Real Time Data Lineage Dashboard for AI Generated Security Questionnaire Evidence

Introduction

Security questionnaires have become a critical choke point in B2B SaaS sales, due diligence, and regulatory audits. Companies are increasingly turning to generative AI to draft answers, extract supporting evidence, and keep policies in sync with evolving standards. While AI dramatically shortens response times, it also introduces an opacity problem: Who created each evidence snippet? From which policy, document, or system does it stem?

A data lineage dashboard solves this problem by visualizing the complete provenance chain of every AI‑generated evidence artifact in real time. It gives compliance officers a single pane of glass where they can trace an answer back to its original clause, see the transformation steps, and verify that no policy drift has occurred.

In this article we will:

• Explain why data lineage is a compliance necessity.
• Describe the architecture that powers a real‑time lineage dashboard.
• Show how a knowledge graph, event streaming, and mermaid visualizations work together.
• Offer a step‑by‑step implementation guide.
• Highlight best practices and future directions.

Why Data Lineage Matters for AI Generated Answers

By exposing the full transformation pipeline – from raw policy documents through pre‑processing, vector embedding, retrieval‑augmented generation (RAG), and final answer synthesis – teams gain confidence that AI is amplifying governance, not bypassing it.

Architecture Overview

The system is built around four core layers:

1. Ingestion Layer – Watches policy repositories (Git, S3, Confluence) and emits change events to a Kafka‑like bus.
2. Processing Layer – Runs document parsers, extracts clauses, creates embeddings, and updates the Evidence Knowledge Graph (EKG).
3. RAG Layer – When a questionnaire request arrives, the Retrieval‑Augmented Generation engine fetches relevant graph nodes, assembles a prompt, and produces an answer plus a list of evidence IDs.
4. Visualization Layer – Consumes the RAG output stream, builds a real‑time lineage graph, and renders it in a web UI using Mermaid.

graph TD
    A["Policy Repository"] -->|Change Event| B["Ingestion Service"]
    B -->|Parsed Clause| C["Evidence KG"]
    D["Questionnaire Request"] -->|Prompt| E["RAG Engine"]
    E -->|Answer + Evidence IDs| F["Lineage Service"]
    F -->|Mermaid JSON| G["Dashboard UI"]
    C -->|Provides Context| E

Key Components

Real‑Time Ingestion Pipeline

1. Watch Repositories – A lightweight file‑system watcher (or Git webhook) detects pushes.
2. Extract Metadata – File type, version hash, author, and timestamp are recorded.
3. Parse Clauses – Regular expressions and NLP models identify clause numbers and titles.
4. Create Graph Nodes – For each clause, a Clause node is created with properties id, title, sourceDocId, version.
5. Publish Event – clause.created events are emitted to the streaming bus.

  flowchart LR
    subgraph Watcher
        A[File Change] --> B[Metadata Extract]
    end
    B --> C[Clause Parser]
    C --> D[Neo4j Create Node]
    D --> E[Kafka clause.created]

Knowledge Graph Integration

The Evidence KG stores three primary node types:

• Document – Raw policy file, versioned.
• Clause – Individual compliance requirement.
• Evidence – Extracted proof items (e.g., logs, screenshots, certificates).

Relationships:

• Document HAS_CLAUSE Clause
• Clause GENERATES Evidence
• Evidence USED_BY Answer

When RAG produces an answer, it attaches the IDs of all Evidence nodes that contributed. This creates a deterministic path that can be visualized instantly.

Mermaid Lineage Diagram

Below is a sample lineage diagram for a fictitious answer to the SOC 2 question “How do you encrypt data at rest?”.

  graph LR
    A["Answer: Data is encrypted using AES‑256 GCM"] --> B["Evidence: Encryption Policy (SOC2‑CC5.2)"]
    B --> C["Clause: Encryption at Rest"]
    C --> D["Document: SecurityPolicy_v3.pdf"]
    B --> E["Evidence: KMS Key Rotation Log"]
    E --> F["Document: KMS_Audit_2025-12.json"]
    A --> G["Evidence: Cloud Provider Encryption Settings"]
    G --> H["Document: CloudConfig_2026-01.yaml"]

The dashboard renders this diagram dynamically, allowing users to click on any node to view the underlying document, version, and raw data.

Benefits for Compliance Teams

• Instant Auditable Trail – Export the entire lineage as a JSON‑LD file for regulator consumption.
• Impact Analysis – When a policy changes, the system can recompute all downstream answers and highlight affected questionnaire items.
• Reduced Manual Work – No longer need to manually copy‑paste clause references; the graph does it automatically.
• Risk Transparency – Visualizing data flow helps security engineers spot weak links (e.g., missing logs).

Implementation Steps

1. Set Up Ingestion

   • Deploy a Git webhook or CloudWatch event rule.
   • Install the policy‑parser microservice (Docker image procurize/policy‑parser:latest).

2. Provision Neo4j

   • Use Neo4j Aura or a self‑hosted cluster.
   • Create constraints on Clause.id and Document.id.

3. Configure Streaming Bus

   • Deploy Apache Kafka or Redpanda.
   • Define topics: policy.updated, clause.created, rag.response.

4. Deploy RAG Service

   • Choose an LLM provider (OpenAI, Anthropic).
   • Implement a Retrieval API that queries Neo4j via Cypher.

5. Build Lineage Service

   • Subscribe to rag.response.
   • For each evidence ID, query Neo4j for the full path.
   • Generate Mermaid JSON and publish to lineage.render.

6. Develop Dashboard UI

   • Use React, react-mermaid2, and a lightweight auth layer (OAuth2).
   • Add filters: date range, document source, risk level.

7. Testing & Validation

   • Create unit tests for each microservice.
   • Run end‑to‑end simulations with synthetic questionnaire data.

8. Rollout

   • Start with a pilot team (e.g., SOC 2 compliance).
   • Gather feedback, iterate on UI/UX, and expand to ISO 27001, GDPR modules.

Best Practices

Future Directions

1. Federated Knowledge Graphs – Combine multiple tenant graphs while preserving data isolation using Zero‑Knowledge Proofs.
2. Explainable AI Overlays – Attach confidence scores and LLM reasoning traces to each edge.
3. Proactive Policy Suggestion – When drift is detected, the system can suggest clause updates based on industry benchmarks.
4. Voice‑First Interaction – Integrate with a voice assistant that reads lineage steps aloud for accessibility.

Conclusion

A real‑time data lineage dashboard transforms AI‑generated security questionnaire evidence from a black box into a transparent, auditable, and actionable asset. By marrying event‑driven ingestion, a semantic knowledge graph, and dynamic Mermaid visualizations, compliance teams gain the visibility they need to trust AI, pass audits, and accelerate deal velocity. Implementing the steps outlined above positions any SaaS organization at the forefront of responsible AI‑driven compliance.