Dynamic Knowledge Graph Driven Compliance Scenario Simulation

In the fast‑moving world of SaaS, security questionnaires have become a gating factor for every new contract. Teams are perpetually racing against time, scrambling to locate evidence, reconcile conflicting policies, and craft answers that satisfy auditors and customers alike. While platforms like Procurize already automate answer retrieval and task routing, the next evolution is proactive preparation—predicting the exact questions that will appear, the evidence they will require, and the compliance gaps they will expose before a formal request lands.

Enter Dynamic Knowledge Graph Driven Compliance Scenario Simulation (DGSCSS). This paradigm marries three powerful concepts:

1. A live, self‑updating compliance knowledge graph that ingests policies, control mappings, audit findings, and regulatory changes.
2. Generative AI (RAG, LLMs, and prompt engineering) that creates realistic questionnaire instances based on the graph’s context.
3. Scenario simulation engines that run “what‑if” audits, evaluate answer confidence, and surface evidence gaps ahead of time.

The result? A continuously rehearsed compliance posture that turns reactive questionnaire filling into a predict‑and‑prevent workflow.

Why Simulate Compliance Scenarios?

By simulating thousands of plausible questionnaires per month, organizations can:

• Quantify readiness with a confidence score for each control.
• Prioritize remediation on low‑confidence areas.
• Reduce turnaround from weeks to days, giving sales teams a competitive edge.
• Demonstrate continuous compliance to regulators and customers.

Architectural Blueprint

  graph LR
    A["Regulatory Feed Service"] --> B["Dynamic Compliance KG"]
    C["Policy Repository"] --> B
    D["Audit Findings DB"] --> B
    B --> E["AI Prompt Engine"]
    E --> F["Scenario Generator"]
    F --> G["Simulation Scheduler"]
    G --> H["Confidence Scoring Module"]
    H --> I["Procurize Integration Layer"]
    I --> J["Real‑Time Dashboard"]

Figure 1: End‑to‑end flow of the DGSCSS architecture.

Core Components

1. Regulatory Feed Service – Consumes APIs from standards bodies (e.g., NIST CSF, ISO 27001, GDPR) and translates updates into graph triples.
2. Dynamic Compliance Knowledge Graph (KG) – Stores entities such as Controls, Policies, Evidence Artifacts, Audit Findings, and Regulatory Requirements. Relationships encode mappings (e.g., controls‑cover‑requirements).
3. AI Prompt Engine – Uses Retrieval‑Augmented Generation (RAG) to craft prompts that ask the LLM to generate questionnaire items reflective of the current KG state.
4. Scenario Generator – Produces a batch of simulated questionnaires, each tagged with a scenario ID and risk profile.
5. Simulation Scheduler – Orchestrates periodic runs (daily/weekly) and on‑demand simulations triggered by policy changes.
6. Confidence Scoring Module – Evaluates each generated answer against existing evidence using similarity metrics, citation coverage, and historical audit success rates.
7. Procurize Integration Layer – Feeds confidence scores, evidence gaps, and recommended remediation tasks back into the Procurize UI.
8. Real‑Time Dashboard – Visualizes readiness heatmaps, drill‑down evidence matrices, and trend lines for compliance drift.

Building the Dynamic Knowledge Graph

1. Ontology Design

Define a lightweight ontology that captures the compliance domain:

entities:
  - Control
  - Policy
  - Evidence
  - Regulation
  - AuditFinding
relations:
  - Controls.map_to(Requirement)
  - Policy.enforces(Control)
  - Evidence.supports(Control)
  - Regulation.requires(Control)
  - AuditFinding.affects(Control)

2. Ingestion Pipelines

• Policy Puller: Scans source control (Git) for Markdown/YAML policy files, parses headings into Policy nodes.
• Control Mapper: Parses internal control frameworks (e.g., SOC‑2) and creates Control entities.
• Evidence Indexer: Uses Document AI to OCR PDFs, extract metadata, and store pointers to cloud storage.
• Regulation Sync: Periodically calls standards APIs, creating/updating Regulation nodes.

3. Graph Storage

Choose a scalable graph DB (Neo4j, Amazon Neptune, or Dgraph). Ensure ACID compliance for real‑time updates, and enable full‑text search on node attributes for fast retrieval by the AI engine.

AI‑Powered Prompt Engineering

The prompt must be context‑rich yet concise to avoid hallucinations. A typical template:

You are a compliance analyst. Using the following knowledge graph excerpts, generate a realistic security questionnaire for a SaaS provider operating in the {industry} sector. Include 10–15 questions covering data privacy, access control, incident response, and third‑party risk. Cite the relevant control IDs and regulation sections in each answer.

[KG_EXCERPT]

• KG_EXCERPT is a RAG‑retrieved subgraph (e.g., top‑10 related nodes) serialized as human‑readable triples.
• Few‑shot examples can be added to improve style consistency.

The LLM (GPT‑4o or Claude 3.5) returns a structured JSON array, which the Scenario Generator validates against schema constraints.

Confidence Scoring Algorithm

1. Evidence Coverage – Ratio of required evidence items that exist in the KG.
2. Semantic Similarity – Cosine similarity between generated answer embeddings and stored evidence embeddings.
3. Historical Success – Weight derived from past audit outcomes for the same control.
4. Regulatory Criticality – Higher weight for controls mandated by high‑impact regulations (e.g., GDPR Art. 32).

Overall confidence = weighted sum, normalized to 0‑100. Scores below 70 trigger remediation tickets in Procurize.

Integration with Procurize

Implementation steps:

1. Deploy the Integration Layer as a micro‑service exposing REST endpoints /simulations/{id}.
2. Configure Procurize to poll the service every hour for new simulation results.
3. Map Procurize’s internal questionnaire_id to the simulation’s scenario_id for traceability.
4. Enable a UI widget in Procurize that lets users launch an “On‑Demand Scenario” for a selected client.

Benefits Quantified

These numbers stem from a pilot with three mid‑size SaaS firms over six months, demonstrating that proactive simulation can save up to 70% of compliance overhead.

Implementation Checklist

• Define compliance ontology and create initial graph schema.
• Set up ingestion pipelines for policies, controls, evidence, and regulatory feeds.
• Deploy a graph database with high‑availability clustering.
• Integrate a Retrieval‑Augmented Generation pipeline (LLM + vector store).
• Build the Scenario Generator and Confidence Scoring modules.
• Develop the Procurize integration micro‑service.
• Design dashboards (heatmaps, evidence matrices) using Grafana or native Procurize UI.
• Conduct a dry‑run simulation, validate answer quality with SMEs.
• Roll out to production, monitor confidence scores, and iterate prompt templates.

Future Directions

1. Federated Knowledge Graphs – Allow multiple subsidiaries to contribute to a shared graph while preserving data sovereignty.
2. Zero‑Knowledge Proofs – Provide auditors verifiable proof that evidence exists without exposing the raw artifact.
3. Self‑Healing Evidence – Auto‑generate missing evidence using Document AI when gaps are detected.
4. Predictive Regulation Radar – Combine news scraping with LLM inference to forecast upcoming regulatory changes and pre‑emptively adjust the graph.

The convergence of AI, graph technology, and automated workflow platforms like Procurize will soon make “always‑ready compliance” a standard expectation rather than a competitive advantage.