinct Non‑interactive Argument of Knowledge (zk‑SNARK) confirming that the answer respects:
  • The correct policy clause selection.
  • No leakage of raw document content.

3.5 Delivery to End‑User

• The final answer appears in the Procurize UI alongside a cryptographic proof badge.
• Auditors can verify the badge using a public verifier key, ensuring compliance without requesting the underlying documents.

4. Security Guarantees

5. Performance Considerations

While SMPC introduces overhead, modern optimizations keep latency within acceptable bounds for questionnaire automation:

Key optimizations:

• Batching – Process multiple questionnaire items in parallel across the same shares.
• Hybrid Protocol – Use secret sharing for heavy linear algebra, switch to garbled circuits only for non‑linear functions (e.g., comparisons).
• Edge Deployment – Deploy one SMPC node on‑premises (e.g., within the company firewall) reducing trust on external clouds.

6. Integration with Procurize

Procurize already provides:

• Document Repository – Centralized storage for compliance artifacts.
• Questionnaire Builder – UI to author, assign, and track questionnaires.
• AI Engine – Fine‑tuned LLM for answer generation.

To incorporate SMPC‑AI:

1. Enable SMPC Mode – Admin toggles a flag in the platform settings.
2. Provision SMPC Nodes – Deploy three Docker containers (Node 1–3) using the official procurize/smpc-node image. The containers automatically register with the platform’s orchestration layer.
3. Define Policy Graph – Export existing policy mappings into a JSON‑LD graph; the platform encrypts and distributes it.
4. Configure Auditable Proofs – Supply a public verification key; the UI will render proof badges automatically.
5. Train Secure LLM – Use the same dataset as the standard AI engine; training occurs off‑chain but the resulting model weights are loaded into Node 1 in a sealed enclave (e.g., Intel SGX) for extra security.

7. Real‑World Use Case: FinTech Vendor Audit

Company: FinFlow, a mid‑size FinTech SaaS provider.

Pain Point: Quarterly audits from banking partners demanded full data‑at‑rest encryption details. Their encryption keys and key‑management policies are classified and cannot be uploaded to a third‑party AI service.

Solution:

1. FinFlow deployed SMPC‑AI nodes—Node 1 in an Azure Confidential Compute VM, Node 2 on‑premises, Node 3 as a Hyperledger Fabric peer.
2. The encryption‑policy document (5 MB) was secret‑shared across nodes.
3. The questionnaire item “Describe key‑rotation schedule” was answered in 4.2 seconds with a verifiable proof.
4. The bank’s auditors verified the proof using the public key, confirming the response originated from FinFlow’s internal policy without ever seeing the policy itself.

Result: Audit turnaround dropped from 7 days to 2 hours, and no compliance breaches were reported.

8. Future Directions

Conclusion

Secure Multiparty Computation, when paired with generative AI, offers a privacy‑first, auditable, and scalable solution for automating security questionnaire responses. It fulfills three critical demands of modern SaaS firms:

1. Speed – Near‑real‑time answer generation reduces deal friction.
2. Security – Confidential data never leaves its rightful owner, protecting against leaks and regulatory violations.
3. Trust – Cryptographic proofs give customers and auditors confidence that answers are rooted in verified internal policies.

By embedding SMPC‑AI into Procurize, organizations can transform a traditionally manual bottleneck into a competitive advantage, enabling faster contract closures while upholding the highest privacy standards.

See Also

• Procurize Blog: AI‑Powered Compliance Integration Into CI/CD Workflows