Interactive AI Compliance Sandbox for Security Questionnaires

TL;DR – A sandbox platform lets organizations generate realistic questionnaire challenges, train AI models on them, and instantly evaluate answer quality, turning the manual pain of security questionnaires into a repeatable, data‑driven process.

Why a Sandbox Is the Missing Link in Questionnaire Automation

Security questionnaires are “the gatekeepers of trust” for SaaS vendors. Yet, most teams still rely on spreadsheets, email threads, and ad‑hoc copy‑and‑paste from policy documents. Even with powerful AI engines, the quality of answers hinges on three hidden factors:

The sandbox captures these gaps by providing a closed‑loop playground where every element – from regulatory change feeds to reviewer comments – is programmable and observable.

Core Architecture of the Sandbox

Below is the high‑level flow. The diagram uses Mermaid syntax, which Hugo will render automatically.

  flowchart LR
    A["Synthetic Vendor Generator"] --> B["Dynamic Questionnaire Engine"]
    B --> C["AI Answer Generator"]
    C --> D["Real‑Time Evaluation Module"]
    D --> E["Explainable Feedback Dashboard"]
    E --> F["Knowledge‑Graph Sync"]
    F --> B
    D --> G["Policy Drift Detector"]
    G --> H["Regulatory Feed Ingestor"]
    H --> B

All node labels are quoted to satisfy Mermaid requirements.

1. Synthetic Vendor Generator

Creates realistic vendor personas (size, industry, data residency, risk appetite). Attributes are randomly drawn from a configurable distribution, ensuring a broad coverage of scenarios.

2. Dynamic Questionnaire Engine

Pulls the latest questionnaire templates (SOC 2, ISO 27001, GDPR, etc.) and injects vendor‑specific variables, producing a unique questionnaire instance each run.

3. AI Answer Generator

Wraps any LLM (OpenAI, Anthropic, or a self‑hosted model) with prompt‑templating that feeds the synthetic vendor context, the questionnaire, and the current policy repository.

4. Real‑Time Evaluation Module

Scores answers on three axes:

• Compliance Accuracy – lexical matching against the policy knowledge‑graph.
• Contextual Relevance – similarity to the vendor’s risk profile.
• Narrative Consistency – coherence across multi‑question answers.

5. Explainable Feedback Dashboard

Shows confidence scores, highlights mismatched evidence, and offers suggested edits. Users can approve, reject, or request a new generation, creating a continuous improvement loop.

6. Knowledge‑Graph Sync

Every approved answer enriches the compliance knowledge‑graph, linking evidence, policy clauses, and vendor attributes.

7. Policy Drift Detector & Regulatory Feed Ingestor

Monitors external feeds (e.g., NIST CSF, ENISA, and DPAs). When a new regulation appears, it triggers a policy version bump, automatically re‑running affected sandbox scenarios.

Building Your First Sandbox Instance

Below is a step‑by‑step cheat sheet. The commands assume a Docker‑based deployment; you can replace them with Kubernetes manifests if you prefer.

# 1. Clone the sandbox repo
git clone https://github.com/procurize/ai-compliance-sandbox.git
cd ai-compliance-sandbox

# 2. Spin up core services (LLM API proxy, Graph DB, Evaluation Engine)
docker compose up -d

# 3. Load baseline policies (SOC2, ISO27001, GDPR)
./scripts/load-policies.sh policies/soc2.yaml policies/iso27001.yaml policies/gdpr.yaml

# 4. Generate a synthetic vendor (Retail SaaS, EU data residency)
curl -X POST http://localhost:8080/api/vendor \
     -H "Content-Type: application/json" \
     -d '{"industry":"Retail SaaS","region":"EU","risk_tier":"Medium"}' \
     -o vendor.json

# 5. Create a questionnaire instance for this vendor
curl -X POST http://localhost:8080/api/questionnaire \
     -H "Content-Type: application/json" \
     -d @vendor.json \
     -o questionnaire.json

# 6. Run the AI Answer Generator
curl -X POST http://localhost:8080/api/generate \
     -H "Content-Type: application/json" \
     -d @questionnaire.json \
     -o answers.json

# 7. Evaluate and receive feedback
curl -X POST http://localhost:8080/api/evaluate \
     -H "Content-Type: application/json" \
     -d @answers.json \
     -o evaluation.json

When you open http://localhost:8080/dashboard, you’ll see a real‑time heatmap of compliance risk, a confidence slider, and an explainability panel that pinpoints the exact policy clause that triggered a low score.

Gamified Coaching: Turning Learning Into Competition

One of the sandbox’s most beloved features is the Coaching Leaderboard. Teams earn points for:

• Speed – answering a full questionnaire within the benchmark time.
• Accuracy – high compliance scores (> 90 %).
• Improvement – reduction of drift over successive runs.

The leaderboard encourages a healthy competition, nudging teams to refine prompts, enrich policy evidence, and adopt best practices. Moreover, the system can surface common failure patterns (e.g., “Missing encryption-at‑rest evidence”) and suggest targeted training modules.

Real‑World Benefits: Numbers From Early Adopters

The sandbox not only slashes time‑to‑response but also builds a living evidence repository that scales with the organization.

Extending the Sandbox: Plug‑In Architecture

The platform is built on a micro‑service “plug‑in” model, making it easy to extend:

Developers can publish their plug‑ins to a Marketplace inside the sandbox, fostering a community of compliance engineers who share reusable components.

Security & Privacy Considerations

Even though the sandbox runs synthetic data, production deployments often involve real policy documents and sometimes confidential evidence. Below are the hardening guidelines:

1. Zero‑Trust Network – All services communicate over mTLS; access is governed by OAuth 2.0 scopes.
2. Data Encryption – At rest storage uses AES‑256; in‑flight data is protected by TLS 1.3.
3. Auditable Logs – Every generation and evaluation event is immutably recorded in a Merkle‑tree ledger, enabling forensic back‑tracking.
4. Privacy‑Preserving Policies – When ingesting real evidence, enable differential privacy on the knowledge‑graph to avoid leaking sensitive fields.

Future Roadmap: From Sandbox to Production‑Ready Autonomous Engine

These enhancements will transform the sandbox from a training ground into an autonomous compliance engine that continuously adapts to the ever‑changing regulatory landscape.

Getting Started Today

1. Visit the open‑source repo – https://github.com/procurize/ai-compliance-sandbox.
2. Deploy a local instance using Docker Compose (see the quick‑start script).
3. Invite your security and product teams to run a “first‑run” challenge.
4. Iterate – refine prompts, enrich evidence, watch the leaderboard climb.

By turning the arduous questionnaire process into an interactive, data‑driven experience, the Interactive AI Compliance Sandbox empowers organizations to respond faster, answer more accurately, and stay ahead of regulatory change.