
# Contextual Evidence Recommendation Engine for Automated Security Questionnaires

**TL;DR** – A Context‑Aware Evidence Recommendation Engine (CERE) fuses large language models (LLMs) with a continuously refreshed knowledge graph to present auditors and security teams with the exact piece of evidence they need—right when they need it. The result is a 60‑80 % reduction in manual search time, higher answer accuracy, and a compliance workflow that scales with the velocity of modern SaaS development.

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## 1. Why a Recommendation Engine Is the Missing Link

Security questionnaires, [SOC 2](https://secureframe.com/hub/soc-2/what-is-soc-2) readiness checks, [ISO 27001](https://www.iso.org/standard/27001) audits, and vendor risk assessments all share a common pain point: **the hunt for the right evidence**. Teams typically maintain a sprawling repository of policies, audit reports, configuration snapshots, and third‑party attestations. When a questionnaire arrives, a compliance analyst must:

1. **Parse the question** (often in natural language, sometimes with industry‑specific jargon).  
2. **Identify the control domain** (e.g., “Access Management”, “Data Retention”).  
3. **Search the repository** for documents that satisfy the control.  
4. **Copy‑paste or re‑write** the response, adding contextual notes.

Even with sophisticated search tools, the manual loop can consume several hours per questionnaire, especially when evidence is scattered across multiple cloud accounts, ticketing systems, and legacy file shares. The error‑prone nature of this process fuels compliance fatigue and can lead to missed deadlines or inaccurate answers—both costly for a fast‑growing SaaS business.

Enter **CERE**: an engine that automatically surfaces the most relevant evidence item(s) *as soon as the question is entered*, driven by a blend of semantic understanding (LLMs) and relational reasoning (knowledge graph traversal).

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## 2. Core Architectural Pillars

CERE is built on three tightly coupled layers:

| Layer | Responsibility | Key Technologies |
|-------|----------------|------------------|
| **Semantic Intent Layer** | Transforms the raw questionnaire text into a structured intent (control family, risk tier, required artifact type). | Prompt‑engineered LLM (e.g., Claude‑3, GPT‑4o) + Retrieval‑Augmented Generation (RAG) |
| **Dynamic Knowledge Graph (DKG)** | Stores entities (documents, controls, assets) and their relationships, continuously refreshed from source systems. | Neo4j/JanusGraph, GraphQL API, Change‑Data‑Capture (CDC) pipelines |
| **Recommendation Engine** | Executes intent‑driven graph queries, ranks candidate evidence, and returns a concise, confidence‑scored recommendation. | Graph Neural Network (GNN) for relevance scoring, reinforcement‑learning loop for feedback incorporation |

Below is a Mermaid diagram that visualizes the data flow.

```mermaid
flowchart LR
    A["User submits questionnaire question"]
    B["LLM parses intent\n(Control, Risk, ArtifactType)"]
    C["DKG lookup based on intent"]
    D["GNN relevance scoring"]
    E["Top‑K evidence items"]
    F["UI presents recommendation\nwith confidence"]
    G["User feedback (accept/reject)"]
    H["RL loop updates GNN weights"]
    A --> B --> C --> D --> E --> F
    F --> G --> H --> D
```

*All node labels are wrapped in double quotes as required.*

---  

## 3. From Text to Intent: Prompt‑Engineered LLM

The first step is to *understand* the question. A carefully crafted prompt extracts three signals:

1. **Control Identifier** – e.g., “ISO 27001 A.9.2.3 – Password Management”.  
2. **Evidence Category** – e.g., “Policy Document”, “Configuration Export”, “Audit Log”.  
3. **Risk Context** – “High‑Risk, External Access”.

A sample prompt (kept terse for security) looks like:

```
You are a compliance analyst. Return a JSON object with the fields:
{
  "control": "<standard ID and title>",
  "evidence_type": "<policy|config|log|report>",
  "risk_tier": "<low|medium|high>"
}
Question: {question}
```

The LLM’s output is validated against a schema, then fed into the DKG query builder.

---  

## 4. The Dynamic Knowledge Graph (DKG)

### 4.1 Entity Model

| Entity | Attributes | Relationships |
|--------|------------|---------------|
| **Document** | `doc_id`, `title`, `type`, `source_system`, `last_modified` | `PROVIDES` → `Control` |
| **Control** | `standard_id`, `title`, `domain` | `REQUIRES` → `Evidence_Type` |
| **Asset** | `asset_id`, `cloud_provider`, `environment` | `HOSTS` → `Document` |
| **User** | `user_id`, `role` | `INTERACTS_WITH` → `Document` |

### 4.2 Real‑Time Sync

Procurize already integrates with SaaS tools such as GitHub, Confluence, ServiceNow, and cloud provider APIs. A CDC‑based micro‑service watches for CRUD events and updates the graph in sub‑second latency, preserving **auditability** (each edge carries a `source_event_id`).

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## 5. Graph‑Driven Recommendation Path

1. **Anchor Node Selection** – The intent’s `control` becomes the starting node.  
2. **Path Expansion** – A breadth‑first search (BFS) explores `PROVIDES` edges limited to the `evidence_type` returned by the LLM.  
3. **Feature Extraction** – For each candidate document, a vector is built from:
   - Textual similarity (embedding from the same LLM).
   - Temporal freshness (`last_modified` age).
   - Usage frequency (how often the doc was referenced in past questionnaires).  
4. **Relevance Scoring** – A GNN aggregates node and edge features, producing a score `s ∈ [0,1]`.  
5. **Ranking & Confidence** – The top‑K documents are ordered by `s`; the engine also outputs the confidence percentile (e.g., “85 % confident this policy satisfies the request”).

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## 6. Human‑in‑the‑Loop Feedback Loop

No recommendation is perfect out of the gate. CERE captures the *accept/reject* decision and any free‑text feedback. This data fuels a reinforcement‑learning (RL) loop that periodically fine‑tunes the GNN’s policy network, aligning the model with the organization’s subjective relevance preferences.

The RL pipeline runs nightly:

```mermaid
stateDiagram-v2
    [*] --> CollectFeedback
    CollectFeedback --> UpdateRewards
    UpdateRewards --> TrainGNN
    TrainGNN --> DeployModel
    DeployModel --> [*]
```

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## 7. Integration With Procurize

Procurize already offers a **Unified Questionnaire Hub** where users can assign tasks, comment, and attach evidence. CERE plugs in as a **smart field widget**:

- When the analyst clicks *“Add Evidence”*, the widget triggers the LLM‑DKG pipeline.
- Recommended documents appear as clickable cards, each with a **“Insert citation”** button that auto‑generates the markdown reference formatted for the questionnaire.
- For multi‑tenant environments, the engine respects **tenant‑level data partitions**—each customer’s graph is isolated, guaranteeing confidentiality while still enabling cross‑tenant learning in a privacy‑preserving manner (via federated averaging of GNN weights).

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## 8. Tangible Benefits

| Metric | Baseline (Manual) | With CERE |
|--------|-------------------|-----------|
| **Average evidence search time** | 15 min per question | 2‑3 min |
| **Answer accuracy (audit pass rate)** | 87 % | 95 % |
| **Team satisfaction (NPS)** | 32 | 68 |
| **Compliance backlog reduction** | 4 weeks | 1 week |

A pilot with a mid‑size fintech (≈200 employees) reported a **72 % cut** in questionnaire turnaround time and a **30 % drop** in revision cycles after the first month.

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## 9. Challenges & Mitigations

| Challenge | Mitigation |
|-----------|------------|
| **Cold‑start for new controls** – No historical evidence references. | Seed the graph with standard policy templates, then use transfer learning from similar controls. |
| **Data privacy across tenants** – Risk of leakage when sharing model updates. | Adopt **Federated Learning**: each tenant trains locally, only model weight deltas are aggregated. |
| **LLM hallucinations** – Mis‑identified control IDs. | Validate LLM output against a **canonical control registry** (ISO, SOC, NIST) before graph query. |
| **Graph drift** – Stale relationships after cloud migrations. | CDC pipelines with **eventual consistency** guarantees and periodic graph health checks. |

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## 10. Future Roadmap

1. **Multimodal Evidence Retrieval** – Incorporate screenshots, configuration diagrams, and video walkthroughs using vision‑enabled LLMs.  
2. **Predictive Regulation Radar** – Fuse real‑time regulatory feeds (e.g., GDPR amendments) to proactively enrich the DKG with upcoming control changes.  
3. **Explainable AI Dashboard** – Visualize why a document received its confidence score (path trace, feature contribution).  
4. **Self‑Healing Graph** – Auto‑detect orphaned nodes and reconcile them via AI‑driven entity resolution.

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## 11. Conclusion

The **Contextual Evidence Recommendation Engine** transforms the labor‑intensive art of security questionnaire answering into a data‑driven, near‑instant experience. By marrying LLM semantic parsing with a living knowledge graph and a GNN‑powered ranking layer, CERE delivers the right evidence, at the right time, with measurable gains in speed, accuracy, and compliance confidence. As SaaS organizations continue to scale, such intelligent assistance will no longer be a nice‑to‑have—it will be the cornerstone of a resilient, audit‑ready operation.

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