Self‑Supervised Knowledge Graph Evolution for Automated Security Questionnaires

Introduction

Security questionnaires, compliance audits, and vendor risk assessments are essential components of B2B SaaS transactions. Yet their manual handling consumes 30‑70 % of a security team’s time, introduces human error, and slows deal velocity.

Procurize’s AI platform already centralizes questionnaires, assigns tasks, and uses large language models (LLMs) to draft answers. The next frontier—self‑supervised knowledge graph (KG) evolution—pushes automation a step further. Instead of a static KG that must be manually curated, the graph learns, adapts, and expands whenever a new questionnaire response is submitted, all without explicit human labeling.

This article walks through:

1. The problem space of static compliance KGs.
2. Core concepts of self‑supervised KG evolution.
3. Architecture blocks and data flows in Procurize.
4. How dynamic risk heatmaps visualize real‑time confidence.
5. Implementation tips, best practices, and future directions.

By the end, you’ll understand how a self‑evolving KG can turn every questionnaire interaction into a learning event, delivering faster, more accurate, and auditable responses.

1. Why Static Knowledge Graphs Fall Short

Traditional compliance KGs are built in a once‑and‑done fashion:

• Manual ingestion of policies, standards (SOC 2, ISO 27001).
• Hard‑coded relations linking controls to evidence types.
• Periodic updates driven by compliance teams (often quarterly).

Consequences:

In a dynamic threat landscape, static KGs cannot keep pace. They need a mechanism that absorbs new data and re‑evaluates relationships continuously.

2. Core Concepts of Self‑Supervised KG Evolution

Self‑supervised learning (SSL) trains models using intrinsic signals from the data itself, eliminating the need for hand‑labeled examples. When applied to a compliance KG, SSL enables three essential capabilities:

2.1 Contrastive Edge Mining

• Each new questionnaire answer is split into statement and evidence pairs.
• The system generates positive pairs (statement ↔ correct evidence) and negative pairs (statement ↔ unrelated evidence).
• A contrastive loss pushes the embedding of positive pairs closer while pulling negatives apart, refining edge weights automatically.

2.2 Pattern‑Based Node Augmentation

• Regex and semantic pattern detectors identify recurring phrasing (“We encrypt at rest”) across answers.
• New nodes (e.g., “Encryption at Rest”) are auto‑created and linked to existing control nodes via semantic similarity scores.

2.3 Confidence‑Weighted Propagation

• Each edge acquires a confidence score derived from the SSL loss magnitude and the underlying LLM’s token‑level probability.
• Propagation algorithms (e.g., personalized PageRank) spread confidence through the graph, enabling real‑time risk heatmaps (see Section 4).

Collectively, these mechanisms let the KG grow organically as the organization answers more questionnaires.

3. Architecture Overview

Below is a Mermaid diagram that visualizes the end‑to‑end data flow within Procurize’s self‑supervised KG engine.

  graph LR
    A["Questionnaire Submission"] --> B["Answer Drafting (LLM)"]
    B --> C["Evidence Retrieval Service"]
    C --> D["Contrastive Edge Miner"]
    D --> E["Pattern Node Generator"]
    E --> F["KG Store (Neo4j)"]
    F --> G["Confidence Propagation Engine"]
    G --> H["Real‑Time Risk Heatmap"]
    H --> I["Answer Validation UI"]
    I --> J["Auditable Export (PDF/JSON)"]
    style A fill:#f9f,stroke:#333,stroke-width:2px
    style J fill:#bbf,stroke:#333,stroke-width:2px

3.1 Component Details

4. Real‑Time Risk Heatmap: From Scores to Action

Confidence scores per edge are aggregated into node risk levels. The heatmap uses a gradient from green (low risk) to red (high risk).

  journey
    title Real‑Time Risk Heatmap Journey
    section Graph Ingestion
      Data Arrival: 5: Procurize Platform
      Contrastive Mining: 4: Edge Scoring Engine
    section Propagation
      Confidence Spread: 3: GraphSAGE
      Normalization: 2: Score Scaling
    section Visualization
      Heatmap Refresh: 5: UI Layer

4.1 Interpreting the Heatmap

Security managers can filter the heatmap by regulatory framework, vendor, or business unit, instantly spotting where compliance gaps are emerging.

5. Implementation Blueprint

5.1 Data Preparation

1. Normalize all incoming documents (PDF → text, CSV → table).
2. Apply entity extraction for controls, assets, and processes.
3. Store raw artifacts in a version‑controlled blob store (e.g., MinIO) with immutable identifiers.

5.2 Training the Contrastive Miner

import torch
from torch.nn import functional as F

def contrastive_loss(pos, neg, temperature=0.07):
    # pos, neg are L2‑normalized embeddings
    logits = torch.cat([pos @ pos.t(), pos @ neg.t()], dim=1) / temperature
    labels = torch.arange(pos.size(0)).to(logits.device)
    return F.cross_entropy(logits, labels)

• Batch size: 256 pairs.
• Optimizer: AdamW, learning rate 3e‑4.
• Scheduler: Cosine annealing with warm‑up (5 %).

Run continuous training every time a batch of new questionnaire answers is persisted.

5.3 Node Augmentation Pipeline

1. Run TF‑IDF on answer texts to surface high‑value n‑grams.
2. Feed n‑grams into a semantic similarity service (Sentence‑BERT).
3. If similarity > 0.85 to an existing node, merge; otherwise create a new node with a temporary confidence of 0.5.

5.4 Confidence Propagation

Implement personalized PageRank with edge confidence as transition probability:

CALL algo.pageRank.stream(
   'MATCH (n) RETURN id(n) AS id',
   'MATCH (a)-[r]->(b) RETURN id(a) AS source, id(b) AS target, r.confidence AS weight',
   {iterations:20, dampingFactor:0.85}
) YIELD nodeId, score
RETURN nodeId, score ORDER BY score DESC LIMIT 10;

The top‑scoring nodes feed directly into the heatmap UI.

5.5 Auditable Export

• Serialize the sub‑graph used for an answer.
• Compute a SHA‑256 hash of the serialized JSON‑LD.
• Attach the hash to the PDF export and store in an append‑only ledger (e.g., Amazon QLDB).

This provides tamper‑evident proof for auditors.

6. Benefits and ROI

Financially, a midsize SaaS firm (≈ 200 questionnaires/year) can save over $250k in labor costs and close deals up to 4 weeks sooner, directly impacting ARR.

7. Best Practices & Pitfalls

Common Pitfalls:

• Over‑fitting to a single vendor’s language – mitigate by mixing data across vendors.
• Neglecting privacy – ensure sensitive artifacts are encrypted at rest and masked in embeddings.
• Ignoring explainability – surface edge confidence and source evidence in the UI for transparency.

8. Future Directions

1. Federated Self‑Supervision – multiple organizations contribute anonymized KG updates without sharing raw evidence.
2. Zero‑Knowledge Proof Integration – auditors can verify answer integrity without seeing underlying documents.
3. Multimodal Evidence – incorporate screenshots, architecture diagrams, and config files using vision‑LLMs.
4. Predictive Regulation Radar – feed the KG into a forecasting model that alerts teams of upcoming regulatory changes before they are published.

These extensions will push the compliance KG from reactive to proactive, turning security questionnaires into a source of strategic insight.

Conclusion

Self‑supervised knowledge graph evolution redefines how SaaS companies handle security questionnaires. By turning every answer into a learning event, firms achieve continuous compliance, dramatically reduce manual effort, and provide auditors with immutable, confidence‑weighted evidence.

Implementing the architecture outlined above equips security teams with a living compliance brain—one that adapts, explains, and scales alongside the business.

See Also

• Self‑Supervised Learning for Graphs: A Survey (arXiv)