Privacy Preserving Data Stitching Engine for Cross Domain Questionnaire Automation

Introduction

Security questionnaires, compliance audits, and vendor risk assessments are becoming the gatekeepers of every B2B SaaS deal. The average questionnaire contains 30‑50 distinct evidential requests—from IAM logs stored in a cloud IAM service, to encryption key inventories kept in a separate key‑management system, to third‑party audit reports hosted on a compliance vault.

Manual collation of this evidence is costly, error‑prone, and increasingly risky from a privacy standpoint. Data stitching, the automated process of extracting, normalizing, and linking evidence across disparate data sources, is the missing link that turns a chaotic evidence pool into a coherent, audit‑ready narrative.

When combined with privacy‑preserving techniques—such as homomorphic encryption, differential privacy, and secure multi‑party computation (SMPC)—stitching can be performed without ever exposing raw confidential data to the orchestration layer. In this article we explore the architecture, benefits, and practical steps for building a Privacy Preserving Data Stitching Engine (PPDSE) on top of the Procurize AI platform.

The Challenge of Cross‑Domain Evidence

Traditional automation pipelines solve the speed problem but fall short on privacy because they rely on a trusted central data lake. A PPDSE must meet both criteria: secure, auditable stitching and regulatory‑compliant handling.

What is Data Stitching?

Data stitching is the programmatic merging of related data fragments into a unified, queryable representation. In the context of security questionnaires:

1. Discovery – Identify which data sources contain evidence that satisfies a particular questionnaire item.
2. Extraction – Pull the raw artifact (log excerpt, policy document, configuration file) from its source, respecting source‑specific access controls.
3. Normalization – Convert heterogeneous formats (JSON, CSV, PDF, XML) into a common schema (e.g., a Compliance Evidence Model).
4. Linkage – Establish relationships between evidence pieces (e.g., link a key‑rotation log to its corresponding KMS policy).
5. Summarization – Generate a concise, AI‑augmented narrative that satisfies the questionnaire field while preserving source provenance.

When the stitching process is privacy‑preserving, each step is executed under cryptographic guarantees that prevent the orchestration engine from learning the underlying raw data.

How Procurize Implements Privacy Preserving Stitching

Procurize’s AI platform already offers a unified questionnaire hub, task assignment, real‑time commenting, and LLM‑driven answer generation. The PPDSE extends this hub with a secure evidence pipeline composed of three layers:

1. Source Connectors with Zero‑Knowledge Encryption

• Each connector (for Snowflake, Azure Blob, ServiceNow, etc.) encrypts the data at source using a public key belonging to the questionnaire instance.
• The encrypted payload never leaves the source in plaintext; only the ciphertext hash is transmitted to the orchestration layer for indexing.

2. Privacy‑Preserving Computation Engine

• Utilizes SMPC to perform normalization and linkage on ciphertext fragments across multiple parties.
• Homomorphic aggregates (e.g., count of compliant controls) are computed without decrypting individual values.
• A Differential Privacy module adds calibrated noise to statistical summaries, protecting individual record exposure.

3. AI‑Augmented Narrative Generator

• The decrypted, vetted evidence is fed into a Retrieval‑Augmented Generation (RAG) pipeline that constructs human‑readable answers.
• Explainability hooks embed provenance metadata (source ID, timestamp, encryption hash) into the final narrative, enabling auditors to verify the answer without seeing raw data.

Mermaid Architecture Diagram

  graph LR
    A["Source Connector<br>(Zero‑Knowledge Encryption)"]
    B["Secure Computation Engine<br>(SMPC + Homomorphic)"]
    C["AI Narrative Generator<br>(RAG + Explainability)"]
    D["Questionnaire Hub<br>(Procurize UI)"]
    E["Auditor Verification<br>(Proof of Origin)"]
    
    A --> B
    B --> C
    C --> D
    D --> E

All node labels are wrapped in double quotes as required, with no escape characters.

Benefits of a Privacy Preserving Data Stitching Engine

Implementation Steps

Step 1: Inventory Data Sources

• Catalog every evidence repository (cloud storage, on‑prem DBs, SaaS APIs).
• Assign a source policy ID that encodes regulatory constraints (e.g., EU‑only, US‑only).

Step 2: Deploy Zero‑Knowledge Connectors

• Use Procurize’s Connector SDK to build adapters that encrypt payloads with the instance public key.
• Register the connector endpoints in the Connector Registry.

Step 3: Define the Compliance Evidence Model (CEM)

CEM:
  id: string
  source_id: string
  type: enum[log, policy, report, config]
  timestamp: datetime
  encrypted_blob: bytes
  metadata:
    jurisdiction: string
    sensitivity: enum[low, medium, high]

All incoming evidence conforms to this schema before entering the computation engine.

Step 4: Configure SMPC Workers

• Spin up a Kubernetes‑based SMPC cluster (e.g., using MP‑SPDZ).
• Distribute the private key shares across workers; no single node can decrypt alone.

Step 5: Build RAG Prompts

• Create prompt templates that reference provenance fields:

Using evidence ID "{{evidence.id}}" from source "{{evidence.source_id}}", summarize compliance with {{question.title}}. Include hash "{{evidence.encrypted_hash}}" for verification.

Step 6: Integrate with Procurize UI

• Add a “Stitch Evidence” button to each questionnaire item.
• When triggered, the UI calls the Stitching API, which orchestrates the steps described above.

Step 7: Test End‑to‑End Auditable Flow

• Run a penetration test to verify that raw data never appears in logs.
• Generate a verification report that auditors can validate against the original source hashes.

Best Practices

1. Least‑Privilege Access – Grant connectors only read‑only, time‑bounded tokens.
2. Key Rotation – Rotate public/private key pairs every 90 days; re‑encrypt existing evidence lazily.
3. Metadata‑First Design – Capture jurisdiction and sensitivity before any computation.
4. Audit Logging – Log every API call with hashed identifiers; store logs in an immutable ledger (e.g., blockchain).
5. Continuous Monitoring – Use a Compliance Radar (another Procurize AI module) to detect new regulatory changes that affect source policies.

Future Outlook

The convergence of generative AI, privacy‑preserving computation, and knowledge graphs heralds a new era where security questionnaires are answered before they are even asked. Anticipated advancements include:

• Predictive Question Generation – AI models that forecast upcoming questionnaire items based on regulatory trend analysis, prompting pre‑emptive evidence stitching.
• Federated Knowledge Graphs – Cross‑company, privacy‑preserving graphs that allow organizations to share anonymized compliance patterns without exposing raw data.
• Zero‑Touch Evidence Generation – LLMs that, using encrypted embeddings, can synthesize required evidence (e.g., policy statements) directly from encrypted source content.

By investing in a PPDSE today, organizations position themselves to harness these innovations without re‑architecting their compliance stack.

Conclusion

Security questionnaires will remain a pivotal friction point in the SaaS sales and audit pipeline. A Privacy Preserving Data Stitching Engine transforms fragmented evidence into a unified, auditable, and AI‑ready asset—delivering speed, accuracy, and regulatory confidence simultaneously. Leveraging Procurize’s modular AI platform, organizations can deploy this engine with minimal disruption, empowering security teams to focus on strategic risk mitigation rather than repetitive data collection.

“Automate the mundane, protect the sensitive, and let AI do the storytelling.” – Procurize Engineering Lead

See Also

• NIST SP 800‑53 Rev. 5 – Security and Privacy Controls
• Google Cloud – Privacy‑Preserving Machine Learning