Interactive Mermaid‑Based Evidence Provenance Dashboard for Real‑Time Questionnaire Audits

Introduction

Security questionnaires, compliance audits, and vendor risk assessments have traditionally been bottlenecks for fast‑moving SaaS companies. While AI can draft answers in seconds, auditors and internal reviewers still ask, “Where did that answer come from? Has it changed since the last audit?” The answer lies in evidence provenance—the ability to trace every response back to its source, version, and approval trail.

Procurize’s next‑generation feature stack introduces an interactive Mermaid dashboard that visualises evidence provenance in real time. The dashboard is powered by a Dynamic Compliance Knowledge Graph (DCKG), continuously synchronised with policy stores, document repositories, and external compliance feeds. By rendering the graph as an intuitive Mermaid diagram, security teams can:

• Navigate the lineage of each answer with a click.
• Validate evidence freshness via automated policy‑drift alerts.
• Export audit‑ready snapshots that embed the visual provenance into compliance reports.

The following sections unpack the architecture, the Mermaid model, integration patterns, and best‑practice roll‑out steps.

1. Why Provenance Matters in Automated Questionnaires

Provenance eliminates these gaps by binding each AI‑generated answer to a unique evidence node in a graph that records:

• Source Document (policy file, third‑party attestation, control evidence)
• Version Hash (cryptographic fingerprint ensuring immutability)
• Owner / Approver (human or bot identity)
• Timestamp (automatic UTC time)
• Policy Drift Flag (auto‑generated by the Real‑Time Drift Engine)

When an auditor clicks on an answer in the dashboard, the system instantly expands the node, revealing all of the above metadata.

2. Core Architecture

Below is a high‑level Mermaid diagram of the provenance pipeline. The diagram uses double‑quoted node labels as required by the specification.

  graph TD
    subgraph AI Engine
        A["LLM Answer Generator"]
        B["Prompt Manager"]
    end
    subgraph Knowledge Graph
        KG["Dynamic Compliance KG"]
        V["Evidence Version Store"]
        D["Drift Detection Service"]
    end
    subgraph UI Layer
        UI["Interactive Mermaid Dashboard"]
        C["Audit Export Service"]
    end
    subgraph Integrations
        R["Policy Repo (Git)"]
        S["Document Store (S3)"]
        M["External Compliance Feed"]
    end

    B --> A
    A --> KG
    KG --> V
    V --> D
    D --> KG
    KG --> UI
    UI --> C
    R --> V
    S --> V
    M --> KG

Key flows

1. Prompt Manager selects a context‑aware prompt that references relevant KG nodes.
2. LLM Answer Generator produces a draft answer.
3. The answer is registered in the KG as a new Answer Node with edges to underlying Evidence Nodes.
4. Evidence Version Store writes a cryptographic hash of each source document.
5. Drift Detection Service continuously compares stored hashes against live policy snapshots; any mismatch automatically flags the answer for review.
6. Interactive Dashboard reads the KG via a GraphQL endpoint, rendering Mermaid code on‑the‑fly.
7. Audit Export Service bundles the current Mermaid SVG, provenance JSON, and answer text into a single PDF package.

3. Building the Mermaid Dashboard

3.1 Data‑to‑Diagram Transformation

The UI layer queries the KG for a specific questionnaire ID. The response includes a nested structure:

{
  "questionId": "Q-101",
  "answer": "We encrypt data at rest using AES‑256.",
  "evidence": [
    {
      "docId": "policy-iso27001",
      "versionHash": "0x9f2c...",
      "approvedBy": "alice@example.com",
      "timestamp": "2025-11-20T14:32:00Z",
      "drift": false
    },
    {
      "docId": "cloud‑kbs‑report",
      "versionHash": "0x4c1a...",
      "approvedBy": "bob@example.com",
      "timestamp": "2025-09-05T09:10:00Z",
      "drift": true
    }
  ]
}

A client‑side renderer converts each evidence entry into a Mermaid sub‑graph:

  graph LR
    A["Answer Q‑101"] --> E1["policy‑iso27001"]
    A --> E2["cloud‑kbs‑report"]
    E1 -->|hash: 0x9f2c| H1["Hash"]
    E2 -->|hash: 0x4c1a| H2["Hash"]
    E2 -->|drift| D["⚠️ Drift Detected"]

The UI overlays visual cues:

• Green node – evidence up‑to‑date.
• Red node – drift flagged.
• Lock icon – cryptographic hash verified.

Note: The reference to policy‑iso27001 aligns with the ISO 27001 standard — see the official specification for details: ISO 27001.

3.2 Interactive Features

All interactions are client‑side only, avoiding extra round‑trips. The underlying Mermaid code is stored in a hidden <textarea> for easy copy‑paste.

4. Integrating Provenance Into Existing Workflows

4.1 CI/CD Compliance Gate

Add a step in your pipeline that fails the build if any answer in the upcoming release has an unresolved drift flag. Example GitHub Action:

name: Evidence Provenance Gate
on: [pull_request]
jobs:
  provenance-check:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v3
      - name: Run Drift Scanner
        run: |
          curl -s https://api.procurize.io/drift?pr=${{ github.event.pull_request.number }} \
          | jq '.drifted | length > 0' && exit 1 || exit 0          

4.2 Slack / Teams Alerting

Configure the Drift Detection Service to push a concise Mermaid snippet into a channel whenever drift occurs. The snippet is automatically rendered by supported bots, giving security leads instant visibility.

4.3 Legal Review Automation

Legal teams can add a “Legal Sign‑Off” edge to evidence nodes. The dashboard then displays a lock icon by the node, signalling that the evidence has passed a legal checklist.

5. Security & Privacy Considerations

6. Real‑World Impact: A Case Study

Company: SecureFinTech Ltd.
Scenario: Quarterly SOC 2 audit required evidence for 182 encryption controls.
Before Dashboard: Manual collation took 12 days; auditors questioned evidence freshness.
After Dashboard:

The provenance visualization cut the audit preparation timeline by 70 %, and the automated drift alerts saved an estimated 160 person‑hours annually.

7. Step‑by‑Step Implementation Guide

1. Enable Knowledge Graph Sync – Connect your policy Git repo, document store, and external compliance feeds in Procurize settings.
2. Activate Provenance Service – Turn on “Evidence Versioning & Drift Detection” in the platform admin console.
3. Configure Mermaid Dashboard – Add dashboard.provenance.enabled = true to the procurize.yaml configuration file.
4. Define Approval Workflows – Use the “Workflow Builder” to attach “Legal Sign‑Off” and “Security Owner” steps to each evidence node.
5. Train Teams – Run a 30‑minute live demo covering node interaction, drift handling, and export procedures.
6. Embed in Auditor Portals – Use the provided IFrame snippet to host the dashboard inside your external audit portal.

<iframe src="https://dashboard.procurize.io/q/2025-Q101"
        width="100%" height="800"
        style="border:none;"></iframe>

7. Monitor Metrics – Track “Drift Events”, “Export Count”, and “Avg. Answer Time” on the Procurize analytics dashboard to quantify ROI.

8. Future Enhancements

9. Conclusion

Procurize’s interactive Mermaid‑based evidence provenance dashboard transforms the opaque world of security questionnaire automation into a transparent, auditable, and collaborative experience. By coupling AI‑generated answers with a live compliance knowledge graph, organizations gain instant lineage visibility, automated drift mitigation, and audit‑ready artifacts—all without sacrificing speed.

Adopting this visual provenance layer not only shortens audit cycles but also builds confidence among regulators, partners, and customers that your security claims are backed by immutable, real‑time evidence.